diff --git a/R/connectivity.R b/R/connectivity.R
index 7d45037..befb79c 100644
--- a/R/connectivity.R
+++ b/R/connectivity.R
@@ -942,9 +942,16 @@ make_connectivity_summary_table <- function(
flextable::align(i = 1, align = "left") |>
flextable::merge_at(i = 7, part = "body") |>
flextable::align(i = 7, align = "left") |>
+ # Footnotes
+ flextable::add_footer_lines("SD = Standard Deviation") |>
# Cell alignment
flextable::align(part = "header", i = 1, align = "center") |>
flextable::align(part = "body", j = 3:8, align = "right") |>
+ # Borders
+ flextable::hline_bottom(
+ border = flextable::fp_border_default(width = 2),
+ part = "footer"
+ ) |>
# Font
flextable::font(fontname = "Times New Roman", part = "all") |>
flextable::fontsize(size = 10, part = "all")
diff --git a/R/similarity.R b/R/similarity.R
index 4a058ca..95e3074 100644
--- a/R/similarity.R
+++ b/R/similarity.R
@@ -2077,6 +2077,9 @@ make_contrast_results_table_nhst <- function(emmeans_tidy, contrasts_tidy) {
) |>
ftExtra::colformat_md(part = "header") |>
# Footer
+ flextable::add_footer_lines(
+ "SE = Standard Error, CI = Compatibility Interval"
+ ) |>
flextable::footnote(
i = 1, j = 1,
value = flextable::as_paragraph(
@@ -2111,7 +2114,7 @@ make_contrast_results_table_nhst <- function(emmeans_tidy, contrasts_tidy) {
part = "header"
) |>
flextable::hline_bottom(
- border = flextable::fp_border_default(width = 1.5),
+ border = flextable::fp_border_default(width = 2),
part = "footer"
) |>
flextable::fix_border_issues(part = "footer") |>
@@ -2253,6 +2256,9 @@ make_contrast_results_table_smd <- function(
) |>
#ftExtra::colformat_md(part = "header") |>
# Footer
+ flextable::add_footer_lines(
+ "SE = Standard Error, CI = Compatibility Interval"
+ ) |>
flextable::footnote(
i = 1, j = 1,
value = flextable::as_paragraph(
@@ -2302,7 +2308,7 @@ make_contrast_results_table_smd <- function(
part = "header"
) |>
flextable::hline_bottom(
- border = flextable::fp_border_default(width = 1.5),
+ border = flextable::fp_border_default(width = 2),
part = "footer"
) |>
flextable::fix_border_issues(part = "footer") |>
@@ -2401,7 +2407,7 @@ make_model_fit_table <- function(model, maximal_model = FALSE) {
flextable::align(part = "body", j = 3:6, align = "right") |>
# Borders
flextable::hline_bottom(
- border = flextable::fp_border_default(width = 1.5),
+ border = flextable::fp_border_default(width = 1),
part = "footer"
) |>
flextable::fix_border_issues(part = "footer") |>
diff --git a/_targets/meta/meta b/_targets/meta/meta
index 3ca4e3d..23b23f1 100644
--- a/_targets/meta/meta
+++ b/_targets/meta/meta
@@ -1,5 +1,5 @@
name|type|data|command|depend|seed|path|time|size|bytes|format|repository|iteration|parent|children|seconds|warnings|error
-.Random.seed|object|35736f970b47e4ee|||||||||||||||
+.Random.seed|object|379d756c2cc5fa9d|||||||||||||||
amplitude_connectivity_histogram_plot_alpha|stem|4a2530013833b801|969c6b5f45774909|f2de017293df53ac|1667591071||t19702.0575519512s|3f5f9405051694bd|15502535|rds|local|vector|||0.445||
amplitude_connectivity_histogram_plot_beta|stem|95c12d2547e7bbec|b6d9506945ab0263|8204e3edd2251bba|-322263758||t19702.0571444603s|18dd5fc3f92b7b29|15494750|rds|local|vector|||1.548||
amplitude_connectivity_histogram_plot_delta|stem|5557490d9c958357|98652b80eb4c975c|23a36046fb6a4423|1358304489||t19702.0572564147s|e5a5554eb777d02e|15508128|rds|local|vector|||0.796||
@@ -2607,26 +2607,26 @@ amplitude_similarity_contrasts_table_alpha|stem|69df0808ebd0e86d|7ffa59b900088c9
amplitude_similarity_contrasts_table_beta|stem|0c3d8c6f707d5d11|2d476e189ab780e1|21f22b0d47d6bb81|-267286099||t19710.9120882719s|00981295e49b76c3|6495|rds|local|vector|||0.646||
amplitude_similarity_contrasts_table_delta|stem|517c4b82875abc9b|81b2b80ad6ce2adc|3cc3e8ed532e1f47|-1240791378||t19710.9121095224s|b8a150773b47a503|6473|rds|local|vector|||0.595||
amplitude_similarity_contrasts_table_gamma|stem|02428775f8417ae7|bb3ab0b79495a366|e2a01aa96b0da2ae|706157262||t19710.9120734083s|404b68da84727707|6417|rds|local|vector|||0.606||
-amplitude_similarity_contrasts_table_nhst_alpha|stem|4b9b1fa539ce193d|5baeec30fe3b5ce4|a693eba7a771092b|-1253903688||t19741.0497669365s|ce6ec5790c49b87d|6630|rds|local|vector|||0.63||
-amplitude_similarity_contrasts_table_nhst_beta|stem|f0fb9941da5513ea|302e38141c2a3f2d|c97228b27541827c|-1558730348||t19741.0498721079s|e477860fdab4c396|6541|rds|local|vector|||0.618||
-amplitude_similarity_contrasts_table_nhst_delta|stem|67ae4ebb2ec32689|847b9556b1eab543|4051c4db62ffee3f|-1873826759||t19741.0497293744s|62a1a979d88f7be8|6516|rds|local|vector|||0.616||
-amplitude_similarity_contrasts_table_nhst_gamma|stem|b878b399ed9676f4|47cc0cb5e41f57f0|3beaad9e8c46cc65|-1984658057||t19741.0497822488s|0ae90d09d1ed6e90|6469|rds|local|vector|||0.605||
-amplitude_similarity_contrasts_table_nhst_maximal_alpha|stem|951c2157a1334bd8|0c341eabfc124ec9|5d292504f12965a2|1498722645||t19741.0497748148s|8061ae0839819379|6645|rds|local|vector|||0.643||
-amplitude_similarity_contrasts_table_nhst_maximal_beta|stem|87db18fb05222146|df1875828ebc49e3|79780ac8bb3c354f|1020866939||t19741.0498795509s|1e8df992ce719253|6526|rds|local|vector|||0.615||
-amplitude_similarity_contrasts_table_nhst_maximal_delta|stem|2605bf0783bbdca8|ad3ed1813fd15247|5ed35d38e3649453|2012835782||t19741.0497220139s|c1f93ba5994e0f44|6539|rds|local|vector|||0.622||
-amplitude_similarity_contrasts_table_nhst_maximal_gamma|stem|2a2b8f65522634d7|078392f75de7aa23|2767562c5f657528|1150318598||t19741.0497895515s|50fd540c7b07ffc3|6480|rds|local|vector|||0.611||
-amplitude_similarity_contrasts_table_nhst_maximal_theta|stem|4ea171e799eefe96|210ae62115870f99|f9369405b3e09be6|148060413||t19741.0498495867s|a21aa4b3861bf769|6586|rds|local|vector|||0.639||
-amplitude_similarity_contrasts_table_nhst_theta|stem|e6e3539689de5ec2|7677ae0375cde98c|6cf1ffbe082e0bda|-1238062608||t19741.0498418884s|81e0c59cd556781a|6581|rds|local|vector|||0.613||
-amplitude_similarity_contrasts_table_smd_alpha|stem|2f382ea00b9ec4ee|419b5cc17ed8bdd9|3359ef9cec85c11a|155435930||t19741.8701482501s|27cd18fc0b7b3b91|7080|rds|local|vector|||1.051||
-amplitude_similarity_contrasts_table_smd_beta|stem|986791a992f38606|cb21891f02fadc46|1f0c974af8589e32|970182000||t19741.8703926228s|553b11ae9cccddd9|6952|rds|local|vector|||1.083||
-amplitude_similarity_contrasts_table_smd_delta|stem|fcc41776d7564d58|c3ba37b7823c7b0f|9c5c66593ba74902|717484963||t19741.8700557195s|32ae7e6a3d0724ab|6864|rds|local|vector|||1.676||
-amplitude_similarity_contrasts_table_smd_gamma|stem|52d54f3ed15bb0f6|f05be414904851cd|54bed25f12118940|1285587541||t19741.8701875276s|a9f5f2b63a416c18|6795|rds|local|vector|||1.048||
-amplitude_similarity_contrasts_table_smd_maximal_alpha|stem|5ed26ace4b97004c|98c2f8e536317839|29adc4140fc2bf2b|-1921346423||t19741.8701713951s|7bac41d65f2ba283|7062|rds|local|vector|||1.122||
-amplitude_similarity_contrasts_table_smd_maximal_beta|stem|fa094ed652f86144|62514e662352d0f0|a00a623fc510b76c|-1657371851||t19741.8704081928s|4529ea1493d881c5|6935|rds|local|vector|||1.045||
-amplitude_similarity_contrasts_table_smd_maximal_delta|stem|b78682a4aed78fd5|0e697ae46d7e5492|49008eff2ce81a10|-1069291159||t19741.8700321543s|d7f3a978de586a21|6856|rds|local|vector|||1.136||
-amplitude_similarity_contrasts_table_smd_maximal_gamma|stem|310aacee167eb419|9e59809b0057dc36|33418908173de800|414377806||t19741.870203403s|4a71a532a25a8b1b|6779|rds|local|vector|||1.066||
-amplitude_similarity_contrasts_table_smd_maximal_theta|stem|4b5d7454a697fbfd|6d4abda5a60d3441|675c6e4ebbdc8bc3|-1679763259||t19741.8703440712s|d090a8135e8a4548|6994|rds|local|vector|||1.053||
-amplitude_similarity_contrasts_table_smd_theta|stem|46316df7d2239b2c|17b711eef80d3fc4|355eafeb2f9bb96e|703924027||t19741.8703284228s|8c0ac8b66d271b8b|7015|rds|local|vector|||1.501||
+amplitude_similarity_contrasts_table_nhst_alpha|stem|e5190e2c9a0b6997|5baeec30fe3b5ce4|4c35251e411251dc|-1253903688||t19759.889368856s|81bb0977b2706cf5|6749|rds|local|vector|||0.616||
+amplitude_similarity_contrasts_table_nhst_beta|stem|7560b9059eff5c20|302e38141c2a3f2d|92ed3bc6804b5905|-1558730348||t19759.8893995396s|4431ff1e4924ca08|6659|rds|local|vector|||0.627||
+amplitude_similarity_contrasts_table_nhst_delta|stem|2c22fe9b78589c55|847b9556b1eab543|e71b46f88d38eea3|-1873826759||t19759.8894552364s|2763b7e4367266ed|6634|rds|local|vector|||0.731||
+amplitude_similarity_contrasts_table_nhst_gamma|stem|81d126136d72c8aa|47cc0cb5e41f57f0|126c01c295d803b7|-1984658057||t19759.8893229864s|f67c12681f71b56c|6587|rds|local|vector|||0.603||
+amplitude_similarity_contrasts_table_nhst_maximal_alpha|stem|b444d0dfdfc61266|0c341eabfc124ec9|1210fd14a4483042|1498722645||t19759.8893615673s|acb6701e9b7413ae|6764|rds|local|vector|||0.645||
+amplitude_similarity_contrasts_table_nhst_maximal_beta|stem|44f7b12befbecd63|df1875828ebc49e3|527504c746cfb03a|1020866939||t19759.8893921123s|8061ae0839819379|6645|rds|local|vector|||0.623||
+amplitude_similarity_contrasts_table_nhst_maximal_delta|stem|efb124865629620d|ad3ed1813fd15247|cda9f5c4b363e84e|2012835782||t19759.8894628962s|f92413f8a8895651|6657|rds|local|vector|||0.639||
+amplitude_similarity_contrasts_table_nhst_maximal_gamma|stem|389eddceb3306481|078392f75de7aa23|d8494de6cbece5c7|1150318598||t19759.8893154823s|671c2020228eb6be|6597|rds|local|vector|||0.639||
+amplitude_similarity_contrasts_table_nhst_maximal_theta|stem|ff1d7f12d9115a77|210ae62115870f99|fb216e7eefe9082a|148060413||t19759.8895307709s|dbc4d88e2d5213ff|6704|rds|local|vector|||0.608||
+amplitude_similarity_contrasts_table_nhst_theta|stem|6e8295676cee8574|7677ae0375cde98c|81dcebe371f8c05a|-1238062608||t19759.8895233706s|56105cc067e6edfd|6699|rds|local|vector|||0.616||
+amplitude_similarity_contrasts_table_smd_alpha|stem|c71a8d3946dc1ae0|419b5cc17ed8bdd9|025acf75d675d61a|155435930||t19759.889590296s|a192a14775032a93|7205|rds|local|vector|||0.473||
+amplitude_similarity_contrasts_table_smd_beta|stem|775d7728d4a6282e|cb21891f02fadc46|60969ffebdeb216e|970182000||t19759.8896143508s|f0b25d555af6e031|7077|rds|local|vector|||0.488||
+amplitude_similarity_contrasts_table_smd_delta|stem|b985771a238b46b0|c3ba37b7823c7b0f|a7c4983dbbaddd50|717484963||t19759.8896559491s|45c3434ebe097da7|6990|rds|local|vector|||0.487||
+amplitude_similarity_contrasts_table_smd_gamma|stem|3d2fcd18302f2726|f05be414904851cd|ccf40b220f935bc5|1285587541||t19759.8895549623s|8c5dcf696dbcaf75|6920|rds|local|vector|||0.473||
+amplitude_similarity_contrasts_table_smd_maximal_alpha|stem|d85baa1187d4aa07|98c2f8e536317839|0bfafc5ad4bdd6f3|-1921346423||t19759.889584641s|c973dac64d1062ee|7188|rds|local|vector|||0.484||
+amplitude_similarity_contrasts_table_smd_maximal_beta|stem|c891e696b84516b1|62514e662352d0f0|4ac1ba8869ecaa9a|-1657371851||t19759.889608518s|b5ac0d69426ccad2|7060|rds|local|vector|||0.515||
+amplitude_similarity_contrasts_table_smd_maximal_delta|stem|1f19563053083525|0e697ae46d7e5492|ec4eb1939f32c9f7|-1069291159||t19759.8896615015s|fc62c2b5970b3c9a|6982|rds|local|vector|||0.465||
+amplitude_similarity_contrasts_table_smd_maximal_gamma|stem|db8785f399ebb021|9e59809b0057dc36|ef3dacd46028f25f|414377806||t19759.8895492244s|fdfb962ed2641f33|6904|rds|local|vector|||0.484||
+amplitude_similarity_contrasts_table_smd_maximal_theta|stem|7302516e1ad20098|6d4abda5a60d3441|da060075391e90fa|-1679763259||t19759.8897089851s|4e8bccee4c3037f6|7120|rds|local|vector|||0.487||
+amplitude_similarity_contrasts_table_smd_theta|stem|441c2252d3c53668|17b711eef80d3fc4|5ceca8632329a78a|703924027||t19759.889703175s|984c8f57cd60aa72|7141|rds|local|vector|||0.458||
amplitude_similarity_contrasts_table_theta|stem|37251f29e1d7120e|fa4fb97910efd195|e1244f68fd2a276b|1128608344||t19710.9121023729s|9a8059a23511faf5|6529|rds|local|vector|||0.577||
amplitude_similarity_contrasts_theta|stem|c3bdf539c6c7a992|92109430421fe018|20a23bf9ee9b4381|-103844332||t19201.0137295004s|fa38ddf4e55432b8|3848629|rds|local|vector|||0.01||
amplitude_similarity_contrasts_tidy_alpha|stem|dec2bcc1a7999045|1b297dd46a2fb883|778e3b1c16c30fa1|1904000561||t19613.7771259276s|3e4efbefff0c81f8|1242|rds|local|vector|||0.103||
@@ -2816,16 +2816,16 @@ amplitude_similarity_matrix_figure_beta|stem|a656be8d1dcf1e84|bacf9f9d10c80442|e
amplitude_similarity_matrix_figure_delta|stem|e374eacfee036273|032d832518551940|af166688eaf38267|632118171|figures/delta/amplitude-similarity-matrix.png|t19741.8934396424s|708088eb4e787ef6|254670|file|local|vector|||6.155||
amplitude_similarity_matrix_figure_gamma|stem|ba0ed5c5cec433e4|cb6033e18308e4de|8eec5d26c77f8747|1048131388|figures/gamma/amplitude-similarity-matrix.png|t19741.8934436228s|1633aeef9193e76d|225193|file|local|vector|||5.744||
amplitude_similarity_matrix_figure_theta|stem|ad7b7cd2651dd2af|9d9da32825e56526|a1e8f34399731303|-2097642401|figures/theta/amplitude-similarity-matrix.png|t19741.8934477537s|c19080792a786ac1|263830|file|local|vector|||6.659||
-amplitude_similarity_model_fit_table_alpha|stem|048158b5d06c161f|11ef236a10432044|0eeea930d37e7d16|496118989||t19736.3616285171s|59d81a5b26637ad7|6329|rds|local|vector|||4.796||
-amplitude_similarity_model_fit_table_beta|stem|d1a943584d68207a|f185b5b5b3d8121f|e81c58b93ba622ab|-942982626||t19736.362513874s|e61a516f176f57b2|6326|rds|local|vector|||4.606||
-amplitude_similarity_model_fit_table_delta|stem|8a1733495c750007|1e01c39b48c5aa0d|b1c356a4cd586d57|1859599040||t19736.3613225991s|4966fd50f04d9897|6296|rds|local|vector|||4.835||
-amplitude_similarity_model_fit_table_gamma|stem|ab3d81aea0b27959|9765a9d540ac4f00|97626d7db5d33168|1448091515||t19736.3617513762s|c0879723ad550b6c|6330|rds|local|vector|||4.807||
-amplitude_similarity_model_fit_table_maximal_alpha|stem|b43e7a6fb06cb38e|c2a66b3271c3d446|dcff28572cc3ae59|145977971||t19736.3616920658s|cbd34aee0cc11388|6429|rds|local|vector|||5.174||
-amplitude_similarity_model_fit_table_maximal_beta|stem|f49c579674aadf76|8dde3dc69e9eeb6d|6ed79f4ffc3c6a80|-117172985||t19736.3625756466s|c6348a0a22589105|6433|rds|local|vector|||5.024||
-amplitude_similarity_model_fit_table_maximal_delta|stem|3cc8fdb34adbb20e|8dba8aafda02a922|970905e958f8f965|-1367303925||t19736.3612630903s|ed03e79efadf6631|6411|rds|local|vector|||5.787||
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-amplitude_similarity_model_fit_table_maximal_theta|stem|3bd23117b9a4d72c|b166efe2f7a74585|00d3e0dec5720abc|1968950776||t19736.362327256s|9c8ae88555db2e3c|6421|rds|local|vector|||4.914||
-amplitude_similarity_model_fit_table_theta|stem|1d87d0dc4b45390d|ae059a15f9417c09|972e8771b414b23e|-1539824970||t19736.3622667754s|3b1fcdc053c84d5d|6297|rds|local|vector|||4.506||
+amplitude_similarity_model_fit_table_alpha|stem|27785ad712ab2712|11ef236a10432044|4e6ad3061255bf54|496118989||t19759.906409064s|e61a516f176f57b2|6326|rds|local|vector|||4.373||
+amplitude_similarity_model_fit_table_beta|stem|b50e997936cb6fb2|f185b5b5b3d8121f|c875b78bf6fd818f|-942982626||t19759.906655753s|55cdb377511470dc|6324|rds|local|vector|||4.352||
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+amplitude_similarity_model_fit_table_gamma|stem|225b0f49162876c6|9765a9d540ac4f00|ea0cffd5f5b132db|1448091515||t19759.9060316717s|c68bdf558919c010|6327|rds|local|vector|||4.817||
+amplitude_similarity_model_fit_table_maximal_alpha|stem|e0e0b2ca87f34591|c2a66b3271c3d446|da466345bc07db1f|145977971||t19759.9063550756s|4ccb3234b1a94a5e|6427|rds|local|vector|||5.799||
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+amplitude_similarity_model_fit_table_maximal_gamma|stem|e084fcebe835c356|dd68ac09fc204381|993ed5768331b8f3|1955871128||t19759.9059722574s|13b2582d800c9950|6434|rds|local|vector|||5.723||
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@@ -2935,7 +2935,7 @@ estimate_similarity|function|57d19ad17ee42927|||||||||||||||
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@@ -9683,7 +9683,7 @@ save_subset_similarity_contrast_barplot_figure|function|3f1bdf64d0a590aa||||||||
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@@ -9707,11 +9707,11 @@ supplement_amplitude_coupling_functional_connectomes|stem|0b5a3413368fe077|ba968
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diff --git a/manuscripts/child-documents/discussion.Rmd b/manuscripts/child-documents/discussion.Rmd
index aca65a3..1bb96ea 100644
--- a/manuscripts/child-documents/discussion.Rmd
+++ b/manuscripts/child-documents/discussion.Rmd
@@ -2,13 +2,13 @@
In this study, we explored the feasibility of using EEG to study network variants by examining whether or not EEG phase coupling and amplitude coupling functional connectomes showed similar evidence of stable individual differences across contexts to what has been described in the fMRI literature. To address this question, we used pairwise contrasts to estimate how much and in which ways functional connectome similarity differed within and between individuals across sessions and states, using eyes open and eyes closed resting state EEG data collected across three sessions over the course of approximately three months. Based on the findings of previous fMRI network variant research [e.g., @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019], we hypothesized that functional connectomes would be more similar within than between individuals across all contrasts, on average, with smaller variations in similarity related to session or state (see Figure \@ref(fig:outcome-plot)).
-Overall, our results were inconclusive. In support of our primary scientific hypothesis, the group-level effect sizes most compatible with our data, given the background model, showed that, on average, both phase coupling and amplitude coupling functional connectomes were more similar within than between individuals across contexts and frequency bands (Figures \@ref(fig:phase-similarity-plots-delta)-\@ref(fig:amplitude-similarity-plots-alpha)); that our data was incompatible with a model that assumed zero difference in functional connectome similarity within and between individuals across contexts (Tables \@ref(tab:phase-similarity-table-delta)-\@ref(tab:amplitude-similarity-table-alpha)); and that these differences were attributable to individual-dependent factors with small modulations by session and state (Tables \@ref(tab:phase-similarity-table-delta)-\@ref(tab:amplitude-similarity-table-alpha)). Moreover, the magnitude of these differences varied across contexts in an expected way, with the largest differences in similarity within and between individuals occurring for functional connectomes from the same sessions and states, the smallest differences occurring for functional connectomes from the different sessions and states, and relatively smaller increases and decreases in similarity across other contexts. At this level of description, our results were broadly consistent with the findings of methodologically similar fMRI studies [e.g., @grattonetal_FunctionalBrainNetworks_2018]. These results remained largely consistent for phase coupling functional connectomes when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figures A\@ref(fig:phase-similarity-plots-hilbert-delta)-A\@ref(fig:phase-similarity-plots-hilbert-gamma)); and with few exceptions, these results were consistent between the reduced and maximal models of functional connectome similarity for both both phase coupling and amplitude coupling functional connectomes (Figures A\@ref(fig:phase-similarity-plots-maximal-theta)-A\@ref(fig:amplitude-similarity-plots-maximal-alpha)).
+Overall, our results were inconclusive. In support of our primary scientific hypothesis, the group-level effect sizes most compatible with our data, given the background model, showed that, on average, both phase coupling and amplitude coupling functional connectomes were more similar within than between individuals across contexts and frequency bands (Figures \@ref(fig:phase-similarity-plots-delta)-\@ref(fig:amplitude-similarity-plots-alpha)); that our data were incompatible with a model that assumed zero difference in functional connectome similarity within and between individuals across contexts (Tables \@ref(tab:phase-similarity-table-delta)-\@ref(tab:amplitude-similarity-table-alpha)); and that these differences were attributable to individual-dependent factors with small modulations by session and state (Tables \@ref(tab:phase-similarity-table-delta)-\@ref(tab:amplitude-similarity-table-alpha)). Moreover, the magnitude of these differences varied across contexts in an expected way, with the largest differences in similarity within and between individuals occurring for functional connectomes from the same sessions and states, the smallest differences occurring for functional connectomes from the different sessions and states, and relatively smaller increases and decreases in similarity across other contexts. At this level of description, our results were broadly consistent with the findings of methodologically similar fMRI studies [e.g., @grattonetal_FunctionalBrainNetworks_2018]. These results remained largely consistent for phase coupling functional connectomes when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figures A\@ref(fig:phase-similarity-plots-hilbert-delta)-A\@ref(fig:phase-similarity-plots-hilbert-gamma)); and with few exceptions, these results were consistent between the reduced and maximal models of functional connectome similarity for both phase coupling and amplitude coupling functional connectomes (Figures A\@ref(fig:phase-similarity-plots-maximal-theta)-A\@ref(fig:amplitude-similarity-plots-maximal-alpha)).
-However, in general, the group-level effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small, depending on the contrast, coupling mode, and frequency band in question (Figures \@ref(fig:phase-similarity-plots-delta)-\@ref(fig:amplitude-similarity-plots-alpha)). Thus, although our results suggested that, on average, the phase coupling and amplitude coupling dynamics of underlying global network activity in each frequency band was differentiated between individuals across contexts *in our sample*, the influence of individual-dependent factors over and above the influence of stable group-dependent factors was either negligible or at best small. This is a clear departure from what has been described in the fMRI literature, where differences in functional connectome similarity within and between individuals have been reported to be stable, relatively large, and visually obvious, indicative of "a clear influence of individual features over and above common organizational principles at the network level," [@mareketal_SpatialTemporalOrganization_2018] such that fMRI functional connectomes may be well-suited for measuring and identifying the neural causes of variation in human behaviour, cognition, and their dysfunction [@grattonetal_FunctionalBrainNetworks_2018]. Our results do not inspire such confidence, and given the inherent limitations of EEG functional connectivity analysis, it is unclear to what extent they reflect genuinely small interindividual differences in electrophysiological connectivity across contexts, versus methodological limitations, selection bias, or random variation in our sample that may have caused us to underestimate or overestimate the magnitude of interindividual differences relative to the (unknown) true effect(s).
+However, in general, the group-level effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small, depending on the contrast, coupling mode, and frequency band in question (Figures \@ref(fig:phase-similarity-plots-delta)-\@ref(fig:amplitude-similarity-plots-alpha)). Thus, although our results suggested that, on average, the phase coupling and amplitude coupling dynamics of underlying global network activity in each frequency band was differentiated between individuals across contexts *in our sample*, the influence of individual-dependent factors over and above the influence of stable group-dependent factors was negligible or small at best. This is a clear departure from what has been described in the fMRI literature, where differences in functional connectome similarity within and between individuals have been reported to be stable, relatively large, and visually obvious, indicative of "a clear influence of individual features over and above common organizational principles at the network level," [@mareketal_SpatialTemporalOrganization_2018] such that fMRI functional connectomes may be well-suited for measuring and identifying the neural causes of variation in human behaviour, cognition, and their dysfunction [@grattonetal_FunctionalBrainNetworks_2018]. Our results do not inspire such confidence. Indeed, given the inherent limitations of EEG functional connectivity analysis, it is unclear to what extent our results reflect genuinely small interindividual differences in electrophysiological connectivity across contexts, versus methodological limitations, selection bias, or random variation in our sample that may have caused us to underestimate or overestimate the magnitude of interindividual differences relative to the (unknown) true effects.
-Finally, we note that although the the group-level effect sizes most compatible with our data, given the background model, more similar within than between individuals, on average, this relationship was not necessarily true for all participants. Indeed, there was a notable degree of outcome variability in the direction (Figure \@ref(fig:subset-similarity-contrast-barplot)) and magnitude (for reduced models, see Figures A\@ref(fig:phase-similarity-subset-plots-delta)-A\@ref(fig:amplitude-similarity-subset-plots-alpha); for maximal models, see Figures A\@ref(fig:phase-similarity-plots-maximal-theta)-A\@ref(fig:amplitude-similarity-plots-maximal-alpha)) of these effects when differences in functional connectome similarity were analyzed at an individual-level. These results were also a clear departure from what has been described in the fMRI literature, where differences in functional connectome similarity within and between individuals have been found to be consistently positive at an individual-level, with relatively little outcome variability in the magnitude of these differences [@grattonetal_FunctionalBrainNetworks_2018]. Moreover, because of the outcome variability in our sample, we caution against confusing the positive and precise estimates of average effects for the group-level contrasts with the predictability of individual outcomes, which were more variable in both their direction and magnitude. However, equally, we also caution against confusing the individual-level contrasts whose direction was unresolved with precise zero effects or evidence of no positive difference in functional connectome similarity. For many of these contrasts, the effect sizes most compatible with our data, given the background model, included a range of effects that---although generally still at most small---were not negligible; thus, even though they leave open the possibility of negative, zero, or positive but negligible effects, they also leave open the possibility of small effects that may be worth investigating further.
+Finally, we note that although the group-level effect sizes most compatible with our data, given the background model, were more similar within than between individuals, on average, this relationship was not true for all participants. Indeed, there was a notable degree of outcome variability in the direction (Figure \@ref(fig:subset-similarity-contrast-barplot)) and magnitude (for reduced models, see Figures A\@ref(fig:phase-similarity-subset-plots-delta)-A\@ref(fig:amplitude-similarity-subset-plots-alpha); for maximal models, see Figures A\@ref(fig:phase-similarity-plots-maximal-theta)-A\@ref(fig:amplitude-similarity-plots-maximal-alpha)) of these effects when differences in functional connectome similarity were analyzed at an individual level. These results were also a clear departure from what has been described in the fMRI literature, where differences in functional connectome similarity within and between individuals have been found to be consistently positive at an individual-level, with relatively little outcome variability in the magnitude of these differences [@grattonetal_FunctionalBrainNetworks_2018]. Moreover, because of the outcome variability in our sample, we caution against confusing the positive and precise estimates of average effects for the group-level contrasts with the predictability of individual outcomes, which were more variable in both their direction and magnitude. However, equally, we also caution against confusing the individual-level contrasts whose direction was unresolved with precise zero effects or evidence of no positive difference in functional connectome similarity. For many of these contrasts, the effect sizes most compatible with our data, given the background model, included a range of effects that---although generally still small---were not negligible; thus, even though they leave open the possibility of negative, zero, or positive but negligible effects, they also leave open the possibility of small effects that may be worth investigating further.
-Considered together, given the background model used to investigate our research question, our results did not generally demonstrate the feasibility of using EEG to study network variants due to the inconsistencies observed across participants in our sample. At best our findings leave open the possibility that EEG functional connectomes may be suitable measures of stable individual differences in whole brain functional network organization, given that both the group-level results and a subset of individual-level results showed consistently positive differences in functional connectome similarity within versus between individuals across contexts and frequency bands; however, equally, it is apparent that if the phase coupling and/or amplitude coupling dynamics of underlying global network activity across frequency bands are indeed influenced by stable individual-dependent factors, further work identifying and developing methods to reliably measure and quantify these stable individual differences is necessary.
+Considered together, given the background model used to investigate our research question, our results did not generally demonstrate the feasibility of using EEG to study network variants due to the inconsistencies observed across participants in our sample. At best our findings leave open the possibility that EEG functional connectomes may be suitable measures of stable individual differences in whole brain functional network organization, given that both the group-level results and a subset of individual-level results showed consistently positive differences in functional connectome similarity within versus between individuals across contexts and frequency bands; however, it is apparent that if the phase coupling and/or amplitude coupling dynamics of underlying global network activity across frequency bands are indeed influenced by stable individual-dependent factors, further work identifying and developing methods to reliably measure and quantify these stable individual differences is necessary.
## Consistency with related neurophysiological connectomics literature
@@ -18,13 +18,13 @@ Notably, the prominence of alpha band functional connectivity coincided with the
Furthermore, the pattern of group-level contrast results for phase coupling functional connectome similarities in the alpha band (Figure \@ref(fig:phase-similarity-plots-alpha)C) appeared to be influenced more by an individual-state effect than an individual-session effect (Figure \@ref(fig:outcome-plot), fourth plot); whereas (although subtle) the delta, theta, beta, and gamma bands (Figures \@ref(fig:phase-similarity-plots-delta)C, \@ref(fig:phase-similarity-plots-theta)C, \@ref(fig:phase-similarity-plots-beta)C, \@ref(fig:phase-similarity-plots-gamma)C) appeared to be influenced more by an individual-session effect than an individual-state effect (Figure \@ref(fig:outcome-plot), third plot). Although we did not predict this outcome, it may also be unsurprising, given that the typical widespread reduction in alpha band oscillations between eyes-closed and eyes-open resting state and associated changes in functional connectivity would be expected to produce such a pattern of results given that, within individuals, functional connectomes would be noticeably dissimilar between these states, on average, but similar within them; whereas the more subtle differences between eyes-closed and eyes-open resting state for other frequency bands would be less likely to produce such noticeable changes.
-Our results are also conceptually consistent with a recent study by @nentwichetal_FunctionalConnectivityEEG_2020, who investigated the relationship between twelve phenotypic variables (age, sex, socioeconomic status, intelligence, and diagnostic assessments for sleep disturbance, behavioural and emotional problems, attention-deficit/hyperactivity disorder, anxiety, inattention, mood, internet addiction, distress tolerance) and phase coupling functional connectome similarities in a large sample of typically and atypically developing children and adolescents during resting in the delta, theta, alpha, and beta bands. They found that all twelve variables were unable to explain variation in functional connectome similarities between and among individuals in the delta, theta, alpha, and beta bands; with the exception of sex and age in the beta band. Although our study did not examine the relationship between any variables of interest and functional connectome similarity, the findings of @nentwichetal_FunctionalConnectivityEEG_2020 indicate---similar to our findings---that (1) functional connectomes may become more differentiated between individuals as a function of frequency band, given they only found phenotype-connectome relationships in the beta band; and (2) network variants may be harder to detect with EEG. Regarding the latter point, @nentwichetal_FunctionalConnectivityEEG_2020 also investigated the relationship between the aforementioned twelve variables and fMRI functional connectome similarities in the same sample. Here they found that four variables (age, sex, intelligence, and the assessment for behavioural and emotional problems) were able to explain variation in functional connectome similarities between and among individuals, suggesting that fMRI functional connectomes may better capture practically relevant information compared to EEG.
+Our results are also conceptually consistent with a recent study by @nentwichetal_FunctionalConnectivityEEG_2020, who investigated the relationship between twelve phenotypic variables (age, sex, socioeconomic status, intelligence, and diagnostic assessments for sleep disturbance, behavioural and emotional problems, attention-deficit/hyperactivity disorder, anxiety, inattention, mood, internet addiction, distress tolerance) and phase coupling functional connectome similarities in a large sample of typically and atypically developing children and adolescents during resting in the delta, theta, alpha, and beta bands. They found that all twelve variables were unable to explain variation in functional connectome similarities between and among individuals in the delta, theta, alpha, and beta bands: with the exception of sex and age in the beta band. Although our study did not examine the relationship between any variables of interest and functional connectome similarity, the findings of @nentwichetal_FunctionalConnectivityEEG_2020 indicate---similar to our findings---that (1) functional connectomes may become more differentiated between individuals as a function of frequency band, given they only found phenotype-connectome relationships in the beta band; and (2) network variants may be harder to detect with EEG. Regarding the latter point, @nentwichetal_FunctionalConnectivityEEG_2020 also investigated the relationship between the aforementioned twelve variables and fMRI functional connectome similarities in the same sample. Here they found that four variables (age, sex, intelligence, and the assessment for behavioural and emotional problems) were able to explain variation in functional connectome similarities between and among individuals, suggesting that fMRI functional connectomes may better capture practically relevant information compared to EEG.
Finally, the smaller variations in within-individual functional connectome similarity across sessions and states observed in our sample were also consistent with previous EEG [@nentwichetal_FunctionalConnectivityEEG_2020], electrocorticography [ECoG\; @mostamesadaghiani_OscillationBasedConnectivityArchitecture_2021], and magnetoencephalography [MEG\; @colcloughetal_HowReliableAre_2016] research, which found that phase coupling and amplitude coupling functional connectomes were generally stable within individuals, showing moderate to high similarity within and between sessions and states across frequency bands. Additionally, in agreement with @colcloughetal_HowReliableAre_2016, we found that within-individual functional connectome similarity was generally higher when functional connectivity was estimated with the orthogonalized amplitude envelope correlation than with the phase lag index.
## Differences between phase and amplitude coupling
-Across all frequency bands, we found that functional connectivity estimated with the orthogonalized amplitude envelope correlation was generally smaller than with the phase lag index (Figure \@ref(fig:connectivity-histograms-figure)). As we discussed in the results section, we expected the estimates from these metrics to differ, given that the phase lag index and orthogonalized amplitude envelope correlation measure different aspects of functional connectivity in mathematically distinct ways. However, we also note that this difference is likely partially due to differences in the frequency specificity of the two methods, with the phase lag index having greater specificity than the orthogonalized amplitude envelope correlation. This difference in frequency specificity is attributable to the phase lag index being estimated and averaged over multiple frequency bins in each frequency band when using the multitaper method, versus the orthogonalized amplitude envelope correlation being estimated with a single analytic signal in each frequency band when using the Hilbert transform method. In Figures A\@ref(fig:phase-similarity-plots-hilbert-delta)-A\@ref(fig:phase-similarity-plots-hilbert-gamma) in Appendix A we show how the results change when the phase lag index is estimated using the Hilbert transform method. Across all five frequency bands we found that (1) functional connectivity was generally lower than we found with the multitaper method, but individual patterns of connectivity were similar or the same; and (2) functional connectome similarity was generally higher than we found with the multitaper method, but patterns of similarity and contrast effect sizes were similar or the same. Thus, although these changes were not great enough to change our conclusions, they did make the differences observed between the phase lag index and orthogonalized amplitude envelope correlation results less stark, and highlight how different spectral analysis methods can influence functional connectivity estimates if analysis parameters cannot be matched [cf. @bruns_FourierHilbertWaveletbased_2004].
+Across all frequency bands, we found that functional connectivity estimated with the orthogonalized amplitude envelope correlation was generally smaller than with the phase lag index (Figure \@ref(fig:connectivity-histograms-figure)). As we discussed in the results section, we expected the estimates from these metrics to differ, given that the phase lag index and orthogonalized amplitude envelope correlation measure different aspects of functional connectivity in mathematically distinct ways. However, we also note that this difference is likely partially due to differences in the frequency specificity of the two methods, with the phase lag index having greater specificity than the orthogonalized amplitude envelope correlation. This difference in frequency specificity is attributable to the phase lag index being estimated and averaged over multiple frequency bins in each frequency band when using the multitaper method, versus the orthogonalized amplitude envelope correlation being estimated with a single analytic signal in each frequency band when using the Hilbert transform method. In Figures A\@ref(fig:phase-similarity-plots-hilbert-delta)-A\@ref(fig:phase-similarity-plots-hilbert-gamma) in Appendix A we show how the results change when the phase lag index is estimated using the Hilbert transform method. Across all five frequency bands we found that (1) functional connectivity was generally lower than we found with the multitaper method, but individual patterns of connectivity were similar or the same; and (2) functional connectome similarity was generally higher than we found with the multitaper method, but patterns of similarity and contrast effect sizes were similar or the same. Thus, although these changes were not great enough to change our conclusions, they did make the differences observed between the phase lag index and orthogonalized amplitude envelope correlation results less stark, highlighting how different spectral analysis methods can influence functional connectivity estimates if analysis parameters cannot be matched [cf. @bruns_FourierHilbertWaveletbased_2004].
Another factor possibly contributing to the differences observed between the phase lag index and orthogonalized amplitude envelope correlation was the effect of epoch length on the two methods. @fraschinietal_EffectEpochLength_2016 compared the effect of non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 seconds) on broadband (1-20 Hz) functional connectivity estimates in a small sample of middle-aged adults during resting eyes-closed resting state. They found that epoch length affected both the magnitude of group-averaged mean functional connectivity and the distinctiveness of functional connectivity patterns. Specifically, group-averaged global functional connectivity was higher for the phase lag index than the orthogonalized amplitude envelope correlation at 1-6 second long epochs, with more comparable estimates between the two methods with 8-16 second long epochs; that group-averaged global functional connectivity decreased as a function of epoch length, with the phase lag index and orthogonalized amplitude envelope correlation stabilizing at 12 and 6 second long epochs, respectively; and that the distinctiveness of group-averaged functional connectivity patterns increased as a function of epoch length, with shorter epochs showing blurrier patterns relative to longer epochs where patterns became more distinct. Because Fraschini et al.'s [-@fraschinietal_EffectEpochLength_2016] results were based on broadband group-averaged global functional connectivity, they are not directly comparable to our own findings; however, they do highlight how preprocessing decisions can (differentially) influence functional connectivity estimates. In the context of our study, we emphasize the need for future work to explore how preprocessing decisions can influence individual-level functional connectivity and functional connectome similarity estimates, with a focus on identifying approaches that can achieve both high within-individual and low between-individual functional connectome similarity.
@@ -34,7 +34,7 @@ Factors that could explain the differences between our findings and what has bee
Network variants are likely sensitive to the spatial resolution of the neuroimaging system used to study them, as this influences the precision with which individual differences in functional connectivity can be measured. For EEG, the number of electrodes and distance between them determines spatial resolution, with higher electrode density corresponding to higher spatial resolution [@ferreeetal_SpatialResolutionScalp_2001; @robinsonetal_VeryHighDensity_2017; @ryynanenetal_EffectElectrodeDensity_2004; @ryynanenetal_EffectMeasurementNoise_2006]. With low electrode density, as in the 10-20 system, EEG typically has a spatial resolution of approximately 5 to 9 centimetres [@burleetal_SpatialTemporalResolutions_2015; @srinivasanetal_EstimatingSpatialNyquist_1998]. Higher density EEG systems can achieve a spatial resolution of up to 1 to 2 centimetres, but this still falls short of the spatial resolution of fMRI, which typically ranges from 500 microns to 4 millimetres [@glover_OverviewFunctionalMagnetic_2011]. Because EEG functional connectomes are naturally spatially blurrier than fMRI functional connectomes, network variants may be harder to detect or less pronounced in EEG, leading to smaller, less consistent, differences in functional connectome similarity within versus between individuals.[^9] Future work might address this possibility either by testing how individual differences in functional connectome similarity vary as a function of electrode density, or by using simultaneous (source-space) EEG and fMRI [@mulert_SimultaneousEEGFMRI_2013] to compare the strength and consistency of network variants between the two modalities in the same sample.
-Network variants are also likely sensitive to the methods used to estimate functional connectivity. There are two assumptions related to this that are important to consider when evaluating our results, and which may be worth investigating in future research. First, we assumed that five minute recordings would be sufficient to get stable functional connectivity estimates with EEG given that (1) the longer scanning times needed to reliably detect network variants with fMRI is related to its low sampling rate [@seitzmanetal_TraitlikeVariantsHuman_2019], and not to the actual timeframe at which neural processes occur; and (2) EEG samples a substantially larger number of time points than fMRI in a much shorter timeframe. Because we found that functional connectomes were generally stable within individuals, showing moderate to high similarity within and between sessions and states across timescales, we believe this assumption was justified. However, we do note that longer recordings have been found to improve the stability of fMRI functional connectivity estimates in a qualitatively distinct way from higher sampling rates [@birnetal_EffectScanLength_2013], likely due to slow network changes being more fully captured by longer recordings. Future work may benefit from testing if the stability of EEG functional connectivity estimates gain a similar improvement from longer recordings, particularly at lower frequencies such as the delta band; such work would pair nicely with the identifying the optimal epoch length(s) for band-limited, individual-level functional connectivity analysis [cf. @fraschinietal_EffectEpochLength_2016], as we discussed in the previous section.
+Network variants are also likely sensitive to the methods used to estimate functional connectivity. There are two assumptions related to this that are important to consider when evaluating our results, and which may be worth investigating in future research. First, we assumed that five-minute recordings would be sufficient to get stable functional connectivity estimates with EEG given that (1) the longer scanning times needed to reliably detect network variants with fMRI is related to its low sampling rate [@seitzmanetal_TraitlikeVariantsHuman_2019], and not to the actual timeframe at which neural processes occur; and (2) EEG samples a substantially larger number of time points than fMRI in a much shorter timeframe. Because we found that functional connectomes were generally stable within individuals, showing moderate to high similarity within and between sessions and states across timescales, we believe this assumption was justified. However, we do note that longer recordings have been found to improve the stability of fMRI functional connectivity estimates in a qualitatively distinct way from higher sampling rates [@birnetal_EffectScanLength_2013], likely due to slow network changes being more fully captured by longer recordings. Future work may benefit from testing if the stability of EEG functional connectivity estimates gain a similar improvement from longer recordings, particularly at lower frequencies such as the delta band; such work would pair nicely with the identifying the optimal epoch length(s) for band-limited, individual-level functional connectivity analysis [cf. @fraschinietal_EffectEpochLength_2016], as we discussed in the previous section.
Second, we assumed that network variants could be observed when considering only phase coupling and amplitude coupling with non-zero lag, discounting the true zero-lag coupling that occurs in functional networks. However, this discounting leads to an underestimation of true connectivity for both the phase lag index and amplitude envelope correlation by a non-trivial amount [@hippetal_LargescaleCorticalCorrelation_2012; @stametal_PhaseLagIndex_2007], and might also remove or attenuate individual-specific features of the functional connectome [@fraschinietal_RobustnessFunctionalConnectivity_2019]. Given that we generally found the strongest similarities between functional connectomes with homogeneously low patterns of coupling, non-zero lag phase and amplitude coupling methods may not be feasible for studying network variants in frequency bands where (1) the magnitude of coupling estimates is expected to be generally homogeneous for a given state, task, or functional connectivity metric; and (2) the chosen method of quantifying functional connectome similarity is sensitive to the distribution of coupling magnitudes. The most extreme examples of this in our study were with amplitude coupling functional connectomes in the delta, theta, beta, and gamma bands, where we found that unexpectedly high similarity within and between individuals was associated with functional connectomes with homogeneously low coupling estimates across EEG channel pairs for all connectomes (Figure A\@ref(fig:amplitude-similarity-delta-theta-beta-gamma)). As we discussed in the results section, these estimates were a statistically valid reflection of the data, given our method for quantifying functional connectome similarity; however, equally, we felt that they were neurophysiologically unrealistic, greatly exaggerating the magnitude of similarity between any pair of functional connectomes and failing to capture the intended nuance across participants, sessions, and states due to the limitations of our chosen method.
@@ -46,7 +46,7 @@ In addition to the discussion above, the following methodological and statistica
First, it is unclear how our results might generalize to more diverse, larger samples. In particular, our participants were healthy young adults in their twenties sampled from a western, educated, industrialized, rich, and democratic population, who have been shown to be outliers on a number of behavioural and cognitive measures in comparison with the rest of the human population [@henrichetal_WeirdestPeopleWorld_2010]. Differences in functional connectivity have been found with ageing [e.g., @samoginetal_AgeRelatedDifferencesFrequencyDependent_2022], neurological disease or disorder [e.g., @engelsetal_DecliningFunctionalConnectivity_2015], mental illnesses such as depression [e.g., @shimetal_AlteredCorticalFunctional_2018], head trauma [e.g., @caoslobounov_AlterationCorticalFunctional_2010], and alcoholism [e.g., @caoetal_DisturbedConnectivityEEG_2014]; thus, it might be reasonable to expect that our exclusion criteria removed meaningful variation from our sample. Additionally, given the size of our sample, and the inconsistencies we observed across participants, we caution against generalizing our results to samples with similar characteristics. Instead, we stress the importance of future work examining these relationships in larger, more representative samples, whose results can then be combined using meta-analytic studies and other cumulative approaches in order to come to more generalized scientific conclusions about electrophysiological network variants [@amrheinetal_InferentialStatisticsDescriptive_2019; @berneramrhein_WhyHowWe_2021; @nicholsetal_AccumulatingEvidenceEcology_2019; @nicholsetal_BetterApproachDealing_2021]. Based on the results of our study, at most we might conclude that---if network variants can be reliably detected with EEG---there is no guarantee that their presence can be detected in a given individual or data set, given the background models used to estimate functional connectivity, functional connectome similarity, and interindividual differences across contexts.
-Second, we assumed that the connectivity structure in each frequency band was stationary over the length of the recording for both phase and amplitude coupling functional connectomes. Although neural oscillations are naturally non-stationary, varying from moment to moment [@faisaletal_NoiseNervousSystem_2008], stationary patterns of functional connectivity during resting state have been identified in EEG [e.g., @olguin-rodriguezetal_CharacteristicFluctuationsStable_2018], fMRI [e.g., @laumannetal_StabilityBOLDFMRI_2017], and simultaneous EEG-fMRI studies [e.g., @danielarzate-menaetal_StationaryEEGPattern_2022], with consistent positive or negative coupling occurring between sites or electrodes, even when averaging over multiple epochs. These stationary patterns are thought to be the substrate for effective brain function, permitting the adaptability and efficiency needed to optimize responses to our often unpredictable environment [@garrettetal_MomenttomomentBrainSignal_2013], and thus reflect neuropsychologically relevant signals of interest. However, we do note that approaches accounting for nonlinear dynamics may provide complimentary insights to those that focus on the stable aspects of network variants. For example, @vandevilleetal_WhenMakesYou_2021 found that fMRI functional connectomes exhibit short transient bursts of uniqueness even at short time windows, with different resting state networks becoming more or less unique between individuals as a function of time window length, suggesting that the dynamic aspects of network variants are worth exploring.
+Second, we assumed that the connectivity structure in each frequency band was stationary over the length of the recording for both phase and amplitude coupling functional connectomes. Although neural oscillations are naturally non-stationary, varying from moment to moment [@faisaletal_NoiseNervousSystem_2008], stationary patterns of functional connectivity during resting state have been identified in EEG [e.g., @olguin-rodriguezetal_CharacteristicFluctuationsStable_2018], fMRI [e.g., @laumannetal_StabilityBOLDFMRI_2017], and simultaneous EEG-fMRI studies [e.g., @danielarzate-menaetal_StationaryEEGPattern_2022], with consistent positive or negative coupling occurring between sites or electrodes, even when averaging over multiple epochs. These stationary patterns are thought to be the substrate for effective brain function, permitting the adaptability and efficiency needed to optimize responses to our often-unpredictable environment [@garrettetal_MomenttomomentBrainSignal_2013], and thus reflect neuropsychologically relevant signals of interest. However, we do note that approaches accounting for nonlinear dynamics may provide complimentary insights to those that focus on the stable aspects of network variants. For example, @vandevilleetal_WhenMakesYou_2021 found that fMRI functional connectomes exhibit short transient bursts of uniqueness even at short time windows, with different resting state networks becoming more or less unique between individuals as a function of time window length, suggesting that the dynamic aspects of network variants are worth exploring.
Third, we limited our investigation to within-frequency analyses of functional connectivity and network similarity. Although cross-frequency coupling is also considered to be a key mechanism by which the brain transmits and processes information, integrating functional systems across multiple spatiotemporal scales [@canoltyknight_FunctionalRoleCrossfrequency_2010], we did not explore for the presence of network variants in cross-frequency coupling functional connectomes given that most research on cross-frequency coupling focuses on coupling within brain regions or between a small number of regions of interest, rather than the entire connectome. Only a handful of studies have demonstrated that cross-frequency coupling also occurs in whole brain functional networks [@keiteletal_VisualCortexResponses_2017; @siebenhuhneretal_CrossfrequencySynchronizationConnects_2016; @palva_PhaseSynchronyNeuronal_2005; @vandermeijetal_PhaseAmplitudeCouplingHuman_2012], and the validity of these findings has recently come into question due to standard cross-frequency coupling analyses showing significant cross-frequency coupling in the absence of any underlying physiological coupling [@aruetal_UntanglingCrossfrequencyCoupling_2015]. Recently, however, @siebenhuhneretal_GenuineCrossfrequencyCoupling_2020 were able to demonstrate the presence of true cross-frequency coupling in whole brain functional networks---ruling out the influence of spurious coupling---in both a small sample of presurgical epilepsy patients during eyes-closed resting state using stereo-EEG, and a small sample of healthy controls during eyes-open resting state using magnetoencephalography. Moreover, they found that network strength was predictive of individual differences on several cognitive tasks in the healthy control sample (the other sample did not complete these tasks); thus, cross-frequency coupling may be a worthwhile avenue for future EEG network variant research if methods for estimating true cross-frequency coupling are validated, improved, and made more accessible [e.g., @idajietal_HarmoniMethodEliminating_2022].
diff --git a/manuscripts/child-documents/frontmatter.Rmd b/manuscripts/child-documents/frontmatter.Rmd
index e252ad1..8b89c11 100644
--- a/manuscripts/child-documents/frontmatter.Rmd
+++ b/manuscripts/child-documents/frontmatter.Rmd
@@ -36,11 +36,11 @@ CALGARY, ALBERTA
-OCTOBER, 2022
+FEBRUARY, 2024
-© Michael McCarthy 2022
+© Michael McCarthy 2024
:::
```{r}
@@ -51,7 +51,7 @@ officer::block_section(officer::prop_section(type = "nextPage"))
Abstract
:::
-Functional MRI (fMRI) studies have shown that the human functional connectome exhibits reliable and substantial variability in organization across individuals, so-called network variants. However, it is unclear whether neuroimaging modalities that measure different aspects of brain function show similar evidence of such individual differences. Here we explored the feasibility of using electroencephalography (EEG) to study network variants using repeated measures eyes-closed and eyes-open resting state data from 14 participants taken across three sessions over the course of three months---estimating how much and in which ways band-limited phase coupling and amplitude coupling functional connectomes differed in similarity within and between individuals across contexts. For each coupling mode and frequency band, we hypothesized that if functional connectome organization was influenced by stable individual-dependent factors in our sample, then functional connectomes would be more similar within than between individuals across all contexts, on average, with smaller variations in similarity related to session or state. Overall, our results were inconclusive. Although we generally found consistently positive differences in functional connectome similarity across coupling modes, frequency bands, and contexts---these differences ranged from practically nil to at most small, on average, and were inconsistent across participants. We discuss several factors that may explain the differences between our results and the larger, more consistent effects reported in fMRI network variant studies, such as the spatial and temporal resolution of EEG and fMRI, and the methods used to estimate functional connectivity. We then offer suggestions for future EEG research that might address some shortcomings of our study.
+Functional MRI (fMRI) studies have shown that the human functional connectome exhibits reliable and substantial variability in organization across individuals, so-called network variants. However, it is unclear whether neuroimaging modalities that measure different aspects of brain function show similar evidence of such individual differences. Here we explored the feasibility of using electroencephalography (EEG) to study network variants using repeated measures eyes-closed and eyes-open resting state data from 14 participants taken across three sessions over the course of three months---estimating how much and in which ways band-limited phase coupling and amplitude coupling functional connectomes differed in similarity within and between individuals across contexts. For each coupling mode and frequency band, we hypothesized that if functional connectome organization was influenced by stable individual-dependent factors in our sample, then functional connectomes would be more similar within than between individuals across all contexts, on average, with smaller variations in similarity related to session or state. Overall, our results were inconclusive. Although we generally found consistently positive differences in functional connectome similarity across coupling modes, frequency bands, and contexts on average---depending on the comparison, these differences were either negligible or at most small, and were inconsistent across participants. We discuss several factors that may explain the differences between our results and the larger, more consistent effects reported in fMRI network variant studies, such as the spatial and temporal resolution of EEG and fMRI, and the methods used to estimate functional connectivity. We then offer suggestions for future EEG research that might address some shortcomings of our study.
*Keywords:* Electroencephalography, Functional Connectivity, Network Variants, Individual Differences
diff --git a/manuscripts/child-documents/introduction.Rmd b/manuscripts/child-documents/introduction.Rmd
index 9f32e8b..835583a 100644
--- a/manuscripts/child-documents/introduction.Rmd
+++ b/manuscripts/child-documents/introduction.Rmd
@@ -2,15 +2,15 @@
Human brains, behaviour, and cognition exhibit important differences between and within individuals across the lifespan. A central goal of modern neuroscience is to identify the neural *causes of variation* [@saucematzel_CausesVariationLearning_2013] in human behaviour, cognition, and their dysfunction by characterizing how brain function differs between and within individuals under a variety of conditions using functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) [for fMRI, see @duboisadolphs_BuildingScienceIndividual_2016; for EEG/MEG, see @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020]. Each of these neuroimaging modalities measure different aspects of brain function across different spatial and temporal scales (and thus have their own strengths and limitations), however, a common point of interest between them is their application to the study of individual differences in whole brain functional network organization [@duboisadolphs_BuildingScienceIndividual_2016; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020].
-Whole brain functional networks are graphs whose *nodes* are either spatially separate brain regions (for fMRI or source-reconstructed EEG/MEG) or sensors (for EEG/MEG) distributed across the entire cerebral cortex,[^1] and whose *edges* quantify the observed statistical dependency between two oscillatory neural signals originating from different nodes in the network. In common neuroscientific parlance, edges in the network represent *functional connectivity* or *coupling* between nodes, subgraphs in the network represent distinct *functional networks*, and the entire graph of the network represents a *functional connectome* [@barchetal_FunctionHumanConnectome_2013; @vanessenetal_HumanConnectomeProject_2012; cf. @spornsetal_HumanConnectomeStructural_2005]. Functional connectomes can be constructed by estimating phase coupling, amplitude coupling, or temporal correlations between all pairs of nodes after band-pass filtering the signals to frequency bands of interest [@bastosschoffelen_TutorialReviewFunctional_2016]. For fMRI studies, functional connectivity is typically estimated using temporal correlations between signals in a single infraslow frequency band (e.g., 0.009-0.08 Hz), resulting in a single functional connectome; whereas for EEG/MEG studies, functional connectivity is typically estimated using phase coupling and/or amplitude coupling in multiple frequency bands over a wide frequency range (e.g., 1-50 Hz) due to neurophysiological signals containing a mixture of distinct neuronal oscillation frequencies, resulting in multiple concurrent functional connectomes [@sadaghianiwirsich_IntrinsicConnectomeOrganization_2020].
+Whole brain functional networks are graphs whose *nodes* are either spatially separate brain regions (for fMRI or source-reconstructed EEG/MEG) or sensors (for EEG/MEG) distributed across the entire cerebral cortex,[^1] and whose *edges* quantify the observed statistical dependency between two oscillatory neural signals originating from different nodes in the network. In common neuroscientific parlance, edges in the network represent *functional connectivity* or *coupling* between nodes, subgraphs in the network represent distinct *functional networks*, and the entire graph of the network represents a *functional connectome* [@barchetal_FunctionHumanConnectome_2013; @vanessenetal_HumanConnectomeProject_2012; cf. @spornsetal_HumanConnectomeStructural_2005]. Functional connectomes can be constructed by estimating phase coupling, amplitude coupling, or temporal correlations between all pairs of nodes after band-pass filtering the signals to frequency bands of interest [@bastosschoffelen_TutorialReviewFunctional_2016], and are typically represented by an adjacency matrix of the weighted edges. For fMRI studies, functional connectivity is typically estimated using temporal correlations between signals in a single infraslow frequency band (e.g., 0.009-0.08 Hz), resulting in a single functional connectome; whereas for EEG/MEG studies, functional connectivity is typically estimated using phase coupling and/or amplitude coupling in multiple frequency bands over a wide frequency range (e.g., 1-50 Hz) due to neurophysiological signals containing a mixture of distinct neuronal oscillation frequencies, resulting in multiple concurrent functional connectomes [@sadaghianiwirsich_IntrinsicConnectomeOrganization_2020].
-Over the last two decades, convergent findings from group-averaged fMRI studies have identified that the human functional connectome is governed by an intrinsic functional architecture [@foxetal_HumanBrainIntrinsically_2005; @petersensporns_BrainNetworksCognitive_2015; @raichle_RestlessBrainHow_2015] wherein functional networks exhibit spontaneous coupling between their nodes that can be captured in both the absence and presence of cognitive demands [@poweretal_FunctionalNetworkOrganization_2011; @uddinetal_UniversalTaxonomyMacroscale_2019; @yeoetal_OrganizationHumanCerebral_2011]. Some of these functional networks are most easily detected in the absence of cognitive demands (i.e., in a *resting state*); others are most easily detected in the presence of cognitive demands (i.e., in a *task state*); and some, such as the default mode network [@biswaletal_FunctionalConnectivityMotor_1995; @greiciusetal_FunctionalConnectivityResting_2003; @raichleetal_DefaultModeBrain_2001; @raichlesnyder_DefaultModeBrain_2007], can be easily detected in both resting and task states [@greiciusmenon_DefaultModeActivityPassive_2004; @honeyetal_PredictingHumanRestingstate_2009]. The spatial organization of these *intrinsic connectivity networks* [@seeleyetal_DissociableIntrinsicConnectivity_2007] has been found to be very stable, exhibiting only minor changes under varying cognitive demands [@coleetal_IntrinsicTaskEvokedNetwork_2014; @grattonetal_FunctionalBrainNetworks_2018; @krienenetal_ReconfigurableTaskdependentFunctional_2014], and largely persisting in unconscious states such as sleep [@picchionietal_SleepFunctionalConnectome_2013]. More recent studies using group-averaged and source-reconstructed EEG/MEG or simultaneous fMRI-EEG have also corroborated these findings, implicating that a similar organization of the human functional connectome is present across the full range of oscillatory frequencies that are typically measured with EEG/MEG [@engeletal_IntrinsicCouplingModes_2013; @mostamesadaghiani_OscillationBasedConnectivityArchitecture_2021; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @wirsichetal_RelationshipEEGFMRI_2021].
+Over the last two decades, convergent findings from group-averaged fMRI studies have identified that the human functional connectome is governed by an intrinsic functional architecture [@foxetal_HumanBrainIntrinsically_2005; @petersensporns_BrainNetworksCognitive_2015; @raichle_RestlessBrainHow_2015] wherein functional networks exhibit spontaneous coupling between their nodes that can be captured in both the absence and presence of cognitive demands [@poweretal_FunctionalNetworkOrganization_2011; @uddinetal_UniversalTaxonomyMacroscale_2019; @yeoetal_OrganizationHumanCerebral_2011]. A standardized taxonomy of these *intrinsic connectivity networks* [@seeleyetal_DissociableIntrinsicConnectivity_2007] has not yet been adopted [@uddinetal_UniversalTaxonomyMacroscale_2019; @uddinetal_ControversiesProgressStandardization_2023]; however, the most prominent networks in the literature are commonly delineated and named according to their putative functions.[^99] For example, the most common description delineates these networks into those associated with higher-level functions (default mode, central executive, and salience networks) and those associated with sensorimotor processing (auditory, visual, and sensorimotor networks). Some of these networks are most easily detected in the absence of cognitive demands (i.e., in a *resting state*); others are most easily detected in the presence of cognitive demands (i.e., in a *task state*); and some, such as the default mode network [@biswaletal_FunctionalConnectivityMotor_1995; @greiciusetal_FunctionalConnectivityResting_2003; @raichleetal_DefaultModeBrain_2001; @raichlesnyder_DefaultModeBrain_2007], can be easily detected in both resting and task states [@greiciusmenon_DefaultModeActivityPassive_2004; @honeyetal_PredictingHumanRestingstate_2009]. Additionally, the spatial organization of these networks has been found to be very stable, exhibiting only minor changes under varying cognitive demands [@coleetal_IntrinsicTaskEvokedNetwork_2014; @grattonetal_FunctionalBrainNetworks_2018; @krienenetal_ReconfigurableTaskdependentFunctional_2014], and largely persisting in unconscious states such as sleep [@picchionietal_SleepFunctionalConnectome_2013]. More recent studies using group-averaged and source-reconstructed EEG/MEG or simultaneous fMRI-EEG have also corroborated these findings, implicating that (1) a similar organization of the human functional connectome is present across the full range of oscillatory frequencies that are typically measured with EEG/MEG [@engeletal_IntrinsicCouplingModes_2013; @mostamesadaghiani_OscillationBasedConnectivityArchitecture_2021; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @wirsichetal_RelationshipEEGFMRI_2021]; and (2) hemodynamic and electrophysiological functional connectomes are likely different expressions of the same underlying brain activity [@danielarzate-menaetal_StationaryEEGPattern_2022; @mantinietal_ElectrophysiologicalSignaturesResting_2007; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020].
However, following the discovery of a stable intrinsic organization of the group-averaged human functional connectome, a growing number of studies using fMRI [@bijsterboschetal_RelationshipSpatialConfiguration_2018; @finnetal_FunctionalConnectomeFingerprinting_2015; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_IndividualspecificFeaturesBrain_2017; @grattonetal_FunctionalBrainNetworks_2018; @kongetal_SpatialTopographyIndividualSpecific_2019; @krausetal_NetworkVariantsAre_2021; @miranda-dominguezetal_ConnectotypingModelBased_2014; @muelleretal_IndividualVariabilityFunctional_2013; @seitzmanetal_TraitlikeVariantsHuman_2019; @smithetal_BrainHubsDefined_2023]---and to a lesser extent---EEG [@nentwichetal_FunctionalConnectivityEEG_2020] and MEG [@colcloughetal_HowReliableAre_2016] have identified reliable and substantial variability in functional connectome organization across *every* individual studied so far that is either underestimated or missed entirely by group-averaged models of the human functional connectome [@fedorenko_EarlyOriginsGrowing_2021; @fedorenkoblank_BrocaAreaNot_2020; @gordonetal_PrecisionFunctionalMapping_2017; @gordonnelson_ThreeTypesIndividual_2021; @muelleretal_IndividualVariabilityFunctional_2013; @speelmanmcgann_HowMeanMean_2013; @zillesamunts_IndividualVariabilityNot_2013]. This has been demonstrated using a variety of approaches. For example, using community detection algorithms, @laumannetal_FunctionalSystemAreal_2015 found evidence of an idiosyncratic spatial organization between individual and group-averaged functional connectomes such that certain functional networks present in the individual were not present in the group (and vice versa); comparing spatial correlations between individual and group-averaged functional connectomes, @seitzmanetal_TraitlikeVariantsHuman_2019 found similar evidence of an idiosyncratic spatial organization across individuals, wherein certain spatially contiguous cortical regions were weakly correlated between individual and group-averaged functional connectomes (the location, size, and functional network assignments of these regions differed across individuals); and comparing functional connectome similarity between and within individuals, @grattonetal_FunctionalBrainNetworks_2018 found that functional connectomes were consistently more similar within than between individuals regardless of varying cognitive demands or time. Borrowing from @seitzmanetal_TraitlikeVariantsHuman_2019, here we use the term *network variants* as a catch-all shorthand to refer to individual differences in functional connectome organization relative to either a group-averaged functional connectome or other individuals' functional connectomes. These individual differences may present themselves in several forms, including interindividual variability in connectivity strength, the size and position of network nodes, or network topography [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023].
-The purpose of this thesis is to explore the feasibility of studying network variants with EEG, addressing the growing need to incorporate findings derived from neurophysiological imaging modalities in order to enrich our understanding of individual differences in whole brain functional network organization beyond what can be learned from fMRI alone [@grattonetal_FunctionalBrainNetworks_2018; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @uddinetal_ControversiesProgressStandardization_2023]. Whereas the first studies of the group-averaged human functional connectome using fMRI date back to the mid-2000s [e.g., @beckmannetal_InvestigationsRestingstateConnectivity_2005], studies using EEG/MEG did not begin until the early 2010s [e.g., @brookesetal_MeasuringFunctionalConnectivity_2011], and the investigation and characterization of whole brain functional networks continues to be heavily biased towards fMRI research methods [@uddinetal_ControversiesProgressStandardization_2023]. Likewise, although the first studies of network variants using fMRI date back to the early 2010s [e.g., @muelleretal_IndividualVariabilityFunctional_2013], we are only aware of one recently published study using EEG [@nentwichetal_FunctionalConnectivityEEG_2020] and another using MEG [@colcloughetal_HowReliableAre_2016]; thus, current knowledge about network variants is almost exclusively based on infraslow frequency functional connectomes measured with fMRI, and it is an open question whether or not higher frequency functional connectomes measured with neuroimaging modalities such as EEG share similar evidence of stable individual differences [@grattonetal_FunctionalBrainNetworks_2018; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020]. Here we take one modest step towards addressing this question.
+The purpose of this thesis is to explore the feasibility of studying network variants with EEG, addressing the growing need to incorporate findings derived from neurophysiological imaging modalities to enrich our understanding of individual differences in whole brain functional network organization beyond what can be learned from fMRI alone [@grattonetal_FunctionalBrainNetworks_2018; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @uddinetal_ControversiesProgressStandardization_2023]. Whereas the first studies of the group-averaged human functional connectome using fMRI date back to the mid-2000s [e.g., @beckmannetal_InvestigationsRestingstateConnectivity_2005], studies using EEG/MEG did not begin until the early 2010s [e.g., @brookesetal_MeasuringFunctionalConnectivity_2011], and the investigation and characterization of whole brain functional networks continues to be heavily biased towards fMRI research methods [@uddinetal_ControversiesProgressStandardization_2023]. Likewise, although the first studies of network variants using fMRI date back to the early 2010s [e.g., @muelleretal_IndividualVariabilityFunctional_2013], we are only aware of one recently published study using EEG [@nentwichetal_FunctionalConnectivityEEG_2020] and another using MEG [@colcloughetal_HowReliableAre_2016]; thus, current knowledge about network variants is almost exclusively based on infraslow frequency functional connectomes measured with fMRI, and it is an open question whether or not higher frequency functional connectomes measured with neuroimaging modalities such as EEG share similar evidence of stable individual differences [@grattonetal_FunctionalBrainNetworks_2018; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020]. Here we take one modest step towards addressing this question.
-The remainder of the introduction is organized as follows: First, we review the intrinsic functional organization of the human brain, focusing on the features of computation and communication in neuronal networks that functional connectivity analysis seeks to (at least partly) quantify. Second, we review the origins of the oscillatory neural signals measured by fMRI and EEG and their connection to underlying neuronal activity, as well as some of the challenges fMRI and EEG face in the pursuit of studying network variants. Third, we provide an overview of functional connectivity analysis, with a focus on methodology in order to build intuition around the interpretation of phase coupling and amplitude coupling. Fourth, we provide background on the motivations for network variant research, followed by a review of key findings. Finally, we introduce the study conducted for this thesis.
+The remainder of the introduction is organized as follows: First, we review the intrinsic functional organization of the human brain, focusing on the features of computation and communication in neuronal networks that functional connectivity analysis seeks to (at least partly) quantify. Second, we review oscillatory neural signals measured by fMRI and EEG and their connection to underlying neuronal activity, as well as some of the challenges fMRI and EEG face in the pursuit of studying network variants. Third, we provide an overview of functional connectivity analysis, with a focus on methodology in order to build intuition around the interpretation of phase coupling and amplitude coupling. Fourth, we provide background on the motivations for network variant research, followed by a review of key findings. Finally, we introduce the study conducted for this thesis.
## Intrinsic functional organization of the human brain
@@ -18,25 +18,25 @@ The adult human brain consists of approximately 86 billion neurons that are orga
Neuronal activity is primarily intrinsic---occurring in an ongoing manner not directly associated with external stimuli or cognitive demands---and can be characterized by its high energy costs, oscillatory patterning, and moment-to-moment variability [@buzsaki_RhythmsBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015]. Intrinsic activity accounts for the majority of the brain's energy costs [@raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015]. In the absence of behavioural and cognitive demands, brain energy consumption accounts for around 20% of all energy consumed by the body of the average adult human; however, this high rate of ongoing energy consumption is affected very little by behavioural and cognitive demands, with even the most arousing perceptual tasks and vigorous motor tasks causing at most a 5% difference in additional energy consumption compared to the resting state baseline [@raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015]. The majority of this energy budget is devoted to neuronal signalling processes, such as postsynaptic glutamate receptors, action potentials, resting potentials, presynaptic neurotransmitter release, and neurotransmitter recycling [@howarthetal_UpdatedEnergyBudgets_2012; @raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015; @raichlemintun_BrainWorkBrain_2006], and therefore, to computation and communication in neuronal networks.
-Oscillatory patterning of neuronal activity is a basic property of normal brain function observable throughout the mammalian brain at multiple spatial and temporal scales, whose spatiotemporal properties have remained relatively constant across all species regardless of brain size [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @cohen_WhereDoesEEG_2017]. The constancy of oscillations across species suggests that as the brain has scaled in size over the course of evolution---with the number of neurons and their connections varying enormously between species---the timing of neuronal activity has been preserved as an important organizing principle for brain normal function [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @fries_RhythmsCognitionCommunication_2015]. Indeed, neuronal networks have a natural tendency to engage in oscillatory activity precisely because the intrinsic properties of both individual neurons and canonical circuit motifs favour rhythmic activity instead of continuous activity [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakietal_OriginExtracellularFields_2012; @singer_NeuronalOscillationsUnavoidable_2018; @turkheimeretal_BrainCodeIts_2015; @whittingtonetal_FutureNeuronalOscillation_2018].
+Oscillatory patterning, which reflects the timing of neuronal activity, is a basic property of normal brain function [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @cohen_WhereDoesEEG_2017; @fries_RhythmsCognitionCommunication_2015]. Neuronal networks have a natural tendency to engage in oscillatory activity because the intrinsic properties of both individual neurons and canonical circuit motifs favour rhythmic activity instead of continuous activity [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakietal_OriginExtracellularFields_2012; @singer_NeuronalOscillationsUnavoidable_2018; @turkheimeretal_BrainCodeIts_2015; @whittingtonetal_FutureNeuronalOscillation_2018]. In individual neurons, the refractory period following an action potential naturally imposes cyclic fluctuations of excitability wherein oscillatory patterning can appear as either fluctuations of the membrane potential or as rhythmic patterns of action potentials, which can then entrain postsynaptic neurons to oscillate in synchrony with the inducing pulses if the pulse frequency is close to their preferred membrane resonance frequency [@singer_NeuronalOscillationsUnavoidable_2018]. At the level of local neuronal populations, canonical circuit motifs such as negative feedback loops can give rise to large-scale oscillations as excitatory neurons drive inhibitory neurons that subsequently inhibit the very same excitatory neurons, naturally leading to an oscillatory patterning of responses between the presynaptic and postsynaptic networks [@buzsaki_RhythmsBrain_2011; @buzsakiwang_MechanismsGammaOscillations_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018].
-In individual neurons, the refractory period following an action potential naturally imposes cyclic fluctuations of excitability wherein oscillatory patterning can appear as either fluctuations of the membrane potential or as rhythmic patterns of action potentials, which can then entrain postsynaptic neurons to oscillate in synchrony with the inducing pulses if the pulse frequency is close to their preferred membrane resonance frequency [@singer_NeuronalOscillationsUnavoidable_2018]. At the level of local neuronal populations, canonical circuit motifs such as negative feedback loops can give rise to large-scale oscillations as excitatory neurons drive inhibitory neurons that subsequently inhibit the very same excitatory neurons, naturally leading to an oscillatory patterning of responses between the presynaptic and postsynaptic networks [@buzsaki_RhythmsBrain_2011; @buzsakiwang_MechanismsGammaOscillations_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018]. Here the frequency, regularity, and probability of oscillatory coupling depends on the time constants of excitatory and inhibitory postsynaptic potentials, which provide windows of alternating reduced and enhanced excitability for action potentials to occur [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018]. If the time constants are similar between the presynaptic and postsynaptic networks, incoming excitatory postsynaptic potentials will arrive at the the peak of enhanced excitability and have a high probability to summate effectively and generate action potentials; however, if the time constants are too dissimilar, excitatory postsynaptic potentials will arrive during inhibitory windows and have a lower probability to summate effectively and reach the firing threshold because of hyperpolarization [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018]. Thus, oscillations provide a putative mechanism through which the brain can dynamically coordinate the flow of information by effectively gating or biasing whether computations are amplified or ignored by different senders and receivers throughout the network [@bastosschoffelen_TutorialReviewFunctional_2016; @buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakiwatson_BrainRhythmsNeural_2012; @engeletal_IntrinsicCouplingModes_2013; @garrettetal_MomenttomomentBrainSignal_2013; @schnitzlergross_NormalPathologicalOscillatory_2005].
+Here the frequency, regularity, and probability of oscillatory coupling depends on the time constants of excitatory and inhibitory postsynaptic potentials, which provide windows of alternating reduced and enhanced excitability for action potentials to occur [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018]. If the time constants are similar between the presynaptic and postsynaptic networks, incoming excitatory postsynaptic potentials will arrive at the peak of enhanced excitability and have a high probability to summate effectively and generate action potentials; however, if the time constants are too dissimilar, excitatory postsynaptic potentials will arrive during inhibitory windows and have a lower probability to summate effectively and reach the firing threshold because of hyperpolarization [@buzsaki_RhythmsBrain_2011; @buzsakiwatson_BrainRhythmsNeural_2012; @fries_RhythmsCognitionCommunication_2015; @singer_NeuronalOscillationsUnavoidable_2018]. Thus, oscillations provide a putative mechanism through which the brain can dynamically coordinate the flow of information by effectively gating or biasing whether computations are amplified or ignored by different senders and receivers throughout the network [@bastosschoffelen_TutorialReviewFunctional_2016; @buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakiwatson_BrainRhythmsNeural_2012; @engeletal_IntrinsicCouplingModes_2013; @garrettetal_MomenttomomentBrainSignal_2013; @schnitzlergross_NormalPathologicalOscillatory_2005].
-Neuronal networks in the cerebral cortex support several families of oscillations (oscillatory bands) that act relatively independently, are continuously present, and span from approximately 0.05 Hz to 500 Hz, covering more than four orders of magnitude on a temporal scale [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakiwatson_BrainRhythmsNeural_2012]. These oscillatory bands follow a hierarchical system defined by frequency bands with logarithmically increasing centre frequencies and relatively constant frequency width ratios between neighbouring bands (Figure \@ref(fig:oscillatory-bands-plot)), leading to a separation of oscillatory bands [@penttonenbuzsaki_NaturalLogarithmicRelationship_2003]. Each oscillatory band is characterized by a distinct temporal processing window determined by its frequency, and several rhythms can temporally coexist, compete, or otherwise interact with each other in the same or different neuronal populations [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004]. The number of neurons that can participate in a given rhythm is constrained by the slow axon conduction velocity of neurons; thus, relative to higher frequency oscillations, lower frequency oscillations involve more neurons and are associated with larger membrane potential fluctuations because in longer temporal windows the action potentials of many more presynaptic neurons can be integrated [@buzsakiwatson_BrainRhythmsNeural_2012]. Because of this structural constraint, when multiple oscillations are present simultaneously, the phase of slower oscillations modulates the amplitude of faster oscillations [@buzsakiwatson_BrainRhythmsNeural_2012].
+Neuronal networks in the cerebral cortex support several families of oscillations (oscillatory bands) that act relatively independently, are continuously present, and span from approximately 0.05 Hz to 500 Hz, covering more than four orders of magnitude on a temporal scale [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004; @buzsakiwatson_BrainRhythmsNeural_2012]. These oscillatory bands are defined as frequency bands with logarithmically increasing centre frequencies and relatively constant frequency width ratios between neighbouring bands [Figure \@ref(fig:oscillatory-bands-plot)\; @penttonenbuzsaki_NaturalLogarithmicRelationship_2003]. Each band is characterized by a distinct temporal processing window determined by its frequency, and several rhythms can temporally coexist, compete, or otherwise interact with each other locally or globally [@buzsaki_RhythmsBrain_2011; @buzsakidraguhn_NeuronalOscillationsCortical_2004]. The number of neurons that can participate in a given rhythm is constrained by the slow axon conduction velocity of neurons; thus, relative to higher frequency oscillations, lower frequency oscillations involve more neurons and are associated with larger membrane potential fluctuations because in longer temporal windows the action potentials of many more presynaptic neurons can be integrated [@buzsakiwatson_BrainRhythmsNeural_2012]. Because of this structural constraint, when multiple oscillations are present simultaneously, the phase of slower oscillations modulates the amplitude of faster oscillations [@buzsakiwatson_BrainRhythmsNeural_2012].
```{r oscillatory-bands-plot}
#| fig.height: 3.630290
-#| fig.cap: "Oscillatory bands in the cerebral cortex follow a linear progression of frequencies on the natural logarithmic scale with relatively constant ratios between neighbouring bands [@penttonenbuzsaki_NaturalLogarithmicRelationship_2003]. The lower and upper limits of neighbouring bands overlap, resulting in a frequency coverage of more than four orders of magnitude on a temporal scale. For each band, the approximate bandwidth and centre frequency is shown along with its commonly used name."
+#| fig.cap: "Oscillatory bands in the cerebral cortex follow a linear progression of frequencies on the natural logarithmic scale with relatively constant ratios between neighbouring bands (Penttonen & Buzsáki, 2003). The lower and upper limits of neighbouring bands overlap, resulting in a frequency coverage of more than four orders of magnitude on a temporal scale. For each band, the approximate bandwidth and centre frequency is shown along with its commonly used name."
knitr::include_graphics(here::here("figures", "oscillatory-bands-plot.png"))
```
-Along with their system of rhythms, neuronal networks in the cerebral cortex are characterized by their high moment-to-moment variability---wherein ever-changing conditions driven largely by intrinsic activity provoke unceasing transitions between qualitatively different states of coupling within and between neuronal populations throughout the network [@arzate-menaetal_StationaryEEGPattern_2022; @faisaletal_NoiseNervousSystem_2008; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @michelkoenig_EEGMicrostatesTool_2018; @steinetal_NeuronalVariabilityNoise_2005]. These state-to-state transitions are governed by itinerant dynamics [@kanekotsuda_ChaoticItinerancy_2003], whereby the network explores multiple discrete states of coupling which may be continually revisited over time rather than settling into any particular state, providing a necessary level of moment-to-moment flexibility to respond to a greater range of imperfect, changing, and otherwise variable stimuli in a way that precludes overfitting whilst maintaining some level of stability and persistence [@breakspear_DynamicModelsLargescale_2017; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sporns_ComplexBrainConnectivity_2022]. In other words, this balance of moment-to-moment variability with recurrence acts as a kind of dynamical scaffold around which the network can organize, capable of creating a large and variable repertoire of stable intrinsic network states when integrated over longer time periods [@arzate-menaetal_StationaryEEGPattern_2022; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @sporns_ComplexBrainConnectivity_2022].
+Along with their system of rhythms, neuronal networks in the cerebral cortex are characterized by their high moment-to-moment variability---wherein ever-changing conditions driven largely by intrinsic activity provoke unceasing transitions between qualitatively different states of coupling within and between neuronal populations throughout the network [@danielarzate-menaetal_StationaryEEGPattern_2022; @faisaletal_NoiseNervousSystem_2008; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @michelkoenig_EEGMicrostatesTool_2018; @steinetal_NeuronalVariabilityNoise_2005]. These state-to-state transitions are governed by itinerant dynamics [@kanekotsuda_ChaoticItinerancy_2003], whereby the network explores multiple discrete states of coupling which may be continually revisited over time rather than settling into any particular state, providing a necessary level of moment-to-moment flexibility to respond to a greater range of imperfect, changing, and otherwise variable stimuli in a way that precludes overfitting whilst maintaining some level of stability and persistence [@breakspear_DynamicModelsLargescale_2017; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sporns_ComplexBrainConnectivity_2022]. In other words, this balance of moment-to-moment variability with recurrence acts as a kind of dynamical scaffold around which the network can organize, capable of creating a large and variable repertoire of stable intrinsic network states when integrated over longer time periods [@danielarzate-menaetal_StationaryEEGPattern_2022; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @sporns_ComplexBrainConnectivity_2022]. Because of this behaviour, the coupling dynamics of neuronal networks can often be sufficiently modelled using methods that (1) do not take the temporal order of time points into account, and (2) assume that the time series is stationary [@danielarzate-menaetal_StationaryEEGPattern_2022; @matkovicetal_StaticDynamicFMRIderived_2023].
-The purpose of functional connectivity analysis is to (at least partly) quantify these stable neurotransmission-mediated interactions, in order to better understand the nature of computation and communication in large-scale brain networks [@bastosschoffelen_TutorialReviewFunctional_2016; @mostamesadaghiani_PhaseAmplitudecouplingAre_2020]. To this end, several neuroimaging and analysis methods have been developed to study group-averaged and individual whole brain functional networks. In the following sections we will focus on fMRI and EEG, and how the analysis of their oscillatory signals has contributed to our understanding of the human functional connectome.
+The purpose of functional connectivity analysis is to (at least partly) quantify these neurotransmission-mediated interactions, in order to better understand the nature of computation and communication in large-scale brain networks [@bastosschoffelen_TutorialReviewFunctional_2016; @mostamesadaghiani_PhaseAmplitudecouplingAre_2020]. To this end, several neuroimaging and analysis methods have been developed to study group-averaged and individual whole brain functional networks. In the following sections we will focus on fMRI and EEG, and how the analysis of their oscillatory signals has contributed to our understanding of the human functional connectome.
## Measuring human brain oscillations
-Ongoing oscillations---reflecting synchronized rhythmic fluctuations in the excitability of local neuronal populations---are the most prominent feature of all neural signals and can be described by three pieces of information: frequency, amplitude, and phase [@cohen_AnalyzingNeuralTime_2014; @cohen_WhereDoesEEG_2017; @luck_IntroductionEventrelatedPotential_2014]. Frequency is the number of oscillatory cycles per second and is measured in hertz (Hz); it describes the speed or timescale of an oscillation. Amplitude is the peak to trough distance of an oscillation and is measured in arbitrary units (AU) or percent change from baseline for fMRI, and microvolts (mV) for EEG; it generally has an inverse relationship with frequency such that higher frequency oscillations have lower amplitudes, and vice versa. Phase is the timing of an oscillation relative to where it is along its oscillatory cycle and is measured in radians or degrees; it is independent of amplitude, so the neural dynamics reflected in amplitude are distinct from those reflected in phase [@cohen_AnalyzingNeuralTime_2014]. These properties of an oscillation are illustrated in Figure \@ref(fig:oscillation-plot), which depicts two sinusoidal waves with the same amplitude (1 AU) and phase (0 degrees) but different frequencies (1 Hz and 2 Hz) evolving over the course of one second in both Cartesian and polar coordinates.
+Ongoing oscillations---reflecting synchronized rhythmic fluctuations in the excitability of local neuronal populations---are the most prominent feature of all neural signals and can be described by three pieces of information: frequency, amplitude, and phase [@cohen_AnalyzingNeuralTime_2014; @cohen_WhereDoesEEG_2017; @luck_IntroductionEventrelatedPotential_2014]. Frequency is the number of oscillatory cycles per second and is measured in hertz (Hz); it describes the speed or timescale of an oscillation. Amplitude is the peak to trough distance of an oscillation and is typically measured in microvolts (mV) for EEG and percent change from baseline for fMRI, normalized from the arbitrary units (AU) of the raw BOLD signal by some baseline value [e.g., the signal mean\; @liuetal_GlobalSignalFMRI_2017]; it generally has an inverse relationship with frequency such that higher frequency oscillations have lower amplitudes, and vice versa. Phase is the timing of an oscillation relative to where it is along its oscillatory cycle and is measured in radians or degrees; it is independent of amplitude, so the neural dynamics reflected in amplitude are distinct from those reflected in phase [@cohen_AnalyzingNeuralTime_2014]. These properties of an oscillation are illustrated in Figure \@ref(fig:oscillation-plot), which depicts two sinusoidal waves with the same amplitude (1 AU) and phase (0 degrees) but different frequencies (1 Hz and 2 Hz) evolving over the course of one second in both Cartesian and polar coordinates.
```{r oscillation-plot}
#| fig.height: 3.630290
@@ -44,41 +44,37 @@ Ongoing oscillations---reflecting synchronized rhythmic fluctuations in the exci
knitr::include_graphics(here::here("figures", "oscillation-plot.png"))
```
-The spatiotemporal dynamics of oscillatory neural signals [i.e., their moment-to-moment variability\; @garrettetal_MomenttomomentBrainSignal_2013] are fundamentally constrained and supported by the underlying structure and function of the human brain [@bullmoresporns_ComplexBrainNetworks_2009; @buzsaki_RhythmsBrain_2011; @pangetal_GeometricConstraintsHuman_2023; @spornsetal_HumanConnectomeStructural_2005]. The dominant perspective on this relationship between brain structure and function in contemporary neuroscience---with its origins dating back to Cajal’s Neuron Doctrine [@jones_GolgiCajalNeuron_1999], Brodmann’s cytoarchitectonics [@zilles_BrodmannPioneerHuman_2018], and over a century of work on functional segregation and integration in the human brain [@friston_FunctionalEffectiveConnectivity_2011]---posits that the spatiotemporal dynamics of oscillatory neural signals arise from ongoing interactions within and between spatially separate and functionally specialized neuronal populations connected by a topologically complex network of short and long range axonal projections [@cohen_AnalyzingNeuralTime_2014; @cohen_WhereDoesEEG_2017; @bullmoresporns_ComplexBrainNetworks_2009; @hagmannetal_MappingStructuralCore_2008; cf. @pangetal_GeometricConstraintsHuman_2023; @spornsetal_HumanConnectomeStructural_2005; @yuste_NeuronDoctrineNeural_2015]. These interactions occur within one or many oscillatory frequency bands due to neuronal populations throughout the cerebral cortex being capable of generating multiple oscillation frequencies both concurrently and selectively, and responding selectively to inputs at multiple preferred frequencies due to the membrane resonances of individual neurons [@akamkullmann_OscillationsFilteringNetworks_2010; @blankenburgetal_InformationFilteringResonant_2015; @hutcheonyarom_ResonanceOscillationIntrinsic_2000; @kopelletal_AreDifferentRhythms_2010; @whittingtonetal_FutureNeuronalOscillation_2018]. From a biophysics perspective, much is known about the origins of fMRI and EEG signals and their connection to underlying neuronal activity [@buxton_PhysicsFunctionalMagnetic_2013; @buzsaki_RhythmsBrain_2011; @cohen_WhereDoesEEG_2017; @nunezsrinivasan_ElectricFieldsBrain_2006]. Here we briefly review these origins, as well as some of the challenges fMRI and EEG face in the pursuit of studying network variants.
+Neuronal interactions occur within one or many oscillatory frequency bands due to neuronal populations throughout the cerebral cortex being capable of generating multiple oscillation frequencies both concurrently and selectively, and responding selectively to inputs at multiple preferred frequencies due to the membrane resonances of individual neurons [@akamkullmann_OscillationsFilteringNetworks_2010; @blankenburgetal_InformationFilteringResonant_2015; @hutcheonyarom_ResonanceOscillationIntrinsic_2000; @kopelletal_AreDifferentRhythms_2010; @whittingtonetal_FutureNeuronalOscillation_2018]. From a biophysics perspective, much is known about the origins of fMRI and EEG signals and their connection to underlying neuronal activity [@buxton_PhysicsFunctionalMagnetic_2013; @buzsaki_RhythmsBrain_2011; @cohen_WhereDoesEEG_2017; @nunezsrinivasan_ElectricFieldsBrain_2006]. Here we briefly review these origins, as well as some of the challenges fMRI and EEG face in the pursuit of studying network variants.
### fMRI
-fMRI measures low frequency (< 0.1 Hz) oscillations in blood oxygenation level dependent (BOLD) signals caused primarily by decreases in local concentrations of paramagnetic deoxyhemoglobin in response to increases in local excitatory and inhibitory postsynaptic neuronal activity through the active process of neurovascular coupling [@halletal_RelationshipMEGFMRI_2014; @hillman_CouplingMechanismSignificance_2014; @moonetal_ContributionExcitatoryInhibitory_2021]. When local neuronal activity increases, oxygen extraction from the blood increases, resulting in increased amounts of deoxyhemoglobin in the blood; however, within approximately 500 milliseconds and peaking around 3-5 seconds before slowly returning to baseline, neurovascular signals increase blood flow and volume to the responding region, bringing oxygenated blood in sufficient excess to overoxygenate the region and wash out deoxyhemoglobin [@hillman_CouplingMechanismSignificance_2014]. Due to the magnetic properties of deoxyhemoglobin, this net decrease in local concentrations of deoxyhemoglobin causes changes in the magnetic field [i.e., $T_2^*$ relaxation\; @ogawaetal_BrainMagneticResonance_1990] which are reflected as fluctuations in the magnetic resonance signal [@buxton_PhysicsFunctionalMagnetic_2013]. For typical whole brain fMRI studies, a single image of the brain (i.e., one volume) is obtained by sequentially acquiring images of all of the slices that cover the brain with a repetition time of about 3 seconds (i.e., the duration to obtain one volume); BOLD signals are recorded at voxels distributed throughout the brain volume with a voxel size of about 3 mm^3 [@bijsterbosch_IntroductionRestingState_2017; @buxton_PhysicsFunctionalMagnetic_2013]. Thus, fMRI provides a spatially precise---but indirect, slow, and temporally delayed---proxy measure of underlying neuronal activity whose interpretation is intrinsically linked to understanding the physiological and metabolic processes that modulate blood flow in the brain [@drew_NeurovascularCouplingMotive_2022; @halletal_InterpretingBOLDDialogue_2016; @hillman_CouplingMechanismSignificance_2014].
+fMRI measures low frequency (< 0.1 Hz) oscillations in blood oxygenation level dependent (BOLD) signals caused primarily by decreases in local concentrations of paramagnetic deoxyhemoglobin in response to increases in local excitatory and inhibitory postsynaptic neuronal activity through the active process of neurovascular coupling [@halletal_RelationshipMEGFMRI_2014; @hillman_CouplingMechanismSignificance_2014; @moonetal_ContributionExcitatoryInhibitory_2021]. When local neuronal activity increases, oxygen extraction from the blood increases, resulting in increased amounts of deoxyhemoglobin in the blood; however, within approximately 500 milliseconds and peaking around 3-5 seconds before slowly returning to baseline, neurovascular signals increase blood flow and volume to the responding region, bringing oxygenated blood in sufficient excess to overoxygenate the region and wash out deoxyhemoglobin [@hillman_CouplingMechanismSignificance_2014]. Due to the magnetic properties of deoxyhemoglobin, this net decrease in local concentrations of deoxyhemoglobin causes changes in the magnetic field [i.e., $T_2^*$ relaxation\; @ogawaetal_BrainMagneticResonance_1990] which are reflected as fluctuations in the magnetic resonance signal [@buxton_PhysicsFunctionalMagnetic_2013]. For typical whole brain fMRI studies, a single image of the brain (i.e., one volume) is obtained by sequentially acquiring images of all of the slices that cover the brain with a repetition time of about 3 seconds (i.e., the duration to obtain one volume); BOLD signals are recorded at voxels distributed throughout the brain volume, typically with a voxel size of about 3 mm$^3$ [@bijsterbosch_IntroductionRestingState_2017; @buxton_PhysicsFunctionalMagnetic_2013]. Thus, fMRI provides a spatially precise---but indirect, slow, and temporally delayed---proxy measure of underlying neuronal activity whose interpretation is intrinsically linked to understanding the physiological and metabolic processes that modulate blood flow in the brain [@drew_NeurovascularCouplingMotive_2022; @halletal_InterpretingBOLDDialogue_2016; @hillman_CouplingMechanismSignificance_2014].
-An important implication of this complication in interpreting fMRI data, particularly for studies of network variants, is that any individual differences in BOLD signal functional connectivity may reflect differences in neuronal activity, neurovascular coupling, or both [@bijsterbosch_IntroductionRestingState_2017]. Although it is commonly assumed that fMRI is detecting individual differences in neuronal activity, it cannot be ruled out that any individual differences observed with fMRI are the result of non-neuronal factors known to influence BOLD signal functional connectivity, such as the maximum oxygen carrying capacity of the blood [@hillman_CouplingMechanismSignificance_2014; @wardetal_IndividualDifferencesHaemoglobin_2020; @yangetal_ImpactHematocritMeasurements_2014]. This capacity is related to hemoglobin and hematocrit levels in the blood, which are known to vary considerably between individuals [@wardetal_IndividualDifferencesHaemoglobin_2020], and systematically with physiological [@jinetal_RelationshipHematocritLevel_2015], psychological [@pattersonetal_StressinducedHemoconcentrationBlood_1995], pharmacological [@drinka_HematocritElevationAssociated_2013], and demographic factors, such as sex [@rushtonbarth_WhatEvidenceGender_2010] and age [@backmanetal_SeasonTimeDay_2016; @cruickshank_VariationsNormalHaemoglobin_1970]. Failing to account for individual differences in hemoglobin and hematocrit levels can result in spurious estimates of functional connectivity between brain regions, confounding the relationship between brain and behavioural or cognitive measures [@wardetal_IndividualDifferencesHaemoglobin_2020]. To our knowledge, no fMRI network variant studies to date have accounted for this potentially confounding variable, and it remains an open question what other non-neuronal factors (if any) may confound or explain the substantial variability in functional connectome organization that has been found to exist across individuals.
+An important implication of this complication in interpreting fMRI data, particularly for studies of network variants, is that any individual differences in BOLD signal functional connectivity may reflect differences in neuronal activity, neurovascular coupling, or both [@bijsterbosch_IntroductionRestingState_2017]. Although it is commonly assumed that fMRI is detecting individual differences in neuronal activity, it cannot be ruled out that any individual differences observed with fMRI are the result of non-neuronal factors known to influence BOLD signal functional connectivity [e.g., the maximum oxygen carrying capacity of the blood, which is known to vary considerably and systematically between individuals\; @hillman_CouplingMechanismSignificance_2014; @wardetal_IndividualDifferencesHaemoglobin_2020; @yangetal_ImpactHematocritMeasurements_2014].
-Finally, fMRI is a non-portable resource-demanding method, with scanners costing hundreds of thousands to millions of dollars and scans costing hundreds of dollars per hour, which places a basic restriction on who can collect fMRI data---and consequently---who can participate and benefit from fMRI research. For instance, all of the network variant studies cited in this thesis were conducted by researchers from the following high-income countries: the United States of America [@bijsterboschetal_RelationshipSpatialConfiguration_2018; @finnetal_FunctionalConnectomeFingerprinting_2015; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_IndividualspecificFeaturesBrain_2017; @grattonetal_FunctionalBrainNetworks_2018; @kongetal_SpatialTopographyIndividualSpecific_2019; @miranda-dominguezetal_ConnectotypingModelBased_2014; @muelleretal_IndividualVariabilityFunctional_2013; @seitzmanetal_TraitlikeVariantsHuman_2019; @smithetal_BrainHubsDefined_2023], the United Kingdom [@bijsterboschetal_RelationshipSpatialConfiguration_2018], the Netherlands [@bijsterboschetal_RelationshipSpatialConfiguration_2018], Germany [@kongetal_SpatialTopographyIndividualSpecific_2019; @muelleretal_IndividualVariabilityFunctional_2013], and Singapore [@kongetal_SpatialTopographyIndividualSpecific_2019; @muelleretal_IndividualVariabilityFunctional_2013], with the data for all but one study being being collected in the United States. Given that the broad purpose of these studies is to investigate variability in functional connectome organization across individuals---with the ultimate goal of identifying the neural causes of variation in human behaviour, cognition, and their dysfunction---our understanding of these individual differences may be distorted or otherwise undermined by the use of samples that differ in many concrete ways from the broader human population living in low-income and middle-income countries, such as their genetic, environmental, social, economic, political, and demographic diversity [@batteletal_MindBrainGap_2021; @campbelltishkoff_AfricanGeneticDiversity_2008; @falketal_WhatRepresentativeBrain_2013; @henrichetal_WeirdestPeopleWorld_2010; @muthukrishnaetal_WesternEducatedIndustrial_2020]; and prevalence of neurological disease [@parraetal_DementiaLatinAmerica_2018] and neurodevelopmental disorders [@bittaetal_BurdenNeurodevelopmentalDisorders_2017].
+In addition, fMRI is a generally non-portable resource-demanding method, which places a basic restriction on who can collect fMRI data---and consequently---who can participate and benefit from fMRI research. For instance, all of the network variant studies cited in this thesis were conducted by researchers from high-income countries like the United States. Given that the broad purpose of these studies is to investigate variability in functional connectome organization across individuals---with the ultimate goal of identifying the neural causes of variation in human behaviour, cognition, and their dysfunction---our understanding of these individual differences may be distorted or otherwise undermined by the use of samples that differ in many concrete ways from the broader human population living in low-income and middle-income countries, such as their genetic, environmental, social, economic, political, and demographic diversity [@batteletal_MindBrainGap_2021; @campbelltishkoff_AfricanGeneticDiversity_2008; @falketal_WhatRepresentativeBrain_2013; @henrichetal_WeirdestPeopleWorld_2010; @muthukrishnaetal_WesternEducatedIndustrial_2020]; and prevalence of neurological disease [@parraetal_DementiaLatinAmerica_2018] and neurodevelopmental disorders [@bittaetal_BurdenNeurodevelopmentalDisorders_2017].
### EEG
-EEG measures low to high frequency (1-150 Hz) oscillations in extracellular currents caused primarily by summed excitatory and inhibitory dendritic postsynaptic potentials from thousands of cortical pyramidal cells in parallel alignment, whose dipoles are oriented perpendicularly to the cortical surface [@cohen_AnalyzingNeuralTime_2014]. Large-scale synchronous events of this nature create electrical fields powerful enough to conduct instantaneously through the brain, meninges, skull, and scalp, to scalp electrodes that measure fluctuations in these fields in real time [@cohen_AnalyzingNeuralTime_2014; @cohen_WhereDoesEEG_2017; @luck_IntroductionEventrelatedPotential_2014]. Other sources of oscillations such as individual neurons, smaller populations of neurons, large populations of neurons with opposing dipoles oriented tangentially to the cortical surface, or deep brain sources either produce no electrical fields or weak electrical fields that cannot be measured at the scalp [@cohen_AnalyzingNeuralTime_2014]. Furthermore, although oscillations occur at frequencies below and above the ranges stated above [@buzsaki_RhythmsBrain_2011], very slow oscillations (< 0.1 Hz) can be difficult to measure due to limitations of most EEG systems, as can faster oscillations (> 80 Hz) due to their low amplitude making them hard to distinguish from noise [@cohen_AnalyzingNeuralTime_2014]. For typical EEG studies, electrophysiological oscillations are recorded simultaneously at electrodes distributed evenly across the scalp (usually 64, 128, or 256 electrodes) with a sampling rate of about 250-1000 Hz, then further processed into single or multiple frequency bands that are thought to capture distinct oscillations [@buzsaki_RhythmsBrain_2011]. Thus, EEG provides a direct (albeit incomplete) measure of neurotransmission-mediated neuronal activity with a high temporal accuracy and resolution, whose interpretation bypasses the complications of linking BOLD signals to the dynamics of underlying neuronal activity. However, because EEG signals are localized to electrodes on the scalp rather than voxels in the brain, the spatial interpretation of EEG is intrinsically linked to understanding how the volume conduction properties of the human head mediates the relationship between EEG signals measured at the scalp and their underlying neuronal sources [@laietal_ComparisonScalpSourcereconstructed_2018; @nunezsrinivasan_ElectricFieldsBrain_2006; @schoffelengross_SourceConnectivityAnalysis_2009].
+EEG measures oscillations in extracellular currents caused primarily by summed excitatory and inhibitory dendritic postsynaptic potentials from thousands of cortical pyramidal cells in parallel alignment, whose dipoles are oriented perpendicularly to the cortical surface [@cohen_AnalyzingNeuralTime_2014]. Large-scale synchronous events of this nature create electrical fields powerful enough to conduct instantaneously through the brain, meninges, skull, and scalp, to scalp electrodes that measure fluctuations in these fields in real time [@cohen_AnalyzingNeuralTime_2014; @cohen_WhereDoesEEG_2017; @luck_IntroductionEventrelatedPotential_2014]. Other sources of oscillations such as individual neurons, smaller populations of neurons, large populations of neurons with opposing dipoles oriented tangentially to the cortical surface, or deep brain sources either produce no electrical fields or weak electrical fields that cannot be measured at the scalp [@cohen_AnalyzingNeuralTime_2014]. Furthermore, although oscillations occur across a wide frequency range [@buzsaki_RhythmsBrain_2011], very slow oscillations (< 0.1 Hz) can be difficult to measure due to limitations of most EEG systems, as can faster oscillations (> 80 Hz) due to their low amplitude making them hard to distinguish from noise [@cohen_AnalyzingNeuralTime_2014]. For typical EEG studies, electrophysiological oscillations are recorded simultaneously at electrodes distributed evenly across the scalp (usually 64, 128, or 256 electrodes) with sampling rates of $\geq$ 500 Hz, then further processed into single or multiple frequency bands that are thought to capture distinct oscillations within this range [@buzsaki_RhythmsBrain_2011]. Thus, EEG provides a direct (albeit incomplete) measure of neuronal activity with a high temporal resolution, whose interpretation bypasses the complications of linking BOLD signals to the dynamics of underlying neuronal activity. However, because EEG signals are measured using electrodes on the scalp rather than voxels in the brain, the spatial interpretation of EEG is intrinsically linked to understanding how the volume conduction properties of the human head mediates the relationship between EEG signals measured at the scalp and their underlying neuronal sources [@laietal_ComparisonScalpSourcereconstructed_2018; @nunezsrinivasan_ElectricFieldsBrain_2006; @schoffelengross_SourceConnectivityAnalysis_2009].
-Volume conduction simply refers to the effects of measuring electrical fields at a distance from their source. The primary effect of volume conduction in EEG is field spread---wherein the electrical fields generated by a source can spread not only to the nearest electrode, but also to many other electrodes (up to tens of centimetres away) [@cohen_AnalyzingNeuralTime_2014; @nunezsrinivasan_ElectricFieldsBrain_2006; @schaworonkownikulin_SensorSpaceAnalysis_2022; @schoffelengross_SourceConnectivityAnalysis_2009]. This creates two challenges for studying whole brain functional networks with EEG. First, because the locations of electrodes are not trivially related to the locations of their sources, the topography of scalp-level EEG cannot be interpreted in terms of the underlying neuroanatomy [@laietal_ComparisonScalpSourcereconstructed_2018; @mahjooryetal_ConsistencyEEGSource_2017]. For example, occipital alpha sources (8-13 Hz) can account for a very large part of the activity measured by frontal electrodes in simulated and empirical data, with the extent of this effect varying based on the dipole orientation of the source, the amplitude of the oscillations, cortical anatomy, and the choice of the reference electrode [@chellaetal_ImpactReferenceChoice_2016; @cohen_AnalyzingNeuralTime_2014; @haufeetal_CriticalAssessmentConnectivity_2013; @schaworonkownikulin_SensorSpaceAnalysis_2022]. Consequently, functional connectivity between, for example, a frontal and occipital electrode may not reflect functional connectivity a frontal and occipital source. However, by these same physical laws, differences in functional connectivity imply that different distributions of neuronal sources are active in the brain over space and time; thus, although volume conduction precludes a neurophysiological interpretation of the topography of scalp-level EEG, differences in functional connectome organization within and between individuals provide an opaque indication of differences in global network activity [@michelkoenig_EEGMicrostatesTool_2018; @schoffelengross_SourceConnectivityAnalysis_2009]. Second, because the same source can be measured by multiple electrodes simultaneously, there is a potential for estimates of functional connectivity between two electrodes to be confounded by those two electrodes measuring the same source [@cohen_AnalyzingNeuralTime_2014; @laietal_ComparisonScalpSourcereconstructed_2018]. Given that the ultimate purpose of functional connectivity analysis is to identify and quantify interactions between spatially separate neuronal populations, the challenges caused by volume conduction are non-trivial and it is now widely acknowledged that volume conduction should be accounted for in EEG functional connectivity analyses [@bastosschoffelen_TutorialReviewFunctional_2016; @haufeetal_CriticalAssessmentConnectivity_2013; @laietal_ComparisonScalpSourcereconstructed_2018; @nolteetal_IdentifyingTrueBrain_2004; @schoffelengross_SourceConnectivityAnalysis_2009].
+Volume conduction simply refers to the effects of measuring electrical fields at a distance from their source. The primary effect of volume conduction in EEG is field spread---wherein the electrical fields generated by a source can spread not only to the nearest electrode, but also to other electrodes (up to tens of centimetres away) [@cohen_AnalyzingNeuralTime_2014; @nunezsrinivasan_ElectricFieldsBrain_2006; @schaworonkownikulin_SensorSpaceAnalysis_2022; @schoffelengross_SourceConnectivityAnalysis_2009]. This creates two challenges for studying whole brain functional networks with EEG. First, because the locations of electrodes are not trivially related to the locations of their sources, the topography of scalp-level EEG cannot be interpreted in terms of the underlying neuroanatomy [@laietal_ComparisonScalpSourcereconstructed_2018; @mahjooryetal_ConsistencyEEGSource_2017]. For example, occipital alpha sources (8-13 Hz) can account for a very large part of the activity measured by frontal electrodes in simulated and empirical data, with the extent of this effect varying based on the dipole orientation of the source, the amplitude of the oscillations, cortical anatomy, and the choice of the reference electrode [@chellaetal_ImpactReferenceChoice_2016; @cohen_AnalyzingNeuralTime_2014; @haufeetal_CriticalAssessmentConnectivity_2013; @schaworonkownikulin_SensorSpaceAnalysis_2022]. Consequently, functional connectivity between, for example, a frontal and occipital electrode may not reflect functional connectivity a frontal and occipital source. However, by these same physical laws, differences in functional connectivity imply that different distributions of neuronal sources are active in the brain over space and time; thus, although volume conduction precludes a neurophysiological interpretation of the topography of scalp-level EEG, differences in functional connectome organization within and between individuals provide an opaque indication of differences in global network activity [@michelkoenig_EEGMicrostatesTool_2018; @schoffelengross_SourceConnectivityAnalysis_2009]. Second, because the same source can be measured by multiple electrodes simultaneously, there is a potential for estimates of functional connectivity between two electrodes to be confounded by those two electrodes measuring the same source [@cohen_AnalyzingNeuralTime_2014; @laietal_ComparisonScalpSourcereconstructed_2018]. Given that the ultimate purpose of functional connectivity analysis is to identify and quantify interactions between spatially separate neuronal populations, the challenges caused by volume conduction are non-trivial and it is now widely acknowledged that volume conduction should be accounted for in EEG functional connectivity analyses [@bastosschoffelen_TutorialReviewFunctional_2016; @haufeetal_CriticalAssessmentConnectivity_2013; @laietal_ComparisonScalpSourcereconstructed_2018; @nolteetal_IdentifyingTrueBrain_2004; @schoffelengross_SourceConnectivityAnalysis_2009].
-To date, two main approaches have been employed throughout the literature to address the challenges caused by volume conduction. The first approach is to use inverse source reconstruction methods, which attempt to unmix the activity measured across all electrodes to estimate the location of the underlying sources [@michelbrunet_EEGSourceImaging_2019]. Inverse source reconstruction methods primarily address the first challenge for studying studying whole brain functional networks with EEG---by estimating the location of the underlying sources, signals are localized to dipoles in the brain, making it possible to interpret EEG in terms of the underlying neuroanatomy. However, artifacts of volume conduction persist in all source estimates (where they are often referred to as source or signal leakage), thus source-reconstructed EEG can still contain spurious estimates of functional connectivity [@bastosschoffelen_TutorialReviewFunctional_2016; @haufeetal_CriticalAssessmentConnectivity_2013; @schoffelengross_SourceConnectivityAnalysis_2009]. Additionally, EEG electrode density is known to non-trivially influence the spatial fidelity of the source localization, as well as the consistency of subsequent functional connectivity estimates, thus it is generally recommended that a minimum of 64 electrodes (but ideally 128 or 256) be used when source localization is important [@hatlestad-halletal_ReliableEvaluationFunctional_2023; @michelbrunet_EEGSourceImaging_2019; @seecketal_StandardizedEEGElectrode_2017].
+To date, two main approaches have been employed throughout the literature to address the challenges caused by volume conduction. The first approach is to use inverse source reconstruction methods, which attempt to unmix the activity measured across all electrodes to estimate the location of the underlying sources [@michelbrunet_EEGSourceImaging_2019]. Source localization is an ill-posed problem (i.e., it has an infinite number of solutions) that works by first modelling the volume conduction properties of the human head to determine the potential at each scalp electrode that would be generated by hypothetical dipoles in the brain (the forward model); then---taking into consideration the forward model and electrode noise---using an inverse source reconstruction algorithm (the source model) to estimate the dipole parameters that best explain the observed scalp potential measurements, based on constraints and assumptions imposed by the source model about the underlying sources [@grechetal_ReviewSolvingInverse_2008; @michelbrunet_EEGSourceImaging_2019]. The spatial fidelity and accuracy of source localization and subsequent functional connectivity estimates is affected by several factors, including the accuracy of the forward model [e.g., @akalinacarmakeig_EffectsForwardModel_2013; @liuetal_ComparisonEEGSource_2023; @nielsenetal_EvaluatingInfluenceAnatomical_2023; @tabernaetal_DetectionRestingStateFunctional_2021], the chosen source model [e.g., @hatlestad-halletal_ReliableEvaluationFunctional_2023; @haufeetal_CriticalAssessmentConnectivity_2013; @liuetal_DetectingLargeScaleBrain_2018; @mahjooryetal_ConsistencyEEGSource_2017], instrumental or biological noise [e.g., @ryynanenetal_EffectElectrodeDensity_2004; @ryynanenetal_EffectMeasurementNoise_2006; @whittingstalletal_EffectsDipolePosition_2003], and electrode density [e.g., @hatlestad-halletal_ReliableEvaluationFunctional_2023; @michelbrunet_EEGSourceImaging_2019; @seecketal_StandardizedEEGElectrode_2017]; because of these factors, the validity of source space functional connectivity estimates may be in question in the absence of other sources of convergent evidence (e.g., fMRI, animal models, etc.). Inverse source reconstruction methods primarily address the first challenge for studying whole brain functional networks with EEG---by estimating the location of the underlying sources, signals are localized to dipoles in the brain, making it possible to interpret EEG in terms of the underlying neuroanatomy. However, artifacts of volume conduction persist in all source estimates (where they are often referred to as source or signal leakage), thus source-reconstructed EEG can still contain spurious estimates of functional connectivity [@bastosschoffelen_TutorialReviewFunctional_2016; @haufeetal_CriticalAssessmentConnectivity_2013; @schoffelengross_SourceConnectivityAnalysis_2009].
-Two main steps are involved in inverse source reconstruction: solving the forward problem and solving the inverse problem [@michelbrunet_EEGSourceImaging_2019]. Solving the forward problem involves modelling the volume conduction properties of the human head (i.e., the forward model) in order to determine the potential at each scalp electrode that would be generated by a known source in the brain. To prevent errors in source reconstruction and subsequent functional connectivity estimates, a realistic forward model must be created that incorporates (1) the shape of the head; (2) the conductivity parameters of the different tissues between the sources in the brain and the potential at each scalp electrode (e.g., the scalp, skull, cerebrospinal fluid, grey matter, etc.); and (3) the exact position of each electrode on the individual's head during the recording [@akalinacarmakeig_EffectsForwardModel_2013; @cespedes-villaretal_InfluencePatientSpecificHead_2020; @chauveauetal_EffectsSkullThickness_2004; @dalaletal_ConsequencesEEGElectrode_2014; @hallezetal_ReviewSolvingForward_2007; @hongetal_HowReducingModel_2011; @liuetal_ComparisonEEGSource_2023; @liuetal_DetectingLargeScaleBrain_2018; @mccannbeltrachini_ImpactSkullSutures_2022; @michelbrunet_EEGSourceImaging_2019; @nielsenetal_EvaluatingInfluenceAnatomical_2023; @tabernaetal_DetectionRestingStateFunctional_2021]; individual anatomical information can be derived from MRI, and electrode positions can be measured using a 3D digitizer, photogrammetry system, or an MRI scanner (in the case of simultaneous fMRI-EEG studies), or otherwise checked using photographs taken during the recording [@michelbrunet_EEGSourceImaging_2019].
+
-Solving the inverse problem involves estimating the source(s) that generated a given EEG scalp potential measurement using an inverse source reconstruction algorithm (i.e., the source model) to determine the dipole parameters that best explain (based on some minimization criterion) the observed scalp potential measurements, taking into consideration the forward model and electrode noise [@michelbrunet_EEGSourceImaging_2019]. The inverse problem is ill-posed, meaning it has an infinite number of solutions, and constraints based on prior information about the desired source characteristics or physiological assumptions have to be incorporated to derive a unique estimate about the dipole parameters that lead to a certain observed scalp potential measurement [@abreuetal_OptimizingEEGSource_2022; @michelbrunet_EEGSourceImaging_2019]. If the constraints imposed by a source model are wrong, the separation of electrode activity into source activity will be wrong as well, potentially compromising the neuroanatomical reliability and validity of any subsequent functional connectivity analyses [@liuetal_DetectingLargeScaleBrain_2018; @mahjooryetal_ConsistencyEEGSource_2017; @nguyen-danseetal_FeasibilityReconstructingSource_2021; @nolteetal_IdentifyingTrueBrain_2004]. Moreover, even if different source models localize activity to similar brain regions, functional connectivity between these regions may be substantially different simply due to the choice of source model [@hatlestad-halletal_ReliableEvaluationFunctional_2023; @haufeetal_CriticalAssessmentConnectivity_2013; @mahjooryetal_ConsistencyEEGSource_2017]. Given that more than 42 inverse source reconstruction algorithms have been proposed to date [@asadzadehetal_SystematicReviewEEG_2020], with the selection of a source model largely coming down to researcher preference [@mahjooryetal_ConsistencyEEGSource_2017], the validity of source space functional connectivity estimates may be in question in the absence of other sources of convergent evidence (e.g., fMRI, animal models, etc.).
+The second approach to addressing the challenges caused by volume conduction is to use functional connectivity metrics that are unlikely to be explained by common sources and are thus robust to volume conduction [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. A variety of robust functional connectivity metrics have been developed to accomplish this [@bastosschoffelen_TutorialReviewFunctional_2016; @hippetal_LargescaleCorticalCorrelation_2012], all of which operate under the same basic principle: Because volume conduction from a common source to multiple electrodes is instantaneous, electrodes measuring a common source will have their signals phase locked with a time lag of zero; conversely, phase locking with a nonzero time lag cannot be caused by volume conduction from a common source, so signals with a consistent nonzero phase difference are likely to have been generated by separate sources [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. Functional connectivity between such signals can, therefore, be interpreted in terms of true interactions between the underlying neuronal sources [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. However, it is important to note that true zero lag interactions also occur in neuronal networks [@golloetal_MechanismsZeroLagSynchronization_2014], and it is likely that this approach misses parts of the brain's interactions, leading to the underestimation of connectivity strength between nodes [@cohen_EffectsTimeLag_2015; @nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. Thus, weak or absent functional connectivity with these metrics could either mean there truly is no interaction, or that the interaction between two sources is not consistently delayed such that one of the sources regularly leads or lags the other one [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007].
-Source-reconstructed EEG is generally a resource-demanding method, requiring the same resources as fMRI in addition to an EEG or simultaneous fMRI-EEG system; however, it is possible to reduce resource demands by using a template MRI of an average human head rather than individual MRI scans for the forward model [@michelbrunet_EEGSourceImaging_2019]. Whether or not it is appropriate to use a template MRI for source-reconstructed EEG network variant studies is an open question. While the advantages of using individual MRI scans for the forward model are obvious---and there is sufficient evidence that using a template MRI results in less precise source localization [@brodbecketal_ElectroencephalographicSourceImaging_2011; @guggisbergetal_LocalizationCorticoperipheralCoherence_2011]---no study to date has investigated how the variability of functional connectome organization changes between and within individuals when using individual or template MRIs. Rather than speculate, here we simply note that in cases where a template MRI is used (or where analyses are done in sensor space rather than source space), EEG is a portable and less resource-demanding method relative to fMRI, with research-grade systems typically costing anywhere from around \$30,000 to \$100,000 depending on the configuration, negligible recording costs, and shorter recording times (due to the high sampling rate), making it a potentially more accessible method to a broader population of researchers and research participants [@ledwidgeetal_RecommendationsDevelopingEEG_2018], including those from lower-income and middle-income countries [@bhavnanietal_AcceptabilityFeasibilityUtility_2022; cf. @nguyen-danseetal_FeasibilityReconstructingSource_2021].
-
-The second approach to addressing the challenges caused by volume conduction is to use functional connectivity metrics that are unlikely to be explained by common sources, and are thus robust to volume conduction [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. A variety of robust functional connectivity metrics have been developed to accomplish this [@bastosschoffelen_TutorialReviewFunctional_2016; @hippetal_LargescaleCorticalCorrelation_2012], all of which operate under the same basic principle: Because volume conduction from a common source to multiple electrodes is instantaneous, electrodes measuring a common source will have their signals phase locked with a time lag of zero; conversely, phase locking with a nonzero time lag cannot be caused by volume conduction from a common source, so signals with a consistent nonzero phase difference are likely to have been generated by separate sources [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. Functional connectivity between such signals can, therefore, be interpreted in terms of true interactions between the underlying neuronal sources [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. However, it is important to note that true zero lag interactions also occur in the brain [@golloetal_MechanismsZeroLagSynchronization_2014], and it is likely that this approach misses parts---or in the worst case all---of the brain's interactions, leading to the underestimation of connectivity strength between nodes [@cohen_EffectsTimeLag_2015; @nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007]. Thus, weak or absent functional connectivity with these metrics could either mean there truly is no interaction, or that the interaction between two sources is not consistently delayed such that one of the sources regularly leads or lags the other one [@nolteetal_IdentifyingTrueBrain_2004; @stametal_PhaseLagIndex_2007].
-
-Finally, because EEG measures oscillations across a broad range of frequencies, and because neuronal populations are capable of generating and responding to multiple oscillation frequencies, it is generally necessary to further process EEG signals into single or multiple frequency bands using band-pass filtering in order to investigate oscillations occurring at different timescales [@sadaghianiwirsich_IntrinsicConnectomeOrganization_2020]. Due to the limitations of EEG mentioned above, EEG is typically limited to investigating the so-called canonical frequency bands (i.e., Delta, 1-4 Hz; Theta, 4-8 Hz; Alpha, 8-13 Hz; Beta, 13-30 Hz; Gamma, 30-80 Hz). Furthermore, because of EEG's susceptibility to 60 Hz electrical line noise, the upper bound of the Gamma band is often reduced to a frequency below 60 Hz where no artifacts from line noise are present.
+Finally, because EEG measures oscillations across a broad range of frequencies, and because neuronal populations are capable of generating and responding to multiple oscillation frequencies, it is generally necessary to further process EEG signals into single or multiple frequency bands using band-pass filtering in order to investigate oscillations occurring at different timescales [@sadaghianiwirsich_IntrinsicConnectomeOrganization_2020]. EEG is typically limited to investigating canonical frequency bands (i.e., Delta, 1-4 Hz; Theta, 4-8 Hz; Alpha, 8-13 Hz; Beta, 13-30 Hz; Gamma, 30-80 Hz). Furthermore, because of EEG's susceptibility to 60 Hz electrical line noise, the upper bound of the Gamma band is often reduced to a frequency below 60 Hz where artifacts from line noise are less prominent.
## Revealing the intrinsic functional organization of the human brain
-As the previous section established, EEG and fMRI signals reflect (more and less directly) synchronized rhythmic fluctuations in the excitability of local neuronal populations, the timing of which is encoded by the phase of the signal and the magnitude of which by the amplitude. These fluctuations vary in frequency, amplitude, and phase over time, reflecting the dynamics of neuronal activity caused by ongoing interactions within and between anatomically connected neuronal populations distributed throughout the cerebral cortex. As the dynamics of ongoing activity unfold upon this structural network, spatially distributed neuronal populations dynamically and recurrently couple to one another, forming creating a large and variable repertoire of stable intrinsic network states when integrated over longer time periods [@arzate-menaetal_StationaryEEGPattern_2022; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @honeyetal_NetworkStructureCerebral_2007; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @sporns_ComplexBrainConnectivity_2022].
+As the previous section established, EEG and fMRI signals reflect synchronized rhythmic fluctuations in the excitability of local neuronal populations, the timing of which is encoded by the phase of the signal and the magnitude of which by the amplitude. These fluctuations vary in frequency, amplitude, and phase over time, reflecting the dynamics of neuronal activity caused by ongoing interactions within and between anatomically connected neuronal populations distributed throughout the cortex. As the dynamics of ongoing activity unfold upon this structural network, spatially distributed neuronal populations dynamically and recurrently couple to one another, forming creating a large and variable repertoire of stable intrinsic network states when integrated over longer time periods [@danielarzate-menaetal_StationaryEEGPattern_2022; @decocorbetta_DynamicalBalanceBrain_2011; @garrettetal_MomenttomomentBrainSignal_2013; @honeyetal_NetworkStructureCerebral_2007; @hutchisonetal_DynamicFunctionalConnectivity_2013; @sadaghianiwirsich_IntrinsicConnectomeOrganization_2020; @sporns_ComplexBrainConnectivity_2022].
Functional connectivity analysis provides a useful framework for describing the coupling that occurs in neuronal networks and is based on the following assumption: If two nodes in a functional connectome have a (strong, consistent) statistical dependency between their signals over time, they are both likely to be involved in the same brain function(s), and thus functionally connected [@bijsterbosch_IntroductionRestingState_2017; @engeletal_IntrinsicCouplingModes_2013]. Because both EEG and fMRI signals are most strongly related to synchronized postsynaptic activity and not to neuronal firing rates, it is important to keep in mind that the coupling described by functional connectivity analysis is based on the inputs to local neuronal populations rather than their outputs [@bijsterbosch_IntroductionRestingState_2017]. Moreover, we limit our discussion here to same-frequency undirected functional connectivity analysis, which is currently the primary means of estimating functional connectivity used throughout the literature as well as this thesis. For discussions of cross-frequency coupling and directed functional connectivity analysis, we direct the reader to reviews by @canoltyknight_FunctionalRoleCrossfrequency_2010 and @bastosschoffelen_TutorialReviewFunctional_2016, respectively.
-A standard acquisition method for functional connectivity analysis in the context of individual differences research is the resting state paradigm, which involves a passive state wherein participants are simply required to sit or lie still with their eyes either closed or open and fixated on a cross (while blinking normally) for the duration of the recording, without being instructed to think of anything in particular [@smithetal_FunctionalConnectomicsRestingstate_2013]. The primary motivation behind this paradigm is that it is a convenient, relatively neutral, method for measuring the intrinsic activity that accounts for the majority of the brain's energy demands [@raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015] that is: easy to acquire and standardize across sites and populations, including those that may not be able to perform more demanding cognitive or behavioural tasks during acquisition [@bijsterbosch_IntroductionRestingState_2017; @foxgreicius_ClinicalApplicationsResting_2010]; and less vulnerable to confounds related to more demanding cognitive or behavioural tasks such as performance, motivation, strategy, practice, or repetition effects, making it suitable for longitudinal designs where stability over time is a point of interest [@finnetal_CanBrainState_2017; @foxgreicius_ClinicalApplicationsResting_2010]. Although the behavioural and cognitive demands of eyes closed and eyes open resting state are similar, each brain state is qualitatively distinct---with associated changes (on average) in the global power and topography of all oscillatory bands from eyes closed to eyes open resting state, the most prominent of these being a widespread reduction in alpha band oscillations [@barryetal_EEGDifferencesEyesclosed_2007; @barryetal_EEGDifferencesChildren_2009; @barrydeblasio_EEGDifferencesEyesclosed_2017].
+A common acquisition method for functional connectivity analysis in the context of individual differences research is the resting state paradigm, which involves a passive state wherein participants are simply required to sit or lie still with their eyes either closed or open and fixated on a cross (while blinking normally) for the duration of the recording, without being instructed to think of anything in particular [@grattonetal_DefiningIndividualSpecificFunctional_2020; @smithetal_FunctionalConnectomicsRestingstate_2013]. The primary motivation behind this paradigm is that it is a robust method for measuring the intrinsic activity that accounts for the majority of the brain's energy demands [@raichle_TwoViewsBrain_2010; @raichle_RestlessBrainHow_2015] that is: easy to acquire and standardize across sites and populations, including those that may not be able to perform more demanding cognitive or behavioural tasks during acquisition [@bijsterbosch_IntroductionRestingState_2017; @foxgreicius_ClinicalApplicationsResting_2010]; and less vulnerable to confounds related to more demanding cognitive or behavioural tasks such as performance, motivation, strategy, practice, or repetition effects, making it suitable for longitudinal designs where stability over time is a point of interest [@finnetal_CanBrainState_2017; @foxgreicius_ClinicalApplicationsResting_2010]. Although the behavioural and cognitive demands of eyes closed and eyes open resting state are similar, each brain state is qualitatively distinct---with associated changes (on average) in the global power and topography of all oscillatory bands from eyes closed to eyes open resting state, the most prominent of these being a widespread reduction in alpha band oscillations [@barryetal_EEGDifferencesEyesclosed_2007; @barryetal_EEGDifferencesChildren_2009; @barrydeblasio_EEGDifferencesEyesclosed_2017].
-As we noted in the previous section, group-averaged statistical approaches to studying the human functional connectome have traditionally proceeded by either performing functional connectivity analysis on the concatenated recordings of all participants, or by averaging functional connectivity estimates across participants, in order to make inferences about abstract models of the average human brain [e.g., @poweretal_FunctionalNetworkOrganization_2011; @yeoetal_OrganizationHumanCerebral_2011]. By definition, these approaches cannot represent the types of connectional, spatial, and topological variation found in individual brains as averaging across individuals obscures any existing interindividual variability, leaving only clues of what organizational aspects may be common across individuals [@gordonetal_PrecisionFunctionalMapping_2017; @gordonnelson_ThreeTypesIndividual_2021; @grattonetal_FunctionalBrainNetworks_2018; @laumannetal_FunctionalSystemAreal_2015; @speelmanmcgann_HowMeanMean_2013; @vanhornetal_IndividualVariabilityBrain_2008]. Moreover, in response to published recommendations, these approaches typically collect only a small amount of data per participant in terms of recording duration (e.g., 5-10 minutes), as this amount of data is generally sufficient to obtain reliable results when averaging across participants [@shehzadetal_RestingBrainUnconstrained_2009; @vandijketal_IntrinsicFunctionalConnectivity_2010]; however, doing so precludes the possibility of reliably characterizing whole brain functional network organization at an individual level within these samples due to the relatively low temporal signal-to-noise ratio of fMRI data [@andersonetal_ReproducibilitySingleSubjectFunctional_2011; @gordonetal_PrecisionFunctionalMapping_2017; @laumannetal_FunctionalSystemAreal_2015; @xuetal_AssessingVariationsAreal_2016].
+As we noted in the previous section, group-averaged statistical approaches to studying the human functional connectome have traditionally proceeded by either performing functional connectivity analysis on the concatenated recordings of all participants, or by averaging functional connectivity estimates across participants [e.g., @poweretal_FunctionalNetworkOrganization_2011; @yeoetal_OrganizationHumanCerebral_2011]. Moreover, in response to published recommendations, these approaches typically collect only a small amount of data per participant in terms of recording duration (e.g., 5-10 minutes), as this amount of data is generally sufficient to obtain reliable results when averaging across participants [@shehzadetal_RestingBrainUnconstrained_2009; @vandijketal_IntrinsicFunctionalConnectivity_2010]; however, doing so precludes the possibility of reliably characterizing whole brain functional network organization at an individual level due to the relatively low temporal signal-to-noise ratio of fMRI data [@andersonetal_ReproducibilitySingleSubjectFunctional_2011; @gordonetal_PrecisionFunctionalMapping_2017; @laumannetal_FunctionalSystemAreal_2015; @xuetal_AssessingVariationsAreal_2016].
-In contrast, capturing individual whole brain functional networks requires the opposite approach---wherein a sufficient amount of data is collected to obtain reliable functional connectivity estimates for each participant, and where functional connectivity analysis and other measures of interest (e.g., graph theoretical measures) are performed at an individual level prior to making any comparisons between individuals in order to preserve interindividual variability [@elliottetal_GeneralFunctionalConnectivity_2019; @gordonetal_PrecisionFunctionalMapping_2017; @laumannetal_FunctionalSystemAreal_2015]. The exact quantity of data needed for reliable estimation has been found to vary depending on the measure of interest and across different sampling methods. For example, @gordonetal_PrecisionFunctionalMapping_2017 found that, on average, a minimum of 30 minutes of eyes open resting state data (retained after motion correction) was required to achieve reliable functional connectivity estimates across participants ($r > .85$); whereas reliable network assignment using community detection algorithms required a minimum of 90 minutes (Dice coefficient $> .75$), and other graph theoretic measures (participation coefficient, global efficiency, and modularity) required a minimum of anywhere from 10 to 80 minutes. Conversely, with less than 10 minutes of resting state data all measures yielded low reliability estimates, as well as systematic bias for graph theoretical measures.
+In contrast, capturing individual whole brain functional networks requires functional connectivity analysis and other measures of interest (e.g., graph theoretical measures) to be performed at an individual level prior to making any comparisons between individuals in order to preserve interindividual variability [@elliottetal_GeneralFunctionalConnectivity_2019; @gordonetal_PrecisionFunctionalMapping_2017; @laumannetal_FunctionalSystemAreal_2015]. The exact quantity of data needed for reliable individual-level estimation has been found to vary depending on the measure of interest and across different sampling methods [@andersonetal_ReproducibilitySingleSubjectFunctional_2011; @elliottetal_GeneralFunctionalConnectivity_2019; @gordonetal_PrecisionFunctionalMapping_2017; @hackeretal_RestingStateNetwork_2013; @laumannetal_FunctionalSystemAreal_2015; @nobleetal_InfluencesTestRetest_2017]. For example, @gordonetal_PrecisionFunctionalMapping_2017 found that, on average, a minimum of 30 minutes of eyes open resting state data (retained after motion correction) was required to achieve reliable functional connectivity estimates across participants ($r > .85$); whereas reliable network assignment using community detection algorithms required a minimum of 90 minutes (Dice coefficient $> .75$), and other graph theoretic measures (participation coefficient, global efficiency, and modularity) required a minimum of anywhere from 10 to 80 minutes. With less than 10 minutes of resting state data all measures yielded low reliability estimates, as well as systematic bias for graph theoretical measures. Additionally, @elliottetal_GeneralFunctionalConnectivity_2019, @laumannetal_FunctionalSystemAreal_2015, and @nobleetal_InfluencesTestRetest_2017 each found that similar or greater reliability could be obtained by acquiring shorter recordings over more sessions compared to longer recordings over fewer sessions (e.g., 15 minutes from two sessions compared to 30 minutes from one session) when combining recordings into a single data set.
-Similar results have been reported by @hackeretal_RestingStateNetwork_2013, who found that, on average, the root mean square error in node assignment to known resting state networks on the basis of connectivity strength (using a multi-layer perceptron) decreased as a function of recording duration, up to the limits of the data at 50 minutes---although the error remained substantial, asymptoting at ~15%; @andersonetal_ReproducibilitySingleSubjectFunctional_2011, who found that the mean difference in both intrasession and intersession edge-wise connectivity strength between 5 minute eyes open resting state scans from a single participant decreased as a function of the number of averaged scans, up to a maximum of 5 scans per intrasession group (i.e., 25 minutes of data per group) and 10 scans per intersession group (i.e., 50 minutes of data per group), where the mean difference in connectivity strength was similarly small in both cases ($\Delta r \approx .1$); @elliottetal_GeneralFunctionalConnectivity_2019, who found that, on average, the edge-wise reliability of eyes open resting state functional connectivity estimates increased as a function of recording duration, up to the limits of the data at 40 minutes where reliability was approaching good (mean ICC = $.54$, 95% CI [$.54$, $.54$]; 26% of edges poor, 32% moderate, 25% good, and 18% excellent); @nobleetal_InfluencesTestRetest_2017, who found that, on average, the edge-wise reliability of eyes open resting state functional connectivity estimates increased as a function of recording duration, up to the limits of the data at 144 minutes where reliability was good (mean ICC = $.65$); and @laumannetal_FunctionalSystemAreal_2015, who found that the split-half reliability of eyes closed resting state functional connectome similarity for a single participant increased as a function of recording duration, beginning to converge around 90 minutes ($r = .97$) before reaching its asymptote at 380 minutes ($r = .99$). For each of these studies, short recording times (e.g., < 10 minutes) were also associated with low reliability estimates. Additionally, @elliottetal_GeneralFunctionalConnectivity_2019, @laumannetal_FunctionalSystemAreal_2015, and @nobleetal_InfluencesTestRetest_2017 each found that similar or greater reliability could be obtained by acquiring shorter recordings over more sessions compared to longer recordings over less sessions (e.g., 15 minutes from two sessions compared to 30 minutes from one session) when combining recordings into a single data set.
-
-Together, these results emphasize the importance of collecting a sufficient amount of data per participant in order to counteract the sampling variability of the fMRI BOLD signal and obtain reliable estimates at an individual level [@andersonetal_ReproducibilitySingleSubjectFunctional_2011; @elliottetal_GeneralFunctionalConnectivity_2019; @gordonetal_PrecisionFunctionalMapping_2017; @hackeretal_RestingStateNetwork_2013; @laumannetal_FunctionalSystemAreal_2015; @nobleetal_InfluencesTestRetest_2017]. Additionally, care is needed when collecting and preprocessing this data, as non-neural artifacts such as motion [@poweretal_SpuriousSystematicCorrelations_2012; @poweretal_MethodsDetectCharacterize_2014; @poweretal_CriticalEventRelatedAppraisal_2020; @satterthwaiteetal_ImpactInscannerHead_2012; @vandijketal_InfluenceHeadMotion_2012], respiration [@birnetal_RespirationResponseFunction_2008; @changglover_RelationshipRespirationEndtidal_2009], and signal loss due to acquisition parameters, head shape or head position [@nobleetal_InfluencesTestRetest_2017] can also induce unintentional interindividual variability in functional connectivity [@uddinetal_ControversiesProgressStandardization_2023].
+Together, these results emphasize the importance of collecting sufficient data per participant to counteract the sampling variability of the fMRI BOLD signal and obtain reliable estimates at an individual level [@andersonetal_ReproducibilitySingleSubjectFunctional_2011; @elliottetal_GeneralFunctionalConnectivity_2019; @gordonetal_PrecisionFunctionalMapping_2017; @hackeretal_RestingStateNetwork_2013; @laumannetal_FunctionalSystemAreal_2015; @nobleetal_InfluencesTestRetest_2017]. Additionally, care is needed when collecting and preprocessing these data, as non-neural artifacts such as motion [@poweretal_SpuriousSystematicCorrelations_2012; @poweretal_MethodsDetectCharacterize_2014; @poweretal_CriticalEventRelatedAppraisal_2020; @satterthwaiteetal_ImpactInscannerHead_2012; @vandijketal_InfluenceHeadMotion_2012], respiration [@birnetal_RespirationResponseFunction_2008; @changglover_RelationshipRespirationEndtidal_2009], and signal loss due to acquisition parameters, head shape or head position [@nobleetal_InfluencesTestRetest_2017] can also induce unintentional interindividual variability in functional connectivity [@uddinetal_ControversiesProgressStandardization_2023].
When such confounds are adequately addressed, it becomes evident that at least three different forms of reliable and substantial interindividual variability are present in individual whole brain functional networks: connectivity strength, the size and position of network nodes, and network topography [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_UniversalTaxonomyMacroscale_2019; @uddinetal_ControversiesProgressStandardization_2023]. Connectivity strength---the magnitude of BOLD signal coupling between nodes---is the most commonly studied form of interindividual variability [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023]. Interindividual variability in connectivity strength has been found to be largely stable over time and across different tasks, suggesting that the majority of whole brain functional network organization arises from stable factors involved in individualistic developmental trajectories (e.g., genetic, environmental, and psychological differences; developmental histories; etc.), rather than more transient factors (e.g., ongoing cognition, day-to-day fluctuations, etc.) [@finnetal_CanBrainState_2017; @grattonetal_FunctionalBrainNetworks_2018; @krausetal_NetworkVariantsAre_2021].[^4] The relative magnitude of these differences has been found to be large enough that functional connectome similarities are consistently greater within than between individuals [@gordonetal_PrecisionFunctionalMapping_2017; @grattonetal_FunctionalBrainNetworks_2018] to such an extent that (1) functional connectomes from the same individual can be accurately matched when comparing a given individual's connectome against all other connectomes in a sample across scan conditions [@finnetal_FunctionalConnectomeFingerprinting_2015; @finnetal_CanBrainState_2017], and over months, years, and the lifespan [@jalbrzikowskietal_FunctionalConnectomeFingerprinting_2020; @horienetal_IndividualFunctionalConnectome_2019; @st-ongeetal_FunctionalConnectomeFingerprinting_2023]; and (2) the functional network affiliation of a given network node can vary across individuals, even when that node is spatially consistent across individuals [@gordonetal_PrecisionFunctionalMapping_2017; @gordonnelson_ThreeTypesIndividual_2021].
-Spatial variability in the size and position of network nodes represents a second form of interindividual variability [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023]. It is well-established that cortical areas vary in their size, shape, and location across individuals, even after precise surface-based anatomical alignment [@frostgoebel_MeasuringStructuralFunctional_2012; @vanessenetal_ParcellationsHemisphericAsymmetries_2012]; thus, it follows that this spatial variability would also be present in the organization of functional networks across individuals [@gordonnelson_ThreeTypesIndividual_2021]. Indeed, such spatial variability has been repeatedly found throughout the cortex [@gordonetal_IndividualVariabilitySystemLevel_2017; @harrisonetal_LargescaleProbabilisticFunctional_2015; @kongetal_SpatialTopographyIndividualSpecific_2019; @lietal_PerformingGrouplevelFunctional_2019; @wangetal_ParcellatingCorticalFunctional_2015], taking the form of areal expansions, contractions, or displacements of network nodes that lead to variation in (1) the exact positions of functional network borders across individuals; and (2) the functional network affiliation of a given network node across individuals [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023]. The relative magnitude of this variability has been found to be large enough that many details of functional network organization are lost when using group-averaged approaches (e.g., the fractionation of larger networks into parallel distributed subnetworks), such that these features can can only be identified at an individual level [@bragaetal_ParallelDistributedNetworks_2019; @bragabuckner_ParallelInterdigitatedDistributed_2017; @dinicolaetal_ParallelDistributedNetworks_2020; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_DefaultmodeNetworkStreams_2020; @gordonnelson_ThreeTypesIndividual_2021]. Additionally, at an individual level, areal expansions and contractions appear to be interdependent within and between functional networks, such that (1) nodes of the same functional network tend to expand or contract together; and (2) the relative expansion of a given functional network tends to reduce the amount of cortex available to other adjacent, connected functional networks [@gordonetal_IndividualVariabilitySystemLevel_2017].
+Spatial variability in the size and position of network nodes represents a second form of interindividual variability [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023]. It is well-established that cortical areas vary in their size, shape, and location across individuals, even after precise surface-based anatomical alignment [@frostgoebel_MeasuringStructuralFunctional_2012; @vanessenetal_ParcellationsHemisphericAsymmetries_2012]; thus, it follows that this spatial variability would also be present in the organization of functional networks across individuals [@gordonnelson_ThreeTypesIndividual_2021]. Indeed, such spatial variability has been repeatedly found throughout the cortex [@gordonetal_IndividualVariabilitySystemLevel_2017; @harrisonetal_LargescaleProbabilisticFunctional_2015; @kongetal_SpatialTopographyIndividualSpecific_2019; @lietal_PerformingGrouplevelFunctional_2019; @wangetal_ParcellatingCorticalFunctional_2015], taking the form of areal expansions, contractions, or displacements of network nodes that lead to variation in (1) the exact positions of functional network borders across individuals; and (2) the functional network affiliation of a given network node across individuals [@gordonnelson_ThreeTypesIndividual_2021; @uddinetal_ControversiesProgressStandardization_2023]. The relative magnitude of this variability has been found to be large enough that many details of functional network organization are lost when using group-averaged approaches (e.g., the fractionation of larger networks into parallel distributed subnetworks), such that these features can only be identified at an individual level [@bragaetal_ParallelDistributedNetworks_2019; @bragabuckner_ParallelInterdigitatedDistributed_2017; @dinicolaetal_ParallelDistributedNetworks_2020; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_DefaultmodeNetworkStreams_2020; @gordonnelson_ThreeTypesIndividual_2021]. Additionally, at an individual level, areal expansions and contractions appear to be interdependent within and between functional networks, such that (1) nodes of the same functional network tend to expand or contract together; and (2) the relative expansion of a given functional network tends to reduce the amount of cortex available to other adjacent, connected functional networks [@gordonetal_IndividualVariabilitySystemLevel_2017].
-Both connectional and spatial variability do not appear to be evenly distributed throughout the cortex---instead there appears to be a characteristic distribution of interindividual variability across individuals, such that certain functional networks and network nodes show greater individualization than others [@grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019]. For example, recent work has found that functional networks associated with higher-level functions show greater individualization than functional networks associated with sensorimotor processing [@grattonetal_FunctionalBrainNetworks_2018; @muelleretal_IndividualVariabilityFunctional_2013; @seitzmanetal_TraitlikeVariantsHuman_2019]; that nodes near the borders of functional networks previously described using group-averaged approaches tend to have more variable network affiliations across individuals than nodes that are not near borders [@gordonetal_IndividualVariabilitySystemLevel_2017]; and that individuals can be separated into trait-like subgroups based on similar distributions of interindividual variability in connectivity strength or the size and position of network nodes [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019]. Together, these findings suggest that the interindividual variability described thus far stems from (systematic) individual deviations from a basic organizing structure that is common across individuals [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019].
+Neither connectional nor spatial variability appear to be evenly distributed throughout the cortex---instead there appears to be a characteristic distribution of interindividual variability across individuals, such that certain functional networks and network nodes show greater individualization than others [@grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019]. For example, recent work suggests that functional networks associated with higher-level functions show greater individualization than functional networks associated with sensorimotor processing [@grattonetal_FunctionalBrainNetworks_2018; @muelleretal_IndividualVariabilityFunctional_2013; @seitzmanetal_TraitlikeVariantsHuman_2019]; that nodes near the borders of functional networks previously described using group-averaged approaches tend to have more variable network affiliations across individuals than nodes that are not near borders [@gordonetal_IndividualVariabilitySystemLevel_2017]; and that individuals can be separated into trait-like subgroups based on similar distributions of interindividual variability in connectivity strength or the size and position of network nodes [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019]. Together, these findings suggest that the interindividual variability described thus far stems from (systematic) individual deviations from a basic organizing structure that is common across individuals [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019].
-Indeed, an important assumption when studying each of the previous forms of interindividual variability is that every individual functional connectome has the same network topography---that is, they each have the same set of matched network nodes [@gordonnelson_ThreeTypesIndividual_2021]. These nodes may vary in their connectivity strength, size, position, or even their functional network affiliations, however, ultimately they are assumed to represent the same cortical components across individuals, making direct comparisons between individuals possible [@gordonnelson_ThreeTypesIndividual_2021]. This assumption is broadly supported by existing evidence: Although reliable and substantial interindividual variability is present in whole brain functional networks, these networks also appear to share common organizing principles across individuals, such that individuals largely seem to have the same set of functional networks composed of the same sets of network nodes [@gordonetal_IndividualVariabilitySystemLevel_2017]. For example, recent work has found that the boundaries between the default mode network and other functional networks can be readily identified within individuals [@bragaetal_ParallelDistributedNetworks_2019; @bragabuckner_ParallelInterdigitatedDistributed_2017; @dinicolaetal_ParallelDistributedNetworks_2020; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_DefaultmodeNetworkStreams_2020; @uddinetal_ControversiesProgressStandardization_2023], in line with the idea that a common basic organizing structure exists across individuals [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019].
+Indeed, an important assumption when studying each of the previous forms of interindividual variability is that every individual functional connectome has the same network topography---that is, they each have the same set of matched network nodes [@gordonnelson_ThreeTypesIndividual_2021]. These nodes may vary in their connectivity strength, size, position, or even their functional network affiliations, however, ultimately they are assumed to represent the same cortical components across individuals, making direct comparisons between individuals possible [@gordonnelson_ThreeTypesIndividual_2021]. This assumption is broadly supported in the literature. Although reliable and substantial interindividual variability is present in whole brain functional networks, these networks also appear to share common organizing principles across individuals, such that individuals largely seem to have the same set of functional networks composed of the same sets of network nodes [@gordonetal_IndividualVariabilitySystemLevel_2017]. For example, recent work has found that the boundaries between the default mode network and other functional networks can be readily identified within individuals [@bragaetal_ParallelDistributedNetworks_2019; @bragabuckner_ParallelInterdigitatedDistributed_2017; @dinicolaetal_ParallelDistributedNetworks_2020; @gordonetal_IndividualVariabilitySystemLevel_2017; @gordonetal_PrecisionFunctionalMapping_2017; @gordonetal_DefaultmodeNetworkStreams_2020; @uddinetal_ControversiesProgressStandardization_2023], in line with the idea that a common basic organizing structure exists across individuals [@gordonetal_IndividualVariabilitySystemLevel_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019].
However, on a local level, interindividual variability in network topography can cause single cortical areas representing network nodes---which appear unitary in group-averaged data and most individuals---to split into multiple discontinuous regions, creating apparent extra network nodes in every individual that are not typically present in other individuals [@glasseretal_MultimodalParcellationHuman_2016; @gordonnelson_ThreeTypesIndividual_2021; @laumannetal_FunctionalSystemAreal_2015; @seitzmanetal_TraitlikeVariantsHuman_2019; @uddinetal_ControversiesProgressStandardization_2023]. In some cases, these apparent extra network nodes have been found to exhibit the same properties as the unitary area [@glasseretal_MultimodalParcellationHuman_2016]; however, in others these nodes have been found to exhibit strong, idiosyncratic connectivity with a functional network different from the one they are situated within [@gordonnelson_ThreeTypesIndividual_2021; @laumannetal_FunctionalSystemAreal_2015; @seitzmanetal_TraitlikeVariantsHuman_2019; @uddinetal_ControversiesProgressStandardization_2023]. As @gordonnelson_ThreeTypesIndividual_2021 explain, the interpretation of this third form of interindividual variability is currently unclear, as it represents network nodes that are so connectionally and spatially divergent from group-averaged data and most other individuals that they may not be classifiable using existing approaches: Topographical variability may simply represent extreme examples of the spatial or connectional variability described above, or it may indeed represent cortical components that do not exist in the networks of most other individuals. Such possibilities remain to be investigated in future work.
@@ -162,15 +156,15 @@ However, on a local level, interindividual variability in network topography can
An important question for studies of network variants is whether or not interindividual variability in connectivity strength, the size and position of network nodes, or network topography relates to individual differences in behaviour, cognition, and their dysfunction [@finnetal_CanBrainState_2017; @seitzmanetal_TraitlikeVariantsHuman_2019]. One possibility is that this interindividual variability is functionally significant, with at least some aspects of this variability interacting within individuals to produce differences in behaviour and cognition [@gordonetal_IndividualVariabilitySystemLevel_2017; @seitzmanetal_TraitlikeVariantsHuman_2019]. Conversely, an alternative possibility is that this interindividual variability is functionally degenerate [@fristonprice_DegeneracyRedundancyCognitive_2003; @tononietal_MeasuresDegeneracyRedundancy_1999; @pricefriston_DegeneracyCognitiveAnatomy_2002]---that is, this variability may represent diverse but equivalently effective modes of functional organization, such that equivalent behavioural and cognitive outcomes may be instantiated by different patterns of organization [@gordonetal_IndividualVariabilitySystemLevel_2017; @seitzmanetal_TraitlikeVariantsHuman_2019].
-Although such possibilities have yet to be directly addressed, several studies have demonstrated that predictive models trained on features derived from connectivity strength, network size, and network topography can be used to predict individual differences in behaviour and cognition in novel participants. For example, features derived from connectivity strength have been demonstrated to predict individual differences in age [@pervaizetal_OptimisingNetworkModelling_2020], sex [@pervaizetal_OptimisingNetworkModelling_2020], fluid intelligence scores [@finnetal_FunctionalConnectomeFingerprinting_2015; @greeneetal_TaskinducedBrainState_2018; @pervaizetal_OptimisingNetworkModelling_2020], composite scores of cognition and emotion [@finnbandettini_MoviewatchingOutperformsRest_2020], neuroticism scores [@pervaizetal_OptimisingNetworkModelling_2020], sustained attention ability [@rosenbergetal_NeuromarkerSustainedAttention_2016], and changes in attentional state over minutes, days, weeks, and months [@rosenbergetal_BehavioralNeuralSignatures_2020]; and features derived from network size and network topography have been demonstrated to predict individual differences in scores on several cognition, personality, and emotion measures [@kongetal_SpatialTopographyIndividualSpecific_2019]. Together these results suggest that all three of these forms of interindividual variability may indeed be functionally significant for a wide range of behavioural and cognitive measures. However, in order to move us closer to an understanding of how much and in which ways these forms of variability may interact within individuals to produce differences in behaviour and cognition, further research is needed to characterize these relationships with greater precision, accuracy, and detail [@finnrosenberg_FingerprintingChoosingPredictive_2021; @mantwilletal_BrainConnectivityFingerprinting_2022; @seitzmanetal_TraitlikeVariantsHuman_2019; @wuetal_ChallengesProspectsBrainbased_2023].
+Although such possibilities have yet to be directly addressed, several studies have demonstrated that predictive models trained on features derived from connectivity strength, network size, and network topography can be used to predict individual differences in behaviour and cognition in novel participants. For example, features derived from connectivity strength have been demonstrated to predict individual differences in age [@pervaizetal_OptimisingNetworkModelling_2020], sex [@pervaizetal_OptimisingNetworkModelling_2020], fluid intelligence scores [@finnetal_FunctionalConnectomeFingerprinting_2015; @greeneetal_TaskinducedBrainState_2018; @pervaizetal_OptimisingNetworkModelling_2020], composite scores of cognition and emotion [@finnbandettini_MoviewatchingOutperformsRest_2020], neuroticism scores [@pervaizetal_OptimisingNetworkModelling_2020], sustained attention ability [@rosenbergetal_NeuromarkerSustainedAttention_2016], and changes in attentional state over minutes, days, weeks, and months [@rosenbergetal_BehavioralNeuralSignatures_2020]. Features derived from network size and network topography have been demonstrated to predict individual differences in scores on several cognition, personality, and emotion measures [@kongetal_SpatialTopographyIndividualSpecific_2019]. Together these results suggest that all three of these forms of interindividual variability may indeed be functionally significant for a wide range of behavioural and cognitive measures. However, in order to move us closer to an understanding of how much and in which ways these forms of variability may interact within individuals to produce differences in behaviour and cognition, further research is needed to characterize these relationships with greater precision, accuracy, and detail [@finnrosenberg_FingerprintingChoosingPredictive_2021; @mantwilletal_BrainConnectivityFingerprinting_2022; @seitzmanetal_TraitlikeVariantsHuman_2019; @wuetal_ChallengesProspectsBrainbased_2023].
## The present study
-The purpose of the present study is to explore the feasibility of studying network variants with EEG across several canonical frequency bands using measures of both phase coupling and amplitude coupling. To address this question we used EEG data from fourteen participants previously collected by our lab as part of a larger study, containing a total of 30 minutes eyes open resting state data (over 6 recordings) and 30 minutes eyes closed resting state data (over 6 recordings) from each participant collected during three sessions over the course of approximately three months. Each session consisted of four 5 minute recordings (2 eyes open, 2 eyes closed), with the time between sessions ranging from approximately one to two weeks from the first to second session, and approximately three months from the second to third session. With this design we were able to explore how functional connectomes differed within and between individuals, sessions, and states, and thus, whether or not higher frequency functional connectomes measured with EEG share similar evidence of stable individual differences to what has been described in the fMRI literature [e.g., @gordonetal_PrecisionFunctionalMapping_2017; @grattonetal_FunctionalBrainNetworks_2018]. Specifically, we were interested in how much and in which ways patterns of sensor-space connectivity strength varied within and between individuals across these different contexts.
+The purpose of the present study is to explore the feasibility of studying network variants with EEG across several canonical frequency bands using measures of both phase coupling and amplitude coupling. To address this question, we used EEG data from fourteen participants previously collected by our lab as part of a larger study, containing a total of 30 minutes eyes open resting state data (over 6 recordings) and 30 minutes eyes closed resting state data (over 6 recordings) from each participant collected during three sessions over the course of approximately three months. Each session consisted of four 5-minute recordings (2 eyes open, 2 eyes closed), with the time between sessions ranging from approximately one to two weeks from the first to second session, and approximately three months from the second to third session. With this design we were able to explore how functional connectomes differed within and between individuals, sessions, and states, and thus, whether or not higher frequency functional connectomes measured with EEG share similar evidence of stable individual differences to what has been described in the fMRI literature [e.g., @gordonetal_PrecisionFunctionalMapping_2017; @grattonetal_FunctionalBrainNetworks_2018]. Specifically, we were interested in how much and in which ways patterns of sensor-space connectivity strength varied within and between individuals across these different contexts.
-We chose to examine sensor-space (rather than source-space) connectivity here because no MRI data was collected from our participants, so we did not have the means to accurately use inverse source reconstruction methods. Although this decision precluded us from comparing functional connectome organization within and between individuals in terms of neurophysiology (e.g., spatial organization, topographical organization, network affiliation, etc.), we reiterate that differences in sensor-space connectivity imply that different distributions of neuronal sources are active in the brain over space and time; thus, comparisons of sensor-space connectivity still provide a sufficient, albeit opaque, method of investigating individual differences in underlying global network activity. To facilitate these comparisons, we used matrix correlations to quantify the similarity between pairs of functional connectomes, reducing the dimensionality of these data to a single interpretable number suitable for subsequent analyses. These correlations take values between 0 (no linear relationship) and 1 (perfect linear relationship), defining a scale of similarity between two functional connectomes that can be interpreted in a straightforward manner in much the same way as the familiar squared Pearson correlation coefficient [@josseholmes_MeasuringMultivariateAssociation_2016; @mayeretal_ExploratoryAnalysisMultiple_2011].
+To facilitate these comparisons, we estimated the strength of sensor-space connectivity using the phase lag index [PLI\; @stametal_PhaseLagIndex_2007] and the orthogonalized amplitude envelope correlation [AEC\; @hippetal_LargescaleCorticalCorrelation_2012], which measure phase coupling and amplitude coupling, respectively. We then used matrix correlations to quantify the similarity between pairs of functional connectomes, reducing the dimensionality of these data to a single interpretable number suitable for subsequent analyses, which served as an index of (inter)individual differences in underlying global network activity. These correlations take values between 0 (no linear relationship) and 1 (perfect linear relationship), defining a scale of similarity between two functional connectomes that can be interpreted in a straightforward manner in much the same way as the familiar squared Pearson correlation coefficient [@josseholmes_MeasuringMultivariateAssociation_2016; @mayeretal_ExploratoryAnalysisMultiple_2011].
-This dataset was particularly well-suited to address this question, given that we had sufficient data per participant, session, and state to examine how much and in which ways functional connectome similarity differed within and between individuals via the interaction of (1) session-dependent variability over the course of weeks and months, and (2) state-dependent variability over the course of minutes during different resting states. Figure \@ref(fig:similarity-archetype-plots) depicts six hypothetical outcomes we could find based on different assumptions about the underlying global network activity. The left column shows outcomes that would occur in cases where functional connectomes *do not* differ between individuals. In particular, we might expect to see (1) a *group effect* with high similarity among all measurements regardless of individual, session, or state, which might occur if the underlying activity we measured is largely intrinsic and common across individuals [cf. @raichle_TwoViewsBrain_2010]; (2) a *group-session effect* with high similarity among all measurements regardless of individual or state, but not session, which might occur if the underlying activity we measured is largely intrinsic, common across individuals, and varies over time in the same way across individuals; or (3) a *group-state effect* with high similarity among all measurements regardless of individual or session, but not state, which might occur if the underlying activity we measured is largely reactive in a way that is common across individuals [cf. @raichle_TwoViewsBrain_2010]. The right column shows outcomes that would occur in cases where functional connectomes *do* differ between individuals. In particular, we might expect to see (4) an *individual effect* with high similarity among all measurements regardless of session or state, but not individual, which might occur if the underlying activity we measured is largely intrinsic and unique within individuals; (5) an *individual-session effect* with high similarity among all measurements regardless of state, but not individual or session, which might occur if the underlying activity we measured is largely intrinsic, unique within individuals, and varies over time within individuals; or (6) an *individual-state effect* with high similarity among all measurements regardless of session, but not individual or state, which might occur if the underlying activity we measured is largely reactive in a way that is unique within individuals.
+This dataset was particularly well-suited to address this question, given that we had sufficient data per participant, session, and state to examine how functional connectome similarity differed within and between individuals across contexts via the interaction of (1) session-dependent variability over the course of weeks and months, and (2) state-dependent variability over the course of minutes during different resting states. Figure \@ref(fig:similarity-archetype-plots) depicts six hypothetical outcomes we could find based on different assumptions about the underlying global network activity. The left column shows outcomes that would occur in cases where functional connectomes *do not* differ between individuals. In particular, we might expect to see (1) a *group effect* with high similarity among all measurements regardless of individual, session, or state, which might occur if the underlying activity we measured is largely intrinsic and common across individuals [cf. @raichle_TwoViewsBrain_2010]; (2) a *group-session effect* with high similarity among all measurements regardless of individual or state, but not session, which might occur if the underlying activity we measured is largely intrinsic, common across individuals, and varies over time in the same way across individuals; or (3) a *group-state effect* with high similarity among all measurements regardless of individual or session, but not state, which might occur if the underlying activity we measured is largely reactive in a way that is common across individuals [cf. @raichle_TwoViewsBrain_2010]. The right column shows outcomes that would occur in cases where functional connectomes *do* differ between individuals. In particular, we might expect to see (4) an *individual effect* with high similarity among all measurements regardless of session or state, but not individual, which might occur if the underlying activity we measured is largely intrinsic and unique within individuals; (5) an *individual-session effect* with high similarity among all measurements regardless of state, but not individual or session, which might occur if the underlying activity we measured is largely intrinsic, unique within individuals, and varies over time within individuals; or (6) an *individual-state effect* with high similarity among all measurements regardless of session, but not individual or state, which might occur if the underlying activity we measured is largely reactive in a way that is unique within individuals.
```{r similarity-archetype-plots}
#| fig.height: 7.865627
@@ -179,7 +173,7 @@ targets::tar_load(similarity_archetypes_figure)
knitr::include_graphics(here::here(similarity_archetypes_figure))
```
-To quantify these effects, we used pairwise contrasts to estimate how much and in which ways functional connectome similarity differed within and between individuals across sessions and states. Figure \@ref(fig:contrasts-plot) illustrates these contrasts as they relate to the six hypothetical outcomes depicted in Figure \@ref(fig:similarity-archetype-plots) above. These contrasts were the estimands [i.e., the target quantities\; @lundbergetal_WhatYourEstimand_2021] of this study, with differences estimated for (1) the overall difference in similarity within and between participants, which we term the *main effect*; (2) the difference in similarity within and between participants for each level of one predictor (e.g., within session similarity) while averaging over levels of the other predictor (e.g., within and between state similarity); (3) the difference in similarity within and between participants for the unique combinations within and between session and state. As Figure \@ref(fig:contrasts-plot) makes clear, when there are equal amounts of functional connectome similarity within and between participants (i.e., when the underlying activity is common across individuals) there is zero difference in functional connectome similarity within versus between participants regardless of the underlying group, session, or state effects; however, when functional connectomes are more similar within than between participants (i.e., when the underlying activity is unique within individuals) there is a positive, non-zero difference which varies according to the type of individual effect (individual, individual-session, or individual-state effects). Thus, under this approach, a necessary condition for network variants to be detected is a positive, non-zero difference in functional connectome similarity.
+To quantify these effects, we used pairwise contrasts to estimate the direction and magnitude of differences in functional connectome similarity within and between individuals across sessions and states. Figure \@ref(fig:contrasts-plot) illustrates these contrasts as they relate to the six hypothetical outcomes depicted in Figure \@ref(fig:similarity-archetype-plots) above. These contrasts were the estimands [i.e., the target quantities\; @lundbergetal_WhatYourEstimand_2021] of this study, with differences estimated for (1) the overall difference in similarity within and between participants, which we term the *main effect*; (2) the difference in similarity within and between participants for each level of one predictor (e.g., within session similarity) while averaging over levels of the other predictor (e.g., within and between state similarity); (3) the difference in similarity within and between participants for the unique combinations within and between session and state. As Figure \@ref(fig:contrasts-plot) makes clear, when there are equal amounts of functional connectome similarity within and between participants (i.e., when the underlying activity is common across individuals) there is zero difference in functional connectome similarity within versus between participants regardless of the underlying group, session, or state effects; however, when functional connectomes are more similar within than between participants (i.e., when the underlying activity is unique within individuals) there is a positive, non-zero difference which varies according to the type of individual effect (individual, individual-session, or individual-state effects). Thus, under this approach, a necessary condition for network variants to be detected is a positive, non-zero difference in functional connectome similarity.
```{r contrasts-plot}
#| fig.height: 5.445434
@@ -187,7 +181,7 @@ To quantify these effects, we used pairwise contrasts to estimate how much and i
knitr::include_graphics(here::here("figures/contrasts-plot.png"))
```
-Here we focus on the estimation of the direction, strength, and uncertainty of these effects in our sample---explicitly refraining from a null-hypothesis significance testing approach in favour of an estimation approach [@berneramrhein_WhyHowWe_2021]. We favour an estimation approach for the following reasons: First, given the existing evidence that functional connectomes are highly stable within individuals with a large amount of unshared organization between people [e.g., @grattonetal_FunctionalBrainNetworks_2018], it is implausible that we will find zero difference in functional connectome similarity within versus between participants; thus, a point null hypothesis of "zero difference" would merely serve as a straw-man hypothesis to be rejected without adding anything meaningful to the question at hand [@berneramrhein_WhyHowWe_2021; @gelman_ProblemsPValuesAre_2016]. Instead, as we illustrated in Figures \@ref(fig:similarity-archetype-plots) and \@ref(fig:contrasts-plot), the motivation behind the present study was the exploration of broad empirical questions about how much and in which ways functional connectome similarity differed within and between individuals across sessions and states, which requires an approach describing and discussing the range of effect sizes that are most compatible with our data (given our background model), rather than the testing of narrow hypotheses that could either be "rejected" or "accepted" [@amrheinetal_InferentialStatisticsDescriptive_2019; @amrheinetal_ScientistsRiseStatistical_2019; @amrheingreenland_DiscussPracticalImportance_2022; @berneramrhein_WhyHowWe_2021]. Second, as numerous statisticians and scientists have warned for decades, the abuse of ritualistic dichotomous inference in lieu of statistical thinking across scientific disciplines has created a crisis of validity for scientific conclusions, including their replicability [@gigerenzer_StatisticalRitualsReplication_2018; @wassersteinetal_MovingWorld05_2019; @wassersteinlazar_ASAStatementPValues_2016]. Rather than contribute to this crisis, we recognize that the primary scientific contribution of the present study is the estimation of the direction, strength, and uncertainty of individual differences in functional connectome similarity in our sample; whereas meta-analytic studies and other cumulative approaches that combine information from multiple studies---each with their own set of conditions, assumptions, patterns of variation, and sources of systematic error---will typically be required to come to more generalized scientific conclusions about electrophysiological network variants [@amrheinetal_InferentialStatisticsDescriptive_2019; @berneramrhein_WhyHowWe_2021; @nicholsetal_AccumulatingEvidenceEcology_2019; @nicholsetal_BetterApproachDealing_2021].
+Here we focus on the estimation of the direction, strength, and uncertainty of these effects in our sample---explicitly refraining from a null-hypothesis significance testing approach in favour of an estimation approach [@berneramrhein_WhyHowWe_2021]. We favour an estimation approach for the following reasons: First, as we illustrated in Figures \@ref(fig:similarity-archetype-plots) and \@ref(fig:contrasts-plot), the motivation behind the present study was the exploration of broad empirical questions about how much and in which ways functional connectome similarity differed within and between individuals across sessions and states, which requires an approach describing and discussing the range of effect sizes that are most compatible with our data (given our background model), rather than the testing of narrow hypotheses that could either be "rejected" or "accepted" [@amrheinetal_InferentialStatisticsDescriptive_2019; @amrheinetal_ScientistsRiseStatistical_2019; @amrheingreenland_DiscussPracticalImportance_2022; @berneramrhein_WhyHowWe_2021]. Second, as numerous statisticians and scientists have warned for decades, the abuse of ritualistic dichotomous inference in lieu of statistical thinking across scientific disciplines has created a crisis of validity for scientific conclusions, including their replicability [@gigerenzer_StatisticalRitualsReplication_2018; @wassersteinetal_MovingWorld05_2019; @wassersteinlazar_ASAStatementPValues_2016]. Rather than contribute to this crisis, we recognize that the primary scientific contribution of the present study is the estimation of the direction, strength, and uncertainty of individual differences in functional connectome similarity in our sample; whereas meta-analytic studies and other cumulative approaches that combine information from multiple studies---each with their own set of conditions, assumptions, patterns of variation, and sources of systematic error---will typically be required to come to more generalized scientific conclusions about electrophysiological network variants [@amrheinetal_InferentialStatisticsDescriptive_2019; @berneramrhein_WhyHowWe_2021; @nicholsetal_AccumulatingEvidenceEcology_2019; @nicholsetal_BetterApproachDealing_2021].
It is likely that the real data will be representative of more than one of the hypothetical outcomes illustrated in Figures \@ref(fig:similarity-archetype-plots) and \@ref(fig:contrasts-plot). Figure \@ref(fig:outcome-plot) illustrates what this might look like should the individual, individual-session, and individual-state effects be equally represented in the underlying global network activity, based on the simple averaging of each effects' contrasts. We emphasize that this illustration represents an unrealistic scenario where each individual's functional connectomes are perfectly similar with themselves and perfectly dissimilar with others; therefore, the magnitude of these effect sizes is greatly exaggerated. In reality, we would expect to find smaller effect sizes whose magnitudes decrease as the dynamics of underlying global network activity becomes more similar across individuals. Moreover, it is rather unlikely that the individual, individual-session, and individual-state effects would be equally weighted; thus, we would also expect to find an additional degree of variation in this hypothetical pattern of results based on the relative contribution of these effects to the stability of functional connectome similarity within individuals across sessions and states [@grattonetal_FunctionalBrainNetworks_2018]. We illustrate the relative influence of these effects in the second, third, and fourth plots of Figure \@ref(fig:outcome-plot). In the second plot we see that as the individual effect makes a greater relative contribution, the magnitude of effect sizes across all contrasts becomes greater; whereas, in the third and fourth plots we see that as either the individual-session or individual-state effects make a greater relative contribution, an additional degree of effect size variation appears with both more and less pronounced differences in functional connectome similarity occurring across sessions and states.
@@ -200,7 +194,7 @@ knitr::include_graphics(here::here("figures/outcome-plot.png"))
Based on the findings of previous fMRI network variant research [e.g., @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019], we expected to find patterns of results consistent with the fourth plot in Figure \@ref(fig:outcome-plot) across all frequency bands for both phase coupling and amplitude coupling functional connectomes---consistent with functional connectomes that were more similar within than between individuals across all contrasts, with greater variations in similarity related to state than session. However, we emphasize that any of the result patterns depicted in Figure \@ref(fig:outcome-plot) would provide supportive evidence of stable individual differences in EEG functional connectomes, albeit with somewhat different interpretations as we described above for the individual, individual-session, and individual-state effects. Thus, our primary scientific hypothesis was that if the phase coupling and/or amplitude coupling dynamics of underlying global network activity in a given frequency band was influenced by stable individual-dependent factors *in our sample*, then functional connectomes would be more similar within than between individuals across all contrasts, on average, with smaller variations in similarity related to session or state.
-We had no expectations regarding the possible range of effect sizes we could find, but again note that relatively higher values correspond to greater within-individual functional connectome stability and between-individual differentiation across contexts; whereas relatively lower values correspond to lesser within-individual functional connectome stability and between-individual differentiation. Thus, we reiterate the importance of considering the range of effect sizes most compatible with our data (given our background model) when drawing conclusions about our primary scientific hypothesis, rather than merely drawing conclusions based on the direction of these effects and whether or not they included zero [@amrheinetal_InferentialStatisticsDescriptive_2019]. Additionally, to contextualize our results against the relevant fMRI literature, we considered the results of @grattonetal_FunctionalBrainNetworks_2018 as a cautious point of reference, keeping in mind that finding equivalent effect sizes would be surprising, given the differences in measurement, design, and analysis between our studies, on top of random variation in the data between our samples. In their study, @grattonetal_FunctionalBrainNetworks_2018 investigated how functional connectome similarity---measured using the Pearson correlation coefficient between the vectorized lower triangles of each functional connectome pair---differed within and between individuals across ten separate fMRI sessions and five different states (eyes-open resting state, and visual coherence, semantic, memory, and motor tasks). They found that functional connectomes were more similar within participants than between participants across contexts, on average, at both the group-level and individual-level by approximately 0.2 points ($\mathrm{Mean_{within}} = 0.556$, $\mathrm{Mean_{between}} = 0.357$) on the response scale when functional connectivity was estimated at the recording level, and by approximately 0.3 points ($\mathrm{Mean_{within}} = 0.795$, $\mathrm{Mean_{between}} = 0.512$) on the response scale when functional connectivity was estimated using the split-half method (see, respectively, Figures S2 and 3 in @grattonetal_FunctionalBrainNetworks_2018).[^5] A similarity matrix for the eyes-open resting state functional connectomes from this sample was also reported by this group of researchers in a separate study, which showed individual differences in functional connectome similarity of a similar magnitude across sessions to those discussed above (see Figure 2H in @gordonetal_PrecisionFunctionalMapping_2017).
+We had no expectations regarding the possible range of effect sizes we could find, but again note that relatively higher values correspond to greater within-individual functional connectome stability and between-individual differentiation across contexts, whereas relatively lower values correspond to lesser within-individual functional connectome stability and between-individual differentiation. Thus, we reiterate the importance of considering the range of effect sizes most compatible with our data (given our background model) when drawing conclusions about our primary scientific hypothesis, rather than merely drawing conclusions based on the direction of these effects and whether or not they included zero [@amrheinetal_InferentialStatisticsDescriptive_2019]. Additionally, to contextualize our results against the relevant fMRI literature, we considered the results of @grattonetal_FunctionalBrainNetworks_2018 as a cautious point of reference, keeping in mind that finding equivalent effect sizes would be surprising, given the differences in measurement, design, and analysis between our studies, on top of random variation in the data between our samples. In their study, @grattonetal_FunctionalBrainNetworks_2018 investigated how functional connectome similarity---measured using the Pearson correlation coefficient between the vectorized lower triangles of each functional connectome pair---differed within and between individuals across ten separate fMRI sessions and five different states (eyes-open resting state, and visual coherence, semantic, memory, and motor tasks). They found that functional connectomes were more similar within participants than between participants across contexts, on average, at both the group-level and individual-level by approximately 0.2 points ($\mathrm{Mean_{within}} = 0.556$, $\mathrm{Mean_{between}} = 0.357$) on the response scale when functional connectivity was estimated at the recording level, and by approximately 0.3 points ($\mathrm{Mean_{within}} = 0.795$, $\mathrm{Mean_{between}} = 0.512$) on the response scale when functional connectivity was estimated using the split-half method (see, respectively, Figures S2 and 3 in @grattonetal_FunctionalBrainNetworks_2018).[^5] A similarity matrix for the eyes-open resting state functional connectomes from this sample was also reported by this group of researchers in a separate study, which showed individual differences in functional connectome similarity of a similar magnitude across sessions to those discussed above (see Figure 2H in @gordonetal_PrecisionFunctionalMapping_2017).
Finally, given our focus on individual differences, we considered it naturally important to investigate outcome variability of these effects across participants; particularly because network variants have been characterized as a common phenomenon present in all individuals, rather than an idiosyncrasy of just a few individuals [@seitzmanetal_TraitlikeVariantsHuman_2019]. This served as a compliment to the group-level analysis of individual differences described above, allowing us to evaluate whether the group-level outcomes we observed would hold at the level of individuals (i.e., the level of observation where these differences originated from) in terms of the direction of these effects. Indeed, a central aim of investigating outcome variability was to dispel any misconceptions about the implications of our findings that could be caused by focusing only on the group-level outcomes---which can lead even highly trained experts to overestimate the importance and generalizability of findings due to uncertainty about average effects often being quite precise even in the presence of substantial outcome variability [@zhangetal_IllusionPredictabilityScientific_2022].
@@ -208,12 +202,14 @@ Finally, given our focus on individual differences, we considered it naturally i
[^1]: Although the nodes of fMRI functional networks can also include subcortical and cerebellar brain regions, we adopt a cortico-centric definition of whole brain functional networks here for inclusivity with EEG/MEG functional networks, which are generally limited to measuring cortical activity. This definition is not particularly limiting to the present discussion, as contemporary fMRI investigations largely ignore subcortical and cerebellar brain regions anyways [@uddinetal_UniversalTaxonomyMacroscale_2019; @uddinetal_ControversiesProgressStandardization_2023], and the fMRI network variant research to date has focused exclusively on cortical connectivity.
+[^99]: It is important to note that such naming conventions should not be taken to mean that these networks are solely involved in the putative function(s) associated with their assigned name or category [@uddinetal_UniversalTaxonomyMacroscale_2019; @yeoetal_OrganizationHumanCerebral_2011], as multiple networks may be involved in the same function(s) (i.e., many-to-one mapping) and singular networks may be involved in multiple functions [i.e., one-to-many mapping\; @pessoa_UnderstandingBrainNetworks_2014].
+
[^2]: Mathematical details and discussion of several widely used phase coupling metrics can be found in a review by @bastosschoffelen_TutorialReviewFunctional_2016.
[^3]: For historically interested readers, this renewed interest in individual differences for network neuroscience research is much akin to Cronbach's [-@cronbach_TwoDisciplinesScientific_1957] treatise on the historic separation of scientific psychology into two distinct disciplines---the so-called *experimental* and *correlational* disciplines, which broadly speaking studied variance only among either treatments or individuals, respectively---and the need for these disciplines to combine their efforts if the field ever hopes to solve its most important problems. Similar reflections are now being seen in the field of network neuroscience, as researchers increasingly recognize the importance of studying the variation that already exists between and within individuals to further our understanding of the human brain [e.g., @elliottetal_WhatTestRetestReliability_2020; @grattonetal_BrainbehaviorCorrelationsTwo_2022; @uddinetal_UniversalTaxonomyMacroscale_2019; @uddinetal_ControversiesProgressStandardization_2023].
[^4]: To be clear, this is not to say that brain state or time have no influence on interindividual variability in connectivity strength. Indeed, the relative magnitude of functional connectome similarities within and between individuals has been found to vary moderately by behavioural or cognitive state and mildly by time such that individualization is measurably greater during certain brain states than others, and slightly greater for recordings taken during the same session [@finnetal_CanBrainState_2017; @grattonetal_FunctionalBrainNetworks_2018; @seitzmanetal_TraitlikeVariantsHuman_2019]. However, this influence is considerably smaller relative to the influence contributed by individuals themselves [@grattonetal_FunctionalBrainNetworks_2018].
-[^5]: These values correspond to the *main effect* contrasts in our study and were obtained by back-transforming the Fisher-transformed Pearson correlation coefficients for the group and individual effects reported in @grattonetal_FunctionalBrainNetworks_2018, then taking their difference. For the split-half method, functional connectivity was estimated for each individual and task on the basis of two 5 session groups (by concatenating the recordings from a given task in each group together to increase the reliability of functional connectivity estimates), rather than at the recording level (for further details, see @grattonetal_FunctionalBrainNetworks_2018).
+[^5]: These values correspond to the *main effect* contrasts in our study and were obtained by back-transforming the Fisher-transformed Pearson correlation coefficients for the group and individual effects reported in @grattonetal_FunctionalBrainNetworks_2018, then taking their difference. For the split-half method, functional connectivity was estimated for each individual and task on the basis of two 5-session groups (by concatenating the recordings from a given task in each group together to increase the reliability of functional connectivity estimates), rather than at the recording level [for further details, see @grattonetal_FunctionalBrainNetworks_2018].
diff --git a/manuscripts/child-documents/methods.Rmd b/manuscripts/child-documents/methods.Rmd
index b939f9c..9023a8c 100644
--- a/manuscripts/child-documents/methods.Rmd
+++ b/manuscripts/child-documents/methods.Rmd
@@ -11,7 +11,7 @@ tar_load(participant_descriptives)
tar_load(participant_descriptives_final)
```
-`r stringr::str_to_sentence(xfun::numbers_to_words(participant_descriptives$n))` healthy adults (`r participant_descriptives$male` males, `r participant_descriptives$female` females) whose ages ranged from `r participant_descriptives$years_age_min` to `r participant_descriptives$years_age_max` years (M = `r papaja::apa_num(participant_descriptives$years_age_mean)`, SD = `r papaja::apa_num(participant_descriptives$years_age_sd)`) and whose years of education ranged from `r participant_descriptives$years_education_min` to `r participant_descriptives$years_education_max` years (M = `r papaja::apa_num(participant_descriptives$years_education_mean)`, SD = `r papaja::apa_num(participant_descriptives$years_education_sd)`) participated in the original study. All participants were right handed, spoke English as a first language, and had normal or corrected-to-normal vision. Exclusion criteria included a history of neurological disease or disorder, mental illness, head trauma, alcoholism or drug abuse, or use of psychotropic medications in the last two years preceding data collection, as determined through a screening questionnaire. We excluded EEG data for analysis from four participants because they did not participate in the final session, and three participants because they had one or more recordings with excessive noise. The final sample used for analysis consisted of `r xfun::numbers_to_words(participant_descriptives_final$n)` participants (`r participant_descriptives_final$male` males, `r participant_descriptives_final$female` females) whose ages ranged from `r participant_descriptives_final$years_age_min` to `r participant_descriptives_final$years_age_max` years (M = `r papaja::apa_num(participant_descriptives_final$years_age_mean)`, SD = `r papaja::apa_num(participant_descriptives_final$years_age_sd)`) and whose years of education ranged from `r participant_descriptives_final$years_education_min` to `r participant_descriptives_final$years_education_max` years (M = `r papaja::apa_num(participant_descriptives_final$years_education_mean)`, SD = `r papaja::apa_num(participant_descriptives_final$years_education_sd)`).
+`r stringr::str_to_sentence(xfun::numbers_to_words(participant_descriptives$n))` healthy adults (`r participant_descriptives$male` males, `r participant_descriptives$female` females) whose ages ranged from `r participant_descriptives$years_age_min` to `r participant_descriptives$years_age_max` years (M = `r papaja::apa_num(participant_descriptives$years_age_mean)`, SD = `r papaja::apa_num(participant_descriptives$years_age_sd)`) and whose years of education ranged from `r participant_descriptives$years_education_min` to `r participant_descriptives$years_education_max` years (M = `r papaja::apa_num(participant_descriptives$years_education_mean)`, SD = `r papaja::apa_num(participant_descriptives$years_education_sd)`) participated in the original study. All participants were right-handed, spoke English as a first language, and had normal or corrected-to-normal vision. Exclusion criteria included a history of neurological disease or disorder, mental illness, head trauma, alcoholism or drug abuse, or use of psychotropic medications in the last two years preceding data collection, as determined through a screening questionnaire. We excluded EEG data for analysis from four participants because they did not participate in the final session, and three participants because they had one or more recordings with excessive noise. The final sample used for analysis consisted of `r xfun::numbers_to_words(participant_descriptives_final$n)` participants (`r participant_descriptives_final$male` males, `r participant_descriptives_final$female` females) whose ages ranged from `r participant_descriptives_final$years_age_min` to `r participant_descriptives_final$years_age_max` years (M = `r papaja::apa_num(participant_descriptives_final$years_age_mean)`, SD = `r papaja::apa_num(participant_descriptives_final$years_age_sd)`) and whose years of education ranged from `r participant_descriptives_final$years_education_min` to `r participant_descriptives_final$years_education_max` years (M = `r papaja::apa_num(participant_descriptives_final$years_education_mean)`, SD = `r papaja::apa_num(participant_descriptives_final$years_education_sd)`).
### Electrophysiological Data Acquisition
@@ -31,7 +31,7 @@ runtime <- targets::tar_meta(fields = "seconds") |>
papaja::print_num()
```
-All computational steps were done using the open source programming language R [version `r r_version`, \; @R-base]. We maintained a reproducible workflow for the entire pipeline---from data cleaning to reporting---using the targets package [@R-targets], and managed R and Python dependencies using the renv package [@R-renv]. All EEG preprocessing and functional connectivity analysis was done using the open source Python package MNE-Python [version 2.2.0, \; @gramfortetal_MEGEEGData_2013], which was called from R using the reticulate package [@R-reticulate]. We estimated the RV coefficient using the FactoMineR package [@R-FactoMineR], and fit the mixed beta regression and contrasts using the glmmTMB [@R-glmmTMB] and emmeans [@R-emmeans] packages, respectively. The DHARMa [@R-DHARMa] and performance [@R-performance] packages were used for model diagnostics. All data visualization was done with a combination of the ggplot2 [@R-ggplot2], ggdist [@R-ggdist], ggh4x [@R-ggh4x], ggnewscale [@R-ggnewscale], and patchwork [@R-patchwork] packages; and all tables were made with a combination of the flextable [@R-flextable], ftExtra [@R-ftExtra], and gtsummary [@R-gtsummary] packages. Other computations were done using the tidyverse [@R-tidyverse] suite of packages. Finally, this manuscript itself was written in R Markdown [@R-rmarkdown] using officedown [@R-officedown] with the papaja package's APA template [@R-papaja], and all reported numbers, figures, and tables were printed using inline code to ensure their accuracy.
+All computational steps were done using the open-source programming language R [version `r r_version`, \; @R-base]. We maintained a reproducible workflow for the entire pipeline---from data cleaning to reporting---using the targets package [@R-targets], and managed R and Python dependencies using the renv package [@R-renv]. All EEG preprocessing and functional connectivity analysis was done using the open-source Python package MNE-Python [version 2.2.0, \; @gramfortetal_MEGEEGData_2013], which was called from R using the reticulate package [@R-reticulate]. We estimated the RV coefficient using the FactoMineR package [@R-FactoMineR], and fit the mixed beta regression and contrasts using the glmmTMB [@R-glmmTMB] and emmeans [@R-emmeans] packages, respectively. The DHARMa [@R-DHARMa] and performance [@R-performance] packages were used for model diagnostics. All data visualization was done with a combination of the ggplot2 [@R-ggplot2], ggdist [@R-ggdist], ggh4x [@R-ggh4x], ggnewscale [@R-ggnewscale], and patchwork [@R-patchwork] packages; and all tables were made with a combination of the flextable [@R-flextable], ftExtra [@R-ftExtra], and gtsummary [@R-gtsummary] packages. Other computations were done using the tidyverse [@R-tidyverse] suite of packages. Finally, this manuscript itself was written in R Markdown [@R-rmarkdown] using officedown [@R-officedown] with the papaja package's APA template [@R-papaja], and all reported numbers, figures, and tables were printed using inline code to ensure their accuracy.
The total computation time for the entire targets pipeline was approximately `r runtime` hours. A complete record of the R environment and packages used in this study can be found in Tables B\@ref(tab:session-info-environment) and B\@ref(tab:session-info-packages) in Appendix B. All code used in this study is openly available, licensed under the MIT License, and can be accessed at the study's GitHub repository or Open Science Framework (OSF) repository (DOI: ). A copy of this licence and its terms can be found in the study's GitHub or OSF repository.
@@ -72,7 +72,7 @@ bad_segments_duration_totals_sd <- sd(bad_segments_duration_totals)
Raw EEG data was preprocessed to remove noise and non-neural artifacts from the data using the following steps. First, following recommendations by @widmannetal_DigitalFilterDesign_2015, a two-pass forward and reverse, zero-phase, non-causal band-pass finite impulse response filter was used to remove slow drift potentials at infraslow frequencies less than 0.10 Hz, line noise at 60.00 Hz, and irrelevant noise fluctuations greater than 60.00 Hz [@decheveignenelken_FiltersWhenWhy_2019; @widmannetal_DigitalFilterDesign_2015]. The finite impulse response filter used a Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation. The lower passband edge and transition bandwidth were set at 0.10 Hz (-12 dB cutoff frequency: 0.05 Hz), and the upper passband edge and transition bandwidth were set at 50.00 Hz (-12 dB cutoff frequency: 56.25 Hz). The filter length was 16501 samples (33.002 seconds). These filter parameters were selected after exploring the difference between raw and filtered signals and verifying that the selected filter parameters improved signal quality over alternative filter parameters or the raw signal [@widmannetal_DigitalFilterDesign_2015]. Second, the data was downsampled from 500 to 200 Hz in order to reduce the size of the data and speed up computations operating on the data. Third, data was rereferenced to the common average reference in order to reduce spatial biases in the signal amplitude of channels caused by their distance to the original reference electrode [@nunezsrinivasan_ElectricFieldsBrain_2006]. Fourth, bad channels and segments were manually marked and removed to prepare the data for ICA decomposition. Channels were marked as bad if they contained excessive noise or drift for a significant portion of the recording. Bad channels were marked in `r papaja::apa_num(bad_channels_percent_bad)`% of recordings, ranging from `r bad_channels_n_min` to `r bad_channels_n_max` bad channels marked per recording with a mode of `r bad_channels_n_mode`. Segments were marked as bad if they contained excessive noise or drift that (1) could interfere with fitting the ICA decomposition due to the amount of variance their component would capture, or (2) was unlikely to be repaired by ICA decomposition. Bad segments were marked in `r papaja::apa_num(bad_segments_percent_bad)`% of the recordings, ranging from `r bad_segments_n_min` to `r bad_segments_n_max` bad segments marked per recording with a mode of `r bad_segments_n_mode`. The total duration of bad segments per recording across the entire sample ranged from `r papaja::apa_num(bad_segments_duration_totals_min)` to `r papaja::apa_num(bad_segments_duration_totals_max)` seconds long (M = `r papaja::apa_num(bad_segments_duration_totals_mean)`, SD = `r papaja::apa_num(bad_segments_duration_totals_sd)`). Fifth, ICA decomposition fitted using the Picard algorithm [@ablinetal_FasterICAOrthogonal_2017; @ablinetal_FasterIndependentComponent_2018] was performed to remove components carrying muscle artifacts or ocular artifacts (eye blinks, saccades, or horizontal eye movements) from the data. Components carrying artifacts were manually selected and then removed. Finally, bad channels were interpolated using spherical splines [@perrinetal_SphericalSplinesScalp_1989] and put back into the data.
-After artifact rejection, the continuous data was divided into 5 second epochs and filtered into five frequency bands to prepare for functional connectivity analysis. This was a three step process. First, the epochs with an 8 second duration were created at 5 second intervals such that an epoch occurred from 0-8 seconds, 5-13 seconds, 10-18 seconds, and so forth, until the end of the recording. Epochs that contained a bad segment were removed. Second, the epoched data was filtered into five frequency bands---delta ($\delta$, 1-4 Hz), theta ($\theta$, 4-8 Hz), alpha ($\alpha$, 8-13 Hz), beta ($\beta$, 13-30 Hz), and gamma ($\gamma$, 30-50 Hz)---using a two-pass forward and reverse, zero-phase, non-causal band-pass finite impulse response filter. Third, epochs were cropped to a 5 second duration starting 1 second into each epoch such that an epoch then occurred from 1-6 seconds, 6-11 seconds, 11-16 seconds, and so forth. We used this three step process in order to avoid distorting the true signal, as filtering epoched data creates edge artifacts at the start and end of each epoch that distort the true signal. Creating longer epochs in the first step provided padding around the edges of each epoch that could be cropped in the third step to remove these edge artifacts; and overlapping epochs in the first step made it possible for the cropped epochs in the third step to be non-overlapping and contiguous with reference to the continuous data, preserving the true signal across the length of the recording. The final 5 second duration was selected in order to have a sufficient number of oscillatory cycles per epoch to get reliable functional connectivity estimates across all five frequency bands.
+After artifact rejection, the continuous data was divided into 5 second epochs and filtered into five frequency bands to prepare for functional connectivity analysis. This was a three-step process. First, the epochs with an 8 second duration were created at 5 second intervals such that an epoch occurred from 0-8 seconds, 5-13 seconds, 10-18 seconds, and so forth, until the end of the recording. Epochs that contained a bad segment were removed. Second, the epoched data was filtered into five frequency bands---delta ($\delta$, 1-4 Hz), theta ($\theta$, 4-8 Hz), alpha ($\alpha$, 8-13 Hz), beta ($\beta$, 13-30 Hz), and gamma ($\gamma$, 30-50 Hz)---using a two-pass forward and reverse, zero-phase, non-causal band-pass finite impulse response filter. Third, epochs were cropped to a 5 second duration starting 1 second into each epoch such that an epoch then occurred from 1-6 seconds, 6-11 seconds, 11-16 seconds, and so forth. We used this three-step process in order to avoid distorting the true signal, as filtering epoched data creates edge artifacts at the start and end of each epoch that distort the true signal. Creating longer epochs in the first step provided padding around the edges of each epoch that could be cropped in the third step to remove these edge artifacts; and overlapping epochs in the first step made it possible for the cropped epochs in the third step to be non-overlapping and contiguous with reference to the continuous data, preserving the true signal across the length of the recording. The final 5 second duration was selected in order to have a sufficient number of oscillatory cycles per epoch to get reliable functional connectivity estimates across all five frequency bands.
## Analyses
@@ -158,13 +158,13 @@ $$
X_{\bot Y}(a, b) = \operatorname{Im} \left(X(a, b) \frac{Y(a, b)^*}{\vert Y(a, b) \vert} \right),
$$
-where $Y(a, b)^*$ is the complex conjugate of $X(a, b)$. Similarly to the phase lag index, the orthogonalization procedure used here allowed us to ignore parts of the signal that can be explained by volume conduction from a single common source, making the subsequent amplitude envelope correlations a sound measure of the interactions between different underlying neural sources [@hippetal_LargescaleCorticalCorrelation_2012]. After orthogonalization, the amplitude envelopes of the two analytic signals were computed by taking the absolute value of each signal, then the amplitude envelope correlation between them was estimated. We computed amplitude envelope correlations for both directions of the orthogonalization ($X$ to $Y$, and $Y$ to $X$), then averaged the values to get the final amplitude envelope correlation estimate for each pair.
+where $Y(a, b)^*$ is the complex conjugate of $X(a, b)$. This orthogonalization procedure allowed us to ignore the parts of each signal that could be explained by volume conduction from a single common source, making the subsequent amplitude envelope correlations a sound measure of the interactions between different underlying neural sources [@hippetal_LargescaleCorticalCorrelation_2012]. After orthogonalization, the amplitude envelopes of the two analytic signals were computed by taking the absolute value of each signal, then the amplitude envelope correlation between them was estimated. We computed amplitude envelope correlations for both directions of the orthogonalization ($X$ to $Y$, and $Y$ to $X$), then averaged the values to get the final amplitude envelope correlation estimate for each pair.
The amplitude envelope correlation takes values between 0 and 1. A value of 0 indicates no amplitude coupling between the orthogonalized signals, and a value of 1 indicates perfect amplitude coupling with a consistent amplitude envelope correlation between the orthogonalized signals.
#### Interpreting functional connectivity
-We use connectivity profile matrices [@demuruetal_FunctionalEffectiveWhole_2017] to compactly display the phase and amplitude coupling functional connectome from each recording. These plots serve as a supplement to our similarity analyses, providing a broad idea of what aspects of functional connectivity are being summarized by a given similarity estimate. Each row shows the vectorized lower-triangle of the phase or amplitude coupling functional connectome from a given recording (i.e., all unique pairs in the connectome), and each column represents a pair of sensors. The strength of coupling between any pair of sensors corresponds to their phase lag index or amplitude envelope correlation estimate and is represented by the colour of that cell in the matrix, with darker colours representing less coupling and brighter colours representing more coupling.
+We use connectivity profile matrices [@demuruetal_FunctionalEffectiveWhole_2017] to compactly display the phase and amplitude coupling functional connectome from each recording. These plots serve as a supplement to our similarity analyses, providing a broad idea of what aspects of functional connectivity are being summarized by a given similarity estimate. Each row shows the vectorized lower triangle of the phase or amplitude coupling functional connectome from a given recording (i.e., all unique pairs in the connectome), and each column represents a pair of sensors. The strength of coupling between any pair of sensors corresponds to their phase lag index or amplitude envelope correlation estimate and is represented by the colour of that cell in the matrix, with darker colours representing less coupling and brighter colours representing more coupling.
### Functional connectome similarity
@@ -190,19 +190,19 @@ We use similarity matrices to examine the similarities of functional connectomes
-We used a mixed beta regression to model how functional connectome similarity varied between and within individuals, sessions, and states at the group-level for each frequency band [for an accessible overview of mixed beta regression, see @doumaweedon_AnalysingContinuousProportions_2019; @heiss_GuideModelingProportions_2021]. The unit of observation was the pair of functional connectomes used for a given similarity estimate. The response variable was the RV coefficient estimated for each pair, which was modelled with a beta distribution and related to the predictors with the logit link function following the parameterization of @cribari-netozeileis_BetaRegression_2010 and @ferraricribari-neto_BetaRegressionModelling_2004. The predictors were binary indicators (where $0 = \textrm{No}$ and $1 = \textrm{Yes}$) for whether a pair was within participant, within session, or within state, and their interactions. Because the RV coefficient estimates are based on the similarity of pairs of functional connectomes we accounted for statistical dependencies between observations by adding random intercepts to the model. All random effects were assumed to be normally distributed [@brooksetal_GlmmTMBBalancesSpeed_2017].
+We used a mixed beta regression to model how functional connectome similarity varied between and within individuals, sessions, and states at the group-level for each frequency band [for an accessible overview of mixed beta regression, see @doumaweedon_AnalysingContinuousProportions_2019; @heiss_GuideModelingProportions_2021]. The unit of observation was the pair of functional connectomes used for a given similarity estimate. The response variable was the RV coefficient estimated for each pair, which was modelled with a beta distribution and related to the predictors with the logit link function following the parameterization of @cribari-netozeileis_BetaRegression_2010 and @ferraricribari-neto_BetaRegressionModelling_2004. The predictors were binary indicators (where $0 = \textrm{No}$ and $1 = \textrm{Yes}$) for whether a pair was within participant, within session, or within state, and their interactions. Because the RV coefficient estimates are based on the similarity of pairs of functional connectomes, we accounted for statistical dependencies between observations by adding random intercepts to the model. All random effects were assumed to be normally distributed [@brooksetal_GlmmTMBBalancesSpeed_2017].
We fit two versions of this model, which differed slightly in their random effects specification: A *maximal model* (@barretal_RandomEffectsStructure_2013) with random intercepts for both the $\mathbf{X}$ and $\mathbf{Y}$ connectome that each observation came from to account for statistical dependencies between observations that had a connectome in common, and a random intercept for the participant pair to account for the repeated observation of the same participant pairs; and a *reduced model* with only random intercepts for both the $\mathbf{X}$ and $\mathbf{Y}$ connectome that each observation came from. The reduced models were fit for pragmatic reasons, due to convergence problems with several of the maximal models where the estimate for the variance of the participant pair parameter was zero (i.e., the fit was singular). The effect of not including participant pair was negligible for point estimates, which were similar or equivalent between models where it was and was not included; but was noticeable for interval estimates, which were generally wider by a factor of two to four when it was included compared to when it was not included. This difference did not meaningfully change the conclusions drawn from our results, however. Results from the reduced models are presented in the results section of this manuscript for parsimony across sections, and results from the maximal models are shown in Figures A\@ref(fig:phase-similarity-plots-maximal-theta)-A\@ref(fig:phase-similarity-plots-hilbert-maximal-gamma) in Appendix A.
The fit of the model was assessed using the following diagnostics: Posterior predictive checks---wherein we simulated replicated data under the fitted model and compared these to the observed data---were used to check for systematic differences between the fitted model and observed data [@gelmanetal_BayesianDataAnalysis_2013; @gelmanetal_RegressionOtherStories_2020; @gelmanhill_DataAnalysisUsing_2006]. Randomized quantile residuals plotted against uniform distribution quantiles were used to check for signs of model misspecification [@dunnsmyth_RandomizedQuantileResiduals_1996; @hartiglohse_DHARMaResidualDiagnostics_2022]. Quantiles of the random effects plotted against standard normal distribution quantiles were used to check if the random effects were normally distributed; however, generalized linear mixed effects models have a large degree of robustness against misspecifying the shape of the random effects distribution [see @mccullochneuhaus_MisspecifyingShapeRandom_2011]. Variance Inflation Factors were used to check for multicollinearity between the predictors [@foxmonette_GeneralizedCollinearityDiagnostics_1992; @marcoulidesraykov_EvaluationVarianceInflation_2019; @obrien_CautionRegardingRules_2007], where a value of less than five as indicated low multicollinearity between the predictors, values between five and ten moderate, and values greater than ten high and not tolerable [@jamesetal_IntroductionStatisticalLearning_2021].
-After fitting the model we constructed a reference grid using estimated marginal means [@lenthetal_EmmeansEstimatedMarginal_2022; @searleetal_PopulationMarginalMeans_1980], which were back-transformed to the response scale (0-1) for interpretability. The estimated marginal means were then used to estimate the difference in functional connectome similarity within and between participants using pairwise contrasts, as we illustrated in the introduction. Differences were estimated for (1) the overall difference in similarity within and between participants, which we term the *main effect*; (2) the difference in similarity within and between participants for each level of one predictor (e.g., within session similarity) while averaging over levels of the other predictor (e.g., within and between state similarity); (3) the difference in similarity within and between participants for the unique combinations within and between session and state. For any of these contrasts: A difference of zero indicated equal amounts of functional connectome similarity within and between participants, a positive difference indicated more similarity within than between participants, and a negative difference indicated more similarity between than within participants.
+After fitting the model, we constructed a reference grid using estimated marginal means [@lenthetal_EmmeansEstimatedMarginal_2022; @searleetal_PopulationMarginalMeans_1980], which were backtransformed to the response scale [0-1] for interpretability. The estimated marginal means were then used to estimate the difference in functional connectome similarity within and between participants using pairwise contrasts, as we illustrated in the introduction. Differences were estimated for (1) the overall difference in similarity within and between participants, which we term the *main effect*; (2) the difference in similarity within and between participants for each level of one predictor (e.g., within session similarity) while averaging over levels of the other predictor (e.g., within and between state similarity); (3) the difference in similarity within and between participants for the unique combinations within and between session and state. For any of these contrasts: A difference of zero indicated equal amounts of functional connectome similarity within and between participants, a positive difference indicated more similarity within than between participants, and a negative difference indicated more similarity between than within participants.
#### Interpreting group-level contrasts
We use interval plots to report the group-level contrasts estimating the difference in functional connectome similarity within and between participants at various levels of the session and state predictors. To interpret these contrasts, we report compatibility intervals (CIs), which are equivalent to classical confidence intervals [@amrheingreenland_DiscussPracticalImportance_2022]. An $x\%$ compatibility interval shows the effect sizes most compatible with our data, given the correctness of the set of procedural and statistical assumptions used to compute the interval, which we call the *background model* [see @amrheingreenland_DiscussPracticalImportance_2022]. As discussed by @amrheinetal_ScientistsRiseStatistical_2019, there are two important points to keep in mind when interpreting compatibility intervals. First, although interval shows the values most compatible with our data, it does not mean values outside the interval are incompatible; they are merely less compatible, given our background model. Indeed, there are many values outside the interval that will also be compatible with our data, which have not been included due to (known and unknown) assumptions that we have not modelled [@amrheinetal_InferentialStatisticsDescriptive_2019]. Second, the point estimate and values near it are more compatible with our data than values near the limits of the interval; we use both 95% and 66% compatibility intervals in our plots to highlight this. Additionally, given that the correctness of *all* assumptions used to compute our estimates was doubtful (e.g., we knowingly misspecified the random effects of the reduced mixed beta regression models, the absence of measurement errors in our procedure is unlikely, etc.), we emphasize that our estimates likely understate uncertainty about the effect sizes most compatible with our data, and should not be taken as showing some general truth [@amrheinetal_InferentialStatisticsDescriptive_2019].
-In addition, to assist readers unfamiliar with the analytic approach we have taken here, for each contrast we also report the observed $p$-value corresponding to a targeted hypothesis test of zero difference in functional connectome similarity within and between participants. The $p$-value takes values between 0 and 1, and provides a measure of the degree of statistical compatibility between the targeted hypothesis and our data, given the background model [@greenland_ValidPValuesBehave_2019; @greenlandetal_StatisticalTestsValues_2016]. A value of 0 indicates complete incompatibility between the targeted hypothesis and our data, and a value of 1 indicates no incompatibility between the targeted hypothesis and our data apparent from the test [@greenland_ValidPValuesBehave_2019]. Additionally, just as we discussed for compatibility intervals, we emphasize that each $p$-value refers to a targeted hypothesis test of *every* assumption used to compute the test, including procedural and statistical assumptions as well as the targeted hypothesis of zero difference [@greenland_InvitedCommentaryNeed_2017]. Thus, a small $p$-value only suggests that there may be a problem with at least one assumption used to compute the test, without indicating which one; and a large $p$-value only suggests that the test did not detect a problem, without indicating whether this was because there were no problems or because the test was insensitive to them [@amrheinetal_InferentialStatisticsDescriptive_2019; @greenland_InvitedCommentaryNeed_2017]. Given that the correctness of *all* assumptions used to compute our tests was doubtful, we emphasize that the observed $p$-values corresponding to each of our targeted hypothesis tests merely describe the relation of our models to our data, and should not be taken as providing generalizable truths about the veracity of our scientific hypotheses [@amrheinetal_InferentialStatisticsDescriptive_2019].
+In addition, to assist readers unfamiliar with the analytic approach we have taken here, for each contrast we also report the observed $p$-value corresponding to a targeted hypothesis test of zero difference in functional connectome similarity within and between participants. Just as we discussed for compatibility intervals, we emphasize that each $p$-value merely provides a measure of the degree of statistical compatibility between the targeted hypothesis and our data, given the background model---referring to a targeted hypothesis test of *every* assumption used to compute the test, including procedural and statistical assumptions as well as the targeted hypothesis of zero difference [@greenland_InvitedCommentaryNeed_2017; @greenland_ValidPValuesBehave_2019; @greenlandetal_StatisticalTestsValues_2016]. Thus, a small $p$-value only suggests that there may be a problem with at least one assumption used to compute the test, without indicating which one; and a large $p$-value only suggests that the test did not detect a problem, without indicating whether this was because there were no problems or because the test was insensitive to them [@amrheinetal_InferentialStatisticsDescriptive_2019; @greenland_InvitedCommentaryNeed_2017].
### Individual-level functional connectome similarity contrasts
diff --git a/manuscripts/child-documents/results.Rmd b/manuscripts/child-documents/results.Rmd
index 64bcdf2..dddc6dc 100644
--- a/manuscripts/child-documents/results.Rmd
+++ b/manuscripts/child-documents/results.Rmd
@@ -1,6 +1,6 @@
# Results
-The results section is split into three parts. In the first part, we provide a brief summary of our functional connectivity analyses, which serves to demonstrate that both of our functional connectivity metrics gave reasonable results in each frequency band that were in agreement with previous published published work. In the second part, we investigate differences in functional connectome similarity within and between participants at the group-level.[^7] In the final part, we investigate outcome variability in differences in functional connectome similarity within and between participants at the individual-level.
+The results section is split into three parts. In the first part, we provide a brief summary of our functional connectivity analyses, which serves to demonstrate that both of our functional connectivity metrics gave reasonable results in each frequency band that were in agreement with previous published work. In the second part, we investigate differences in functional connectome similarity within and between participants at the group-level.[^7] In the final part, we investigate outcome variability in differences in functional connectome similarity within and between participants at the individual-level.
## Functional connectivity analyses
@@ -48,9 +48,9 @@ knitr::opts_chunk$set(fig.height = 5.445434)
Figure \@ref(fig:phase-similarity-plots-delta)A shows the connectivity profiles for all phase coupling functional connectomes in the delta band, organized by participant and recording. Plots and summary statistics of the distribution of coupling magnitudes across all recordings and EEG channel pairs are shown above in Figure \@ref(fig:connectivity-histograms-figure) and Table \@ref(tab:connectivity-summary-table), respectively.
-Figure \@ref(fig:phase-similarity-plots-delta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the delta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-delta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:phase-similarity-table-delta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the delta band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+Figure \@ref(fig:phase-similarity-plots-delta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the delta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-delta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:phase-similarity-table-delta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the delta band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
-These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-delta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained consistent between the reduced and maximal models of functional connectome similarity when the phase lag index was estimated using the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-delta)), but with slightly more uncertainty in the estimates. The maximal model of functional connectome similarity did not converge when the phase lag index was estimated using the multitaper method (Table A\@ref(tab:convergence-check-table)).
+These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-delta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained consistent between the reduced and maximal models of functional connectome similarity when the phase lag index was estimated using the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-delta)), but with slightly more uncertainty in the estimates. The maximal model of functional connectome similarity did not converge when the phase lag index was estimated using the multitaper method (Table A\@ref(tab:convergence-check-table)).
\newpage
@@ -77,9 +77,9 @@ targets::tar_read(phase_similarity_contrasts_table_nhst_delta) |>
Figure \@ref(fig:phase-similarity-plots-theta)A shows the connectivity profiles for all phase coupling functional connectomes in the theta band, organized by participant and recording. Plots and summary statistics of the distribution of coupling magnitudes across all recordings and EEG channel pairs are shown above in Figure \@ref(fig:connectivity-histograms-figure) and Table \@ref(tab:connectivity-summary-table), respectively.
-Figure \@ref(fig:phase-similarity-plots-theta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the theta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-theta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were at most small. Table \@ref(tab:phase-similarity-table-theta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the theta band was differentiated between individuals across contexts, but with only a slight influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+Figure \@ref(fig:phase-similarity-plots-theta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the theta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-theta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were at most small. Table \@ref(tab:phase-similarity-table-theta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the theta band was differentiated between individuals across contexts, but with only a slight influence of individual-dependent factors over and above the influence of stable group-dependent factors.
-These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-theta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally of similar magnitudes, with the exception of the within sessions and between states contrast which was visibly smaller. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-theta)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-theta)), but with slightly more uncertainty in the estimates.
+These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-theta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally of similar magnitudes, with the exception of the within sessions and between states contrast which was visibly smaller. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-theta)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-theta)), but with slightly more uncertainty in the estimates.
\newpage
@@ -108,9 +108,9 @@ Figure \@ref(fig:phase-similarity-plots-alpha)A shows the connectivity profiles
Figure \@ref(fig:phase-similarity-plots-alpha)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the alpha band. There were no visually obvious patterns of similarity consistent across participants, but there were some individual differences worth highlighting: Participant P03 showed high similarity across sessions and states, corresponding to an individual effect; Participants P14, P17, and P19 showed moderate to high similarity in the eyes open state across sessions, and moderate similarity between participants, corresponding to a partial state effect; and participants P07, P08, P11, and P20 showed moderate similarity across sessions and states with themselves and each other, corresponding to a partial group effect. The remaining participants showed no distinctive patterns. Additionally, unlike the delta, theta, beta, and gamma bands, the amount of similarity between participants was generally moderate and considerably more variable; not consistently high.
-However, despite the presence of more visually apparent individual effects, there was still a strong group effect. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-alpha)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were still at most small. Table \@ref(tab:phase-similarity-table-alpha) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with small modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the alpha band was differentiated between individuals across contexts, but with only a small influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+However, despite the presence of more visually apparent individual effects, there was still a strong group effect. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-alpha)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were still at most small. Table \@ref(tab:phase-similarity-table-alpha) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with small modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the alpha band was differentiated between individuals across contexts, but with only a small influence of individual-dependent factors over and above the influence of stable group-dependent factors.
-These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-alpha)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; and (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; however, differences in functional connectome similarity were generally of similar magnitudes. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-alpha)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-alpha)), but with slightly more uncertainty in the estimates.
+These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-alpha)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; and (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; however, differences in functional connectome similarity were generally of similar magnitudes. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-alpha)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-alpha)), but with slightly more uncertainty in the estimates.
\newpage
@@ -137,9 +137,9 @@ targets::tar_read(phase_similarity_contrasts_table_nhst_alpha) |>
Figure \@ref(fig:phase-similarity-plots-beta)A shows the connectivity profiles for all phase coupling functional connectomes in the beta band, organized by participant and recording. Plots and summary statistics of the distribution of coupling magnitudes across all recordings and EEG channel pairs are shown above in Figure \@ref(fig:connectivity-histograms-figure) and Table \@ref(tab:connectivity-summary-table), respectively.
-Figure \@ref(fig:phase-similarity-plots-beta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the beta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-beta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were at most small. Table \@ref(tab:phase-similarity-table-beta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the beta band was differentiated between individuals across contexts, but with only a slight influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+Figure \@ref(fig:phase-similarity-plots-beta)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the beta band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-beta)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, were at most small. Table \@ref(tab:phase-similarity-table-beta) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the beta band was differentiated between individuals across contexts, but with only a slight influence of individual-dependent factors over and above the influence of stable group-dependent factors.
-These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-beta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-beta)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-beta)), but with slightly more uncertainty in the estimates.
+These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-beta)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-beta)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-beta)), but with slightly more uncertainty in the estimates.
\newpage
@@ -166,9 +166,9 @@ targets::tar_read(phase_similarity_contrasts_table_nhst_beta) |>
Figure \@ref(fig:phase-similarity-plots-gamma)A shows the connectivity profiles for all phase coupling functional connectomes in the gamma band, organized by participant and recording. Plots and summary statistics of the distribution of coupling magnitudes across all recordings and EEG channel pairs are shown above in Figure \@ref(fig:connectivity-histograms-figure) and Table \@ref(tab:connectivity-summary-table), respectively.
-Figure \@ref(fig:phase-similarity-plots-gamma)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the gamma band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-gamma)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:phase-similarity-table-gamma) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the gamma band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+Figure \@ref(fig:phase-similarity-plots-gamma)B shows the functional connectome similarity estimates between all pairs of phase coupling functional connectomes in the gamma band. There was a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state. This was supported by the group-level contrast results in Figure \@ref(fig:phase-similarity-plots-gamma)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:phase-similarity-table-gamma) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with slight modulations by session and state. Jointly, these results suggest that, on average, the phase coupling dynamics of underlying global network activity in the gamma band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
-These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-gamma)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained largely consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-gamma)) and the Hilbert transform method (Figures A\@ref(fig:phase-similarity-plots-hilbert-maximal-gamma)), but with slightly more uncertainty in the estimates. This increased uncertainty resulted in the direction of the between sessions, between sessions and within states, and between sessions and states contrasts to become unresolved. However, the difference in interpretation between the maximal model and reduced model results here was minor: The maximal model simply requires the qualification that the differences in functional connectome similarity within and between participants most compatible with our data for these contrasts, given the background model, also included the possibility of (essentially) zero difference.
+These results remained largely consistent when the phase lag index was estimated using the Hilbert transform method instead of the multitaper method (Figure A\@ref(fig:phase-similarity-plots-hilbert-gamma)), with some minor differences: (1) phase coupling was generally weaker, but individual patterns of coupling were similar or the same; (2) functional connectome similarity was generally higher, but patterns of similarity were similar or the same; and (3) differences in functional connectome similarity were generally smaller, but were still positive with a similar pattern of variation across contexts. Likewise, the contrast results remained largely consistent between the reduced and maximal mixed beta regression models of functional connectome similarity when the phase lag index was estimated using both the multitaper method (Figure A\@ref(fig:phase-similarity-plots-maximal-gamma)) and the Hilbert transform method (Figure A\@ref(fig:phase-similarity-plots-hilbert-maximal-gamma)), but with slightly more uncertainty in the estimates. This increased uncertainty resulted in the direction of the between sessions, between sessions and within states, and between sessions and states contrasts to become unresolved. However, the difference in interpretation between the maximal model and reduced model results here was minor: The maximal model simply requires the qualification that the differences in functional connectome similarity within and between participants most compatible with our data for these contrasts, given the background model, also included the possibility of (essentially) zero difference.
\newpage
@@ -199,7 +199,7 @@ Figure \@ref(fig:amplitude-similarity-plots-alpha)A shows the connectivity profi
Figure \@ref(fig:amplitude-similarity-plots-alpha)B shows the functional connectome similarity estimates between all pairs of amplitude coupling functional connectomes in the alpha band. Interestingly---although perhaps not surprisingly given the spatial correspondence between phase coupling and amplitude coupling functional connectomes (Figure \@ref(fig:illustrative-connectomes-figure))---similar patterns of individual differences in functional connectome similarity were observed here in relation to the phase coupling similarities observed in the alpha band (Figure \@ref(fig:phase-similarity-plots-alpha)B). However, there was also a visually obvious group effect, with high similarity between functional connectomes regardless of participant, session, or state.
-This was supported by the group-level contrast results in Figure \@ref(fig:amplitude-similarity-plots-alpha)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:amplitude-similarity-table-alpha) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most small) positive differences in functional connectome similarity can be attributed to individual-dependent factors with small modulations by session and state. Jointly, these results suggest that, on average, the amplitude coupling dynamics of underlying global network activity in the alpha band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
+This was supported by the group-level contrast results in Figure \@ref(fig:amplitude-similarity-plots-alpha)C, which showed that although functional connectomes were more similar within participants than between participants at all levels of the session and state predictors, on average, the effect sizes most compatible with our data, given the background model, ranged from practically nil to at most small. Table \@ref(tab:amplitude-similarity-table-alpha) shows the estimated marginal means and targeted hypothesis tests of zero difference within and between participants corresponding to each of these contrasts. Note that the between-participant functional connectome similarity means were stable across contexts, whereas the within-participant means were more variable, indicating that the (at most) small positive differences in functional connectome similarity can be attributed to individual-dependent factors with small modulations by session and state. Jointly, these results suggest that, on average, the amplitude coupling dynamics of underlying global network activity in the alpha band was differentiated between individuals across contexts, but with only a slight, negligible influence of individual-dependent factors over and above the influence of stable group-dependent factors.
The contrast results remained largely consistent between the reduced and maximal mixed beta regression models of functional connectome similarity, but with slightly more uncertainty in the estimates (Figure A\@ref(fig:amplitude-similarity-plots-maximal-alpha)). This increased uncertainty resulted in the direction of the between sessions and states contrast to become unresolved. However, the difference in interpretation between the maximal model and reduced model results here was minor: The maximal model simply requires the qualification that the differences in functional connectome similarity within and between participants most compatible with our data for this contrast, given the background model, also included the possibility of (essentially) zero difference.
diff --git a/manuscripts/references.json b/manuscripts/references.json
index ae1accd..6b12655 100644
--- a/manuscripts/references.json
+++ b/manuscripts/references.json
@@ -2,7 +2,6 @@
{"id":"abdiherve_RVCoefficientCongruence_2007","author":[{"literal":"Abdi, Hervé"}],"citation-key":"abdiherve_RVCoefficientCongruence_2007","issued":{"date-parts":[["2007"]]},"title":"RV Coefficient and Congruence Coefficient","type":"document"},
{"id":"ablinetal_FasterICAOrthogonal_2017","abstract":"Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy over the set of orthogonal matrices, which can quickly separate both super- and sub-Gaussian signals. It returns the same set of sources as the widely used FastICA algorithm. Through numerical experiments, we show that our method is faster and more robust than FastICA on real data.","accessed":{"date-parts":[["2021",11,11]]},"author":[{"family":"Ablin","given":"Pierre"},{"family":"Cardoso","given":"Jean-François"},{"family":"Gramfort","given":"Alexandre"}],"citation-key":"ablinetal_FasterICAOrthogonal_2017","container-title":"arXiv:1711.10873 [stat]","issued":{"date-parts":[["2017",11,29]]},"source":"arXiv.org","title":"Faster ICA under orthogonal constraint","type":"article-journal","URL":"http://arxiv.org/abs/1711.10873"},
{"id":"ablinetal_FasterIndependentComponent_2018","abstract":"Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian independent sources. The maximization of the corresponding likelihood is a challenging problem if it has to be completed quickly and accurately on large sets of real data. We introduce the Preconditioned ICA for Real Data (Picard) algorithm, which is a relative L-BFGS algorithm preconditioned with sparse Hessian approximations. Extensive numerical comparisons to several algorithms of the same class demonstrate the superior performance of the proposed technique, especially on real data, for which the ICA model does not necessarily hold.","accessed":{"date-parts":[["2021",11,11]]},"author":[{"family":"Ablin","given":"Pierre"},{"family":"Cardoso","given":"Jean-François"},{"family":"Gramfort","given":"Alexandre"}],"citation-key":"ablinetal_FasterIndependentComponent_2018","container-title":"IEEE Transactions on Signal Processing","container-title-short":"IEEE Trans. Signal Process.","DOI":"10.1109/TSP.2018.2844203","ISSN":"1053-587X, 1941-0476","issue":"15","issued":{"date-parts":[["2018",8,1]]},"page":"4040-4049","source":"arXiv.org","title":"Faster independent component analysis by preconditioning with Hessian approximations","type":"article-journal","URL":"http://arxiv.org/abs/1706.08171","volume":"66"},
- {"id":"abreuetal_OptimizingEEGSource_2022","abstract":"Abstract\n \n Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability\n P\n (model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Abreu","given":"Rodolfo"},{"family":"Soares","given":"Júlia F."},{"family":"Lima","given":"Ana Cláudia"},{"family":"Sousa","given":"Lívia"},{"family":"Batista","given":"Sónia"},{"family":"Castelo-Branco","given":"Miguel"},{"family":"Duarte","given":"João Valente"}],"citation-key":"abreuetal_OptimizingEEGSource_2022","container-title":"Brain Topography","container-title-short":"Brain Topogr","DOI":"10.1007/s10548-022-00891-3","ISSN":"0896-0267, 1573-6792","issue":"3","issued":{"date-parts":[["2022",5]]},"language":"en","page":"282-301","source":"DOI.org (Crossref)","title":"Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors","type":"article-journal","URL":"https://link.springer.com/10.1007/s10548-022-00891-3","volume":"35"},
{"id":"adrianmathews_BergerRhythmPotential_1934","accessed":{"date-parts":[["2021",10,20]]},"author":[{"family":"Adrian","given":"E. D."},{"family":"Mathews","given":"B. H. C."}],"citation-key":"adrianmathews_BergerRhythmPotential_1934","container-title":"Brain","container-title-short":"Brain","DOI":"10.1093/brain/57.4.355","ISSN":"0006-8950","issue":"4","issued":{"date-parts":[["1934",12,1]]},"page":"355-385","source":"Silverchair","title":"The Berger Rhythm: Potential changes from the occipital lobes in man","title-short":"THE BERGER RHYTHM","type":"article-journal","URL":"https://doi.org/10.1093/brain/57.4.355","volume":"57"},
{"id":"akalinacarmakeig_EffectsForwardModel_2013","abstract":"Abstract\n \n Subject-specific four-layer boundary element method (BEM) electrical forward head models for four participants, generated from magnetic resonance (MR) head images using NFT (\n www.sccn.ucsd.edu/wiki/NFT\n ), were used to simulate electroencephalographic (EEG) scalp potentials at 256 recorded electrode positions produced by single current dipoles of a 3-D grid in brain space. Locations of these dipoles were then estimated using gradient descent within five template head models fit to the electrode positions. These were: a spherical model, three-layer and four-layer BEM head models based on the Montreal Neurological Institute (MNI) template head image, and these BEM models warped to the recorded electrode positions. Smallest localization errors (4.1–6.2 mm, medians) were obtained using the electrode-position warped four-layer BEM models, with largest localization errors (~20 mm) for most basal brain locations. When we increased the brain-to-skull conductivity ratio assumed in the template model scalp projections from the simulated value (25:1) to a higher value (80:1) used in earlier studies, the estimated dipole locations moved outwards (12.4 mm, median). We also investigated the effects of errors in co-registering the electrode positions, of reducing electrode counts, and of adding a fifth, isotropic white matter layer to one individual head model. Results show that when individual subject MR head images are not available to construct subject-specific head models, accurate EEG source localization should employ a four- or five-layer BEM template head model incorporating an accurate skull conductivity estimate and warped to 64 or more accurately 3-D measured and co-registered electrode positions.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Akalin Acar","given":"Zeynep"},{"family":"Makeig","given":"Scott"}],"citation-key":"akalinacarmakeig_EffectsForwardModel_2013","container-title":"Brain Topography","container-title-short":"Brain Topogr","DOI":"10.1007/s10548-012-0274-6","ISSN":"0896-0267, 1573-6792","issue":"3","issued":{"date-parts":[["2013",7]]},"language":"en","page":"378-396","source":"DOI.org (Crossref)","title":"Effects of Forward Model Errors on EEG Source Localization","type":"article-journal","URL":"https://link.springer.com/10.1007/s10548-012-0274-6","volume":"26"},
{"id":"akamkullmann_OscillationsFilteringNetworks_2010","accessed":{"date-parts":[["2023",7,6]]},"author":[{"family":"Akam","given":"Thomas"},{"family":"Kullmann","given":"Dimitri M."}],"citation-key":"akamkullmann_OscillationsFilteringNetworks_2010","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2010.06.019","ISSN":"08966273","issue":"2","issued":{"date-parts":[["2010",7]]},"language":"en","page":"308-320","source":"DOI.org (Crossref)","title":"Oscillations and Filtering Networks Support Flexible Routing of Information","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627310004770","volume":"67"},
@@ -11,11 +10,8 @@
{"id":"amrheingreenland_DiscussPracticalImportance_2022","abstract":"It has long been argued that we need to consider much more than an observed point estimate and a p-value to understand statistical results. One of the most persistent misconceptions about p-values is that they are necessarily calculated assuming a null hypothesis of no effect is true. Instead, p-values can and should be calculated for multiple hypothesized values for the effect size. For example, a p-value function allows us to visualize results continuously by examining how the p-value varies as we move across possible effect sizes. For more focused discussions, a 95% confidence interval shows the subset of possible effect sizes that have p-values larger than 0.05 as calculated from the same data and the same background statistical assumptions. In this sense a confidence interval can be taken as showing the effect sizes that are most compatible with the data, given the assumptions, and thus may be better termed a compatibility interval. The question that should then be asked is whether any or all of the effect sizes within the interval are substantial enough to be of practical importance.","accessed":{"date-parts":[["2022",7,5]]},"author":[{"family":"Amrhein","given":"Valentin"},{"family":"Greenland","given":"Sander"}],"citation-key":"amrheingreenland_DiscussPracticalImportance_2022","container-title":"Journal of Information Technology","container-title-short":"Journal of Information Technology","DOI":"10.1177/02683962221105904","ISSN":"0268-3962","issued":{"date-parts":[["2022",5,24]]},"language":"en","page":"02683962221105904","source":"SAGE Journals","title":"Discuss practical importance of results based on interval estimates and p-value functions, not only on point estimates and null p-values","type":"article-journal","URL":"https://doi.org/10.1177/02683962221105904"},
{"id":"andersonetal_ReproducibilitySingleSubjectFunctional_2011","abstract":"BACKGROUND AND PURPOSE: Measurements of resting-state functional connectivity have increasingly been used for characterization of neuropathologic and neurodevelopmental populations. We collected data to characterize how much imaging time is necessary to obtain reproducible quantitative functional connectivity measurements needed for a reliable single-subject diagnostic test.\nMATERIALS AND METHODS: We obtained 100 five-minute BOLD scans on a single subject, divided into 10 sessions of 10 scans each, with the subject at rest or while watching video clips of cartoons. These data were compared with resting-state BOLD scans from 36 healthy control subjects by evaluating the correlation between each pair of 64 small spheric regions of interest obtained from a published functional brain parcellation.\nRESULTS: Single-subject and group data converged to reliable estimates of individual and population connectivity values proportional to 1 / sqrt(n). Dramatic improvements in reliability were seen by using ≤25 minutes of imaging time, with smaller improvements for additional time. Functional connectivity “fingerprints” for the individual and population began diverging at approximately 15 minutes of imaging time, with increasing reliability even at 4 hours of imaging time. Twenty-five minutes of BOLD imaging time was required before any individual connections could reliably discriminate an individual from a group of healthy control subjects. A classifier discriminating scans during which our subject was resting or watching cartoons was 95% accurate at 10 minutes and 100% accurate at 15 minutes of imaging time.\nCONCLUSIONS: An individual subject and control population converged to reliable different functional connectivity profiles that were task-modulated and could be discriminated with sufficient imaging time.","accessed":{"date-parts":[["2021",11,4]]},"author":[{"family":"Anderson","given":"J. S."},{"family":"Ferguson","given":"M. A."},{"family":"Lopez-Larson","given":"M."},{"family":"Yurgelun-Todd","given":"D."}],"citation-key":"andersonetal_ReproducibilitySingleSubjectFunctional_2011","container-title":"American Journal of Neuroradiology","DOI":"10.3174/ajnr.A2330","ISSN":"0195-6108, 1936-959X","issue":"3","issued":{"date-parts":[["2011",3,1]]},"language":"en","license":"Copyright © American Society of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org","page":"548-555","PMID":"21273356","publisher":"American Journal of Neuroradiology","section":"Functional","source":"www.ajnr.org","title":"Reproducibility of Single-Subject Functional Connectivity Measurements","type":"article-journal","URL":"http://www.ajnr.org/content/32/3/548","volume":"32"},
{"id":"aruetal_UntanglingCrossfrequencyCoupling_2015","accessed":{"date-parts":[["2022",8,24]]},"author":[{"family":"Aru","given":"Juhan"},{"family":"Aru","given":"Jaan"},{"family":"Priesemann","given":"Viola"},{"family":"Wibral","given":"Michael"},{"family":"Lana","given":"Luiz"},{"family":"Pipa","given":"Gordon"},{"family":"Singer","given":"Wolf"},{"family":"Vicente","given":"Raul"}],"citation-key":"aruetal_UntanglingCrossfrequencyCoupling_2015","container-title":"Current Opinion in Neurobiology","container-title-short":"Current Opinion in Neurobiology","DOI":"10.1016/j.conb.2014.08.002","ISSN":"09594388","issued":{"date-parts":[["2015",4]]},"language":"en","page":"51-61","source":"DOI.org (Crossref)","title":"Untangling cross-frequency coupling in neuroscience","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0959438814001640","volume":"31"},
- {"id":"arzate-menaetal_StationaryEEGPattern_2022","accessed":{"date-parts":[["2023",8,10]]},"author":[{"family":"Arzate-Mena","given":"J. Daniel"},{"family":"Abela","given":"Eugenio"},{"family":"Olguín-Rodríguez","given":"Paola V."},{"family":"Ríos-Herrera","given":"Wady"},{"family":"Alcauter","given":"Sarael"},{"family":"Schindler","given":"Kaspar"},{"family":"Wiest","given":"Roland"},{"family":"Müller","given":"Markus F."},{"family":"Rummel","given":"Christian"}],"citation-key":"arzate-menaetal_StationaryEEGPattern_2022","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2021.118763","ISSN":"10538119","issued":{"date-parts":[["2022",2]]},"language":"en","page":"118763","source":"DOI.org (Crossref)","title":"Stationary EEG pattern relates to large-scale resting state networks – An EEG-fMRI study connecting brain networks across time-scales","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811921010351","volume":"246"},
- {"id":"asadzadehetal_SystematicReviewEEG_2020","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Asadzadeh","given":"Shiva"},{"family":"Yousefi Rezaii","given":"Tohid"},{"family":"Beheshti","given":"Soosan"},{"family":"Delpak","given":"Azra"},{"family":"Meshgini","given":"Saeed"}],"citation-key":"asadzadehetal_SystematicReviewEEG_2020","container-title":"Journal of Neuroscience Methods","container-title-short":"Journal of Neuroscience Methods","DOI":"10.1016/j.jneumeth.2020.108740","ISSN":"01650270","issued":{"date-parts":[["2020",6]]},"language":"en","page":"108740","source":"DOI.org (Crossref)","title":"A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0165027020301631","volume":"339"},
{"id":"azevedoetal_EqualNumbersNeuronal_2009","abstract":"The human brain is often considered to be the most cognitively capable among mammalian brains and to be much larger than expected for a mammal of our body size. Although the number of neurons is generally assumed to be a determinant of computational power, and despite the widespread quotes that the human brain contains 100 billion neurons and ten times more glial cells, the absolute number of neurons and glial cells in the human brain remains unknown. Here we determine these numbers by using the isotropic fractionator and compare them with the expected values for a human-sized primate. We find that the adult male human brain contains on average 86.1 +/- 8.1 billion NeuN-positive cells (\"neurons\") and 84.6 +/- 9.8 billion NeuN-negative (\"nonneuronal\") cells. With only 19% of all neurons located in the cerebral cortex, greater cortical size (representing 82% of total brain mass) in humans compared with other primates does not reflect an increased relative number of cortical neurons. The ratios between glial cells and neurons in the human brain structures are similar to those found in other primates, and their numbers of cells match those expected for a primate of human proportions. These findings challenge the common view that humans stand out from other primates in their brain composition and indicate that, with regard to numbers of neuronal and nonneuronal cells, the human brain is an isometrically scaled-up primate brain.","author":[{"family":"Azevedo","given":"Frederico A. C."},{"family":"Carvalho","given":"Ludmila R. B."},{"family":"Grinberg","given":"Lea T."},{"family":"Farfel","given":"José Marcelo"},{"family":"Ferretti","given":"Renata E. L."},{"family":"Leite","given":"Renata E. P."},{"family":"Jacob Filho","given":"Wilson"},{"family":"Lent","given":"Roberto"},{"family":"Herculano-Houzel","given":"Suzana"}],"citation-key":"azevedoetal_EqualNumbersNeuronal_2009","container-title":"The Journal of Comparative Neurology","container-title-short":"J Comp Neurol","DOI":"10.1002/cne.21974","ISSN":"1096-9861","issue":"5","issued":{"date-parts":[["2009",4,10]]},"language":"eng","page":"532-541","PMID":"19226510","source":"PubMed","title":"Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain","type":"article-journal","volume":"513"},
{"id":"babadibrown_ReviewMultitaperSpectral_2014","abstract":"Nonparametric spectral estimation is a widely used technique in many applications ranging from radar and seismic data analysis to electroencephalography (EEG) and speech processing. Among the techniques that are used to estimate the spectral representation of a system based on finite observations, multitaper spectral estimation has many important optimality properties, but is not as widely used as it possibly could be. We give a brief overview of the standard nonparametric spectral estimation theory and the multitaper spectral estimation, and give two examples from EEG analyses of anesthesia and sleep.","author":[{"family":"Babadi","given":"Behtash"},{"family":"Brown","given":"Emery N."}],"citation-key":"babadibrown_ReviewMultitaperSpectral_2014","container-title":"IEEE transactions on bio-medical engineering","container-title-short":"IEEE Trans Biomed Eng","DOI":"10.1109/TBME.2014.2311996","ISSN":"1558-2531","issue":"5","issued":{"date-parts":[["2014",5]]},"language":"eng","page":"1555-1564","PMID":"24759284","source":"PubMed","title":"A review of multitaper spectral analysis","type":"article-journal","volume":"61"},
- {"id":"backmanetal_SeasonTimeDay_2016","accessed":{"date-parts":[["2023",6,28]]},"author":[{"family":"Bäckman","given":"Sari"},{"family":"Larjo","given":"Antti"},{"family":"Soikkeli","given":"Juha"},{"family":"Castrén","given":"Johanna"},{"family":"Ihalainen","given":"Jarkko"},{"family":"Syrjälä","given":"Martti"}],"citation-key":"backmanetal_SeasonTimeDay_2016","container-title":"Transfusion","container-title-short":"Transfusion","DOI":"10.1111/trf.13578","ISSN":"00411132","issue":"6","issued":{"date-parts":[["2016",6]]},"language":"en","page":"1287-1294","source":"DOI.org (Crossref)","title":"Season and time of day affect capillary blood hemoglobin level and low hemoglobin deferral in blood donors: analysis in a national blood bank: SEASON, TIME OF DAY, AND BLOOD DONOR Hb","title-short":"Season and time of day affect capillary blood hemoglobin level and low hemoglobin deferral in blood donors","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1111/trf.13578","volume":"56"},
{"id":"barchetal_FunctionHumanConnectome_2013","abstract":"The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.","author":[{"family":"Barch","given":"Deanna M."},{"family":"Burgess","given":"Gregory C."},{"family":"Harms","given":"Michael P."},{"family":"Petersen","given":"Steven E."},{"family":"Schlaggar","given":"Bradley L."},{"family":"Corbetta","given":"Maurizio"},{"family":"Glasser","given":"Matthew F."},{"family":"Curtiss","given":"Sandra"},{"family":"Dixit","given":"Sachin"},{"family":"Feldt","given":"Cindy"},{"family":"Nolan","given":"Dan"},{"family":"Bryant","given":"Edward"},{"family":"Hartley","given":"Tucker"},{"family":"Footer","given":"Owen"},{"family":"Bjork","given":"James M."},{"family":"Poldrack","given":"Russ"},{"family":"Smith","given":"Steve"},{"family":"Johansen-Berg","given":"Heidi"},{"family":"Snyder","given":"Abraham Z."},{"family":"Van Essen","given":"David C."},{"literal":"WU-Minn HCP Consortium"}],"citation-key":"barchetal_FunctionHumanConnectome_2013","container-title":"NeuroImage","container-title-short":"Neuroimage","DOI":"10.1016/j.neuroimage.2013.05.033","ISSN":"1095-9572","issued":{"date-parts":[["2013",10,15]]},"language":"eng","page":"169-189","PMCID":"PMC4011498","PMID":"23684877","source":"PubMed","title":"Function in the human connectome: task-fMRI and individual differences in behavior","title-short":"Function in the human connectome","type":"article-journal","volume":"80"},
{"id":"barretal_RandomEffectsStructure_2013","accessed":{"date-parts":[["2024",1,2]]},"author":[{"family":"Barr","given":"Dale J."},{"family":"Levy","given":"Roger"},{"family":"Scheepers","given":"Christoph"},{"family":"Tily","given":"Harry J."}],"citation-key":"barretal_RandomEffectsStructure_2013","container-title":"Journal of Memory and Language","container-title-short":"Journal of Memory and Language","DOI":"10.1016/j.jml.2012.11.001","ISSN":"0749596X","issue":"3","issued":{"date-parts":[["2013",4]]},"language":"en","page":"255-278","source":"DOI.org (Crossref)","title":"Random effects structure for confirmatory hypothesis testing: Keep it maximal","title-short":"Random effects structure for confirmatory hypothesis testing","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0749596X12001180","volume":"68"},
{"id":"barrydeblasio_EEGDifferencesEyesclosed_2017","accessed":{"date-parts":[["2023",9,19]]},"author":[{"family":"Barry","given":"Robert J."},{"family":"De Blasio","given":"Frances M."}],"citation-key":"barrydeblasio_EEGDifferencesEyesclosed_2017","container-title":"Biological Psychology","container-title-short":"Biological Psychology","DOI":"10.1016/j.biopsycho.2017.09.010","ISSN":"03010511","issued":{"date-parts":[["2017",10]]},"language":"en","page":"293-304","source":"DOI.org (Crossref)","title":"EEG differences between eyes-closed and eyes-open resting remain in healthy ageing","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0301051117302454","volume":"129"},
@@ -60,11 +56,9 @@
{"id":"caoetal_DisturbedConnectivityEEG_2014","accessed":{"date-parts":[["2022",8,8]]},"author":[{"family":"Cao","given":"Rui"},{"family":"Wu","given":"Zheng"},{"family":"Li","given":"Haifang"},{"family":"Xiang","given":"Jie"},{"family":"Chen","given":"Junjie"}],"citation-key":"caoetal_DisturbedConnectivityEEG_2014","container-title":"Bio-Medical Materials and Engineering","DOI":"10.3233/BME-141112","ISSN":"09592989, 18783619","issue":"6","issued":{"date-parts":[["2014"]]},"page":"2927-2936","source":"DOI.org (Crossref)","title":"Disturbed Connectivity of EEG Functional Networks in Alcoholism: A Graph-Theoretic Analysis","title-short":"Disturbed Connectivity of EEG Functional Networks in Alcoholism","type":"article-journal","URL":"https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/BME-141112","volume":"24"},
{"id":"caoslobounov_AlterationCorticalFunctional_2010","accessed":{"date-parts":[["2022",8,8]]},"author":[{"family":"Cao","given":"C."},{"family":"Slobounov","given":"S."}],"citation-key":"caoslobounov_AlterationCorticalFunctional_2010","container-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","container-title-short":"IEEE Trans. Neural Syst. Rehabil. Eng.","DOI":"10.1109/TNSRE.2009.2027704","ISSN":"1534-4320, 1558-0210","issue":"1","issued":{"date-parts":[["2010",2]]},"page":"11-19","source":"DOI.org (Crossref)","title":"Alteration of Cortical Functional Connectivity as a Result of Traumatic Brain Injury Revealed by Graph Theory, ICA, and sLORETA Analyses of EEG Signals","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/5166505/","volume":"18"},
{"id":"caramazza_DrawingInferencesStructure_1986","abstract":"An analysis of the logic of valid inferences about the structure of normal cognitive processes from the study of impaired cognitive performance in brain-damaged patients is presented. The logic of inferences from group studies and single-case studies is compared. It is shown that given certain assumptions, only the single-case method allows valid inferences about the structure of cognitive systems from the analysis of impaired performance. It is also argued that although the single-case approach is not entirely problem-free, the difficulties encountered are relatively minor.","accessed":{"date-parts":[["2021",9,30]]},"author":[{"family":"Caramazza","given":"Alfonso"}],"citation-key":"caramazza_DrawingInferencesStructure_1986","container-title":"Brain and Cognition","container-title-short":"Brain and Cognition","DOI":"10.1016/0278-2626(86)90061-8","ISSN":"0278-2626","issue":"1","issued":{"date-parts":[["1986",1,1]]},"language":"en","page":"41-66","source":"ScienceDirect","title":"On drawing inferences about the structure of normal cognitive systems from the analysis of patterns of impaired performance: The case for single-patient studies","title-short":"On drawing inferences about the structure of normal cognitive systems from the analysis of patterns of impaired performance","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/0278262686900618","volume":"5"},
- {"id":"cespedes-villaretal_InfluencePatientSpecificHead_2020","abstract":"Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Céspedes-Villar","given":"Yohan"},{"family":"Martinez-Vargas","given":"Juan David"},{"family":"Castellanos-Dominguez","given":"G."}],"citation-key":"cespedes-villaretal_InfluencePatientSpecificHead_2020","container-title":"Computational and Mathematical Methods in Medicine","container-title-short":"Computational and Mathematical Methods in Medicine","DOI":"10.1155/2020/5076865","ISSN":"1748-670X, 1748-6718","issued":{"date-parts":[["2020",4,3]]},"language":"en","page":"1-15","source":"DOI.org (Crossref)","title":"Influence of Patient-Specific Head Modeling on EEG Source Imaging","type":"article-journal","URL":"https://www.hindawi.com/journals/cmmm/2020/5076865/","volume":"2020"},
{"id":"changglover_RelationshipRespirationEndtidal_2009","abstract":"A significant component of BOLD fMRI physiological noise is caused by variations in the depth and rate of respiration. It has previously been demonstrated that a breath-to-breath metric of respiratory variation (respiratory volume per time; RVT), computed from pneumatic belt measurements of chest expansion, has a strong linear relationship with resting-state BOLD signals across the brain. RVT is believed to capture breathing-induced changes in arterial CO(2), which is a cerebral vasodilator; indeed, separate studies have found that spontaneous fluctuations in end-tidal CO(2) (PETCO(2)) are correlated with BOLD signal time series. The present study quantifies the degree to which RVT and PETCO(2) measurements relate to one another and explain common aspects of the resting-state BOLD signal. It is found that RVT (particularly when convolved with a particular impulse response, the \"respiration response function\") is highly correlated with PETCO(2), and that both explain remarkably similar spatial and temporal BOLD signal variance across the brain. In addition, end-tidal O(2) is shown to be largely redundant with PETCO(2). Finally, the latency at which PETCO(2) and respiration belt measures are correlated with the time series of individual voxels is found to vary across the brain and may reveal properties of intrinsic vascular response delays.","author":[{"family":"Chang","given":"Catie"},{"family":"Glover","given":"Gary H."}],"citation-key":"changglover_RelationshipRespirationEndtidal_2009","container-title":"NeuroImage","container-title-short":"Neuroimage","DOI":"10.1016/j.neuroimage.2009.04.048","ISSN":"1095-9572","issue":"4","issued":{"date-parts":[["2009",10,1]]},"language":"eng","page":"1381-1393","PMCID":"PMC2721281","PMID":"19393322","source":"PubMed","title":"Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI","type":"article-journal","volume":"47"},
{"id":"chatrianetal_ModifiedNomenclature10_1988","abstract":"A modified nomenclature for designating the auxiliary electrodes of the 10% system is described.","author":[{"family":"Chatrian","given":"G. E."},{"family":"Lettich","given":"E."},{"family":"Nelson","given":"P. L."}],"citation-key":"chatrianetal_ModifiedNomenclature10_1988","container-title":"Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society","container-title-short":"J Clin Neurophysiol","ISSN":"0736-0258","issue":"2","issued":{"date-parts":[["1988",4]]},"language":"eng","page":"183-186","PMID":"3250964","source":"PubMed","title":"Modified nomenclature for the \"10%\" electrode system","type":"article-journal","volume":"5"},
{"id":"chatrianetal_TenPercentElectrode_1985","accessed":{"date-parts":[["2022",5,30]]},"author":[{"family":"Chatrian","given":"G. E."},{"family":"Lettich","given":"E."},{"family":"Nelson","given":"P. L."}],"citation-key":"chatrianetal_TenPercentElectrode_1985","container-title":"American Journal of EEG Technology","container-title-short":"American Journal of EEG Technology","DOI":"10.1080/00029238.1985.11080163","ISSN":"0002-9238, 2375-8600","issue":"2","issued":{"date-parts":[["1985",6]]},"language":"en","page":"83-92","source":"DOI.org (Crossref)","title":"Ten Percent Electrode System for Topographic Studies of Spontaneous and Evoked EEG Activities","type":"article-journal","URL":"https://www.tandfonline.com/doi/full/10.1080/00029238.1985.11080163","volume":"25"},
- {"id":"chauveauetal_EffectsSkullThickness_2004","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Chauveau","given":"Nicolas"},{"family":"Franceries","given":"Xavier"},{"family":"Doyon","given":"Bernard"},{"family":"Rigaud","given":"Bernard"},{"family":"Morucci","given":"Jean Pierre"},{"family":"Celsis","given":"Pierre"}],"citation-key":"chauveauetal_EffectsSkullThickness_2004","container-title":"Human Brain Mapping","container-title-short":"Hum. Brain Mapp.","DOI":"10.1002/hbm.10152","ISSN":"1065-9471, 1097-0193","issue":"2","issued":{"date-parts":[["2004",2]]},"language":"en","page":"86-97","source":"DOI.org (Crossref)","title":"Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1002/hbm.10152","volume":"21"},
{"id":"chellaetal_ImpactReferenceChoice_2016","accessed":{"date-parts":[["2023",7,3]]},"author":[{"family":"Chella","given":"Federico"},{"family":"Pizzella","given":"Vittorio"},{"family":"Zappasodi","given":"Filippo"},{"family":"Marzetti","given":"Laura"}],"citation-key":"chellaetal_ImpactReferenceChoice_2016","container-title":"Journal of Neural Engineering","container-title-short":"J. Neural Eng.","DOI":"10.1088/1741-2560/13/3/036016","ISSN":"1741-2560, 1741-2552","issue":"3","issued":{"date-parts":[["2016",6,1]]},"page":"036016","source":"DOI.org (Crossref)","title":"Impact of the reference choice on scalp EEG connectivity estimation","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1741-2560/13/3/036016","volume":"13"},
{"id":"cnuddeetal_IncreasedNeuralEfficiency_2021","abstract":"Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in this context. The neural changes that occur could be related to a shift from initially effortful performance that is supported by control-related processing, to efficient task performance that is supported by domain-specific processing. To investigate this, we replicated the British Lexicon Project, and had participants complete 16 h of LDT over several days. We recorded electroencephalography (EEG) at three intervals to track neural change during LDT performance and assessed event-related potentials and brain signal complexity. We found that response times decreased during LDT performance, and there was evidence of neural change through N170, P200, N400, and late positive component (LPC) amplitudes across the EEG sessions, which suggested a shift from control-related to domain-specific processing. We also found widespread complexity decreases alongside localized increases, suggesting that processing became more efficient with specific increases in processing flexibility. Together, these findings suggest that neural processing becomes more efficient and optimized to support prolonged LDT performance.","accessed":{"date-parts":[["2021",11,7]]},"author":[{"family":"Cnudde","given":"Kelsey"},{"family":"Hees","given":"Sophia","non-dropping-particle":"van"},{"family":"Brown","given":"Sage"},{"family":"Wijk","given":"Gwen","non-dropping-particle":"van der"},{"family":"Pexman","given":"Penny M."},{"family":"Protzner","given":"Andrea B."}],"citation-key":"cnuddeetal_IncreasedNeuralEfficiency_2021","container-title":"Entropy","DOI":"10.3390/e23030304","issue":"3","issued":{"date-parts":[["2021",3]]},"language":"en","license":"http://creativecommons.org/licenses/by/3.0/","number":"3","page":"304","publisher":"Multidisciplinary Digital Publishing Institute","source":"www.mdpi.com","title":"Increased Neural Efficiency in Visual Word Recognition: Evidence from Alterations in Event-Related Potentials and Multiscale Entropy","title-short":"Increased Neural Efficiency in Visual Word Recognition","type":"article-journal","URL":"https://www.mdpi.com/1099-4300/23/3/304","volume":"23"},
{"id":"cohen_AnalyzingNeuralTime_2014","author":[{"family":"Cohen","given":"M. X."}],"call-number":"QP363.3 .C633 2014","citation-key":"cohen_AnalyzingNeuralTime_2014","collection-title":"Issues in clinical and cognitive neuropsychology","event-place":"Cambridge, Massachusetts","ISBN":"978-0-262-01987-3","issued":{"date-parts":[["2014"]]},"language":"en","note":"Lectures (and code): https://mikexcohen.com/lectures.html\n\nForum: https://discuss.sincxpress.com\n\nData: https://mikexcohen.com/data/","number-of-pages":"578","publisher":"The MIT Press","publisher-place":"Cambridge, Massachusetts","source":"Library of Congress ISBN","title":"Analyzing neural time series data: theory and practice","title-short":"Analyzing neural time series data","type":"book"},
@@ -74,8 +68,6 @@
{"id":"coleetal_IntrinsicTaskEvokedNetwork_2014","accessed":{"date-parts":[["2023",6,9]]},"author":[{"family":"Cole","given":"Michael W."},{"family":"Bassett","given":"Danielle S."},{"family":"Power","given":"Jonathan D."},{"family":"Braver","given":"Todd S."},{"family":"Petersen","given":"Steven E."}],"citation-key":"coleetal_IntrinsicTaskEvokedNetwork_2014","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2014.05.014","ISSN":"08966273","issue":"1","issued":{"date-parts":[["2014",7]]},"language":"en","page":"238-251","source":"DOI.org (Crossref)","title":"Intrinsic and Task-Evoked Network Architectures of the Human Brain","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627314004000","volume":"83"},
{"id":"cribari-netozeileis_BetaRegression_2010","accessed":{"date-parts":[["2022",6,14]]},"author":[{"family":"Cribari-Neto","given":"Francisco"},{"family":"Zeileis","given":"Achim"}],"citation-key":"cribari-netozeileis_BetaRegression_2010","container-title":"Journal of Statistical Software","container-title-short":"J. Stat. Soft.","DOI":"10.18637/jss.v034.i02","ISSN":"1548-7660","issue":"2","issued":{"date-parts":[["2010"]]},"language":"en","source":"DOI.org (Crossref)","title":"Beta Regression in R","type":"article-journal","URL":"http://www.jstatsoft.org/v34/i02/","volume":"34"},
{"id":"cronbach_TwoDisciplinesScientific_1957","abstract":"No man can be acquainted with all of psychology today.\" The past and future place within psychology of 2 historic streams of method, thought, and affiliation—experimental psychology and correlational psychology—is discussed in this address of the President at the 65th annual convention of the APA. \"The well-known virtue of the experimental method is that it brings situational variables under tight control… . The correlation method, for its part, can study what man has not learned to control or can never hope to control… . A true federation of the disciplines is required. Kept independent, they can give only wrong answers or no answers at all regarding certain important problems… . Correlational psychology studies only variance among organisms; experimental psychology studies only variance among treatments. A united discipline will study both of these, but it will also be concerned with the otherwise neglected interactions between organismic and treatment variables. Our job is to invent constructs and to form a network of laws which permits prediction.","accessed":{"date-parts":[["2020",7,23]]},"author":[{"family":"Cronbach","given":"Lee J."}],"citation-key":"cronbach_TwoDisciplinesScientific_1957","container-title":"American Psychologist","container-title-short":"American Psychologist","DOI":"10.1037/h0043943","ISSN":"0003-066X","issue":"11","issued":{"date-parts":[["1957"]]},"language":"en","page":"671-684","source":"DOI.org (Crossref)","title":"The two disciplines of scientific psychology.","type":"article-journal","URL":"http://content.apa.org/journals/amp/12/11/671","volume":"12"},
- {"id":"cruickshank_VariationsNormalHaemoglobin_1970","accessed":{"date-parts":[["2023",6,28]]},"author":[{"family":"Cruickshank","given":"J. M."}],"citation-key":"cruickshank_VariationsNormalHaemoglobin_1970","container-title":"British Journal of Haematology","container-title-short":"Br J Haematol","DOI":"10.1111/j.1365-2141.1970.tb00773.x","ISSN":"0007-1048, 1365-2141","issue":"5","issued":{"date-parts":[["1970",5]]},"language":"en","page":"523-530","source":"DOI.org (Crossref)","title":"Some Variations in the Normal Haemoglobin Concentration","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2141.1970.tb00773.x","volume":"18"},
- {"id":"dalaletal_ConsequencesEEGElectrode_2014","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Dalal","given":"Sarang S."},{"family":"Rampp","given":"Stefan"},{"family":"Willomitzer","given":"Florian"},{"family":"Ettl","given":"Svenja"}],"citation-key":"dalaletal_ConsequencesEEGElectrode_2014","container-title":"Frontiers in Neuroscience","container-title-short":"Front. Neurosci.","DOI":"10.3389/fnins.2014.00042","ISSN":"1662-453X","issued":{"date-parts":[["2014",3,11]]},"source":"DOI.org (Crossref)","title":"Consequences of EEG electrode position error on ultimate beamformer source reconstruction performance","type":"article-journal","URL":"http://journal.frontiersin.org/article/10.3389/fnins.2014.00042/abstract","volume":"8"},
{"id":"danielarzate-menaetal_StationaryEEGPattern_2022","accessed":{"date-parts":[["2022",7,14]]},"author":[{"family":"Daniel Arzate-Mena","given":"J."},{"family":"Abela","given":"Eugenio"},{"family":"Olguín-Rodríguez","given":"Paola V."},{"family":"Ríos-Herrera","given":"Wady"},{"family":"Alcauter","given":"Sarael"},{"family":"Schindler","given":"Kaspar"},{"family":"Wiest","given":"Roland"},{"family":"Müller","given":"Markus F."},{"family":"Rummel","given":"Christian"}],"citation-key":"danielarzate-menaetal_StationaryEEGPattern_2022","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2021.118763","ISSN":"10538119","issued":{"date-parts":[["2022",2]]},"language":"en","page":"118763","source":"DOI.org (Crossref)","title":"Stationary EEG pattern relates to large-scale resting state networks – An EEG-fMRI study connecting brain networks across time-scales","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811921010351","volume":"246"},
{"id":"decheveignenelken_FiltersWhenWhy_2019","abstract":"Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a scientist’s training, yet the impact of filters on interpreting data is not always fully appreciated. This paper reviews the issue and explains what a filter is, what problems are to be expected when using them, how to choose the right filter, and how to avoid filtering by using alternative tools. Time-frequency analysis shares some of the same problems that filters have, particularly in the case of wavelet transforms. We recommend reporting filter characteristics with sufficient details, including a plot of the impulse or step response as an inset.","accessed":{"date-parts":[["2021",11,23]]},"author":[{"family":"Cheveigné","given":"Alain","non-dropping-particle":"de"},{"family":"Nelken","given":"Israel"}],"citation-key":"decheveignenelken_FiltersWhenWhy_2019","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2019.02.039","ISSN":"0896-6273","issue":"2","issued":{"date-parts":[["2019",4,17]]},"language":"en","page":"280-293","source":"ScienceDirect","title":"Filters: When, Why, and How (Not) to Use Them","title-short":"Filters","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0896627319301746","volume":"102"},
{"id":"decocorbetta_DynamicalBalanceBrain_2011","abstract":"The authors review evidence that spontaneous, that is, not stimulus or task driven, activity in the brain at the level of large-scale neural systems is not noise, but orderly and organized in a series of functional networks that maintain, at all times, a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anticorrelation between networks, depends on noise-driven transitions between different multistable cluster synchronization states. These multistable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional subnetworks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals.","author":[{"family":"Deco","given":"Gustavo"},{"family":"Corbetta","given":"Maurizio"}],"citation-key":"decocorbetta_DynamicalBalanceBrain_2011","container-title":"The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry","container-title-short":"Neuroscientist","DOI":"10.1177/1073858409354384","ISSN":"1089-4098","issue":"1","issued":{"date-parts":[["2011",2]]},"language":"eng","page":"107-123","PMCID":"PMC4139497","PMID":"21196530","source":"PubMed","title":"The dynamical balance of the brain at rest","type":"article-journal","volume":"17"},
@@ -83,7 +75,6 @@
{"id":"dinicolaetal_ParallelDistributedNetworks_2020","abstract":"Association cortex is organized into large-scale distributed networks. One such network, the default network (DN), is linked to diverse forms of internal mentation, opening debate about whether shared or distinct anatomy supports multiple forms of cognition. Using within-individual analysis procedures that preserve idiosyncratic anatomical details, we probed whether multiple tasks from two domains, episodic projection and theory of mind (ToM), rely on the same or distinct networks. In an initial experiment (6 subjects, each scanned 4 times), we found evidence that episodic projection and ToM tasks activate separate regions distributed throughout the cortex, with adjacent regions in parietal, temporal, prefrontal, and midline zones. These distinctions were predicted by the hypothesis that the DN comprises two parallel, interdigitated networks. One network, linked to parahippocampal cortex (PHC), is preferentially recruited during episodic projection, including both remembering and imagining the future. A second juxtaposed network, which includes the temporoparietal junction (TPJ), is differentially engaged during multiple forms of ToM. In two prospectively acquired independent experiments, we replicated and triplicated the dissociation (each with 6 subjects scanned 4 times). Furthermore, the dissociation was found in all zones when analyzed independently, including robustly in midline regions previously described as hubs. The TPJ-linked network is interwoven with the PHC-linked network across the cortex, making clear why it is difficult to fully resolve the two networks in group-averaged or lower-resolution data. These results refine our understanding of the functional-anatomical organization of association cortex and raise fundamental questions about how specialization might arise in parallel, juxtaposed association networks.\n NEW & NOTEWORTHY Two distributed, interdigitated networks exist within the bounds of the canonical default network. Here we used repeated scanning of individuals, across three independent samples, to provide evidence that tasks requiring episodic projection or theory of mind differentially recruit the two networks across multiple cortical zones. The two distributed networks thus appear to preferentially subserve distinct functions.","accessed":{"date-parts":[["2023",10,20]]},"author":[{"family":"DiNicola","given":"Lauren M."},{"family":"Braga","given":"Rodrigo M."},{"family":"Buckner","given":"Randy L."}],"citation-key":"dinicolaetal_ParallelDistributedNetworks_2020","container-title":"Journal of Neurophysiology","container-title-short":"Journal of Neurophysiology","DOI":"10.1152/jn.00529.2019","ISSN":"0022-3077, 1522-1598","issue":"3","issued":{"date-parts":[["2020",3,1]]},"language":"en","page":"1144-1179","source":"DOI.org (Crossref)","title":"Parallel distributed networks dissociate episodic and social functions within the individual","type":"article-journal","URL":"https://journals.physiology.org/doi/10.1152/jn.00529.2019","volume":"123"},
{"id":"doumaweedon_AnalysingContinuousProportions_2019","abstract":"Proportional data, in which response variables are expressed as percentages or fractions of a whole, are analysed in many subfields of ecology and evolution. The scale-independence of proportions makes them appropriate to analyse many biological phenomena, but statistical analyses are not straightforward, since proportions can only take values from zero to one and their variance is usually not constant across the range of the predictor. Transformations to overcome these problems are often applied, but can lead to biased estimates and difficulties in interpretation.\n\nIn this paper, we provide an overview of the different types of proportional data and discuss the different analysis strategies available. In particular, we review and discuss the use of promising, but little used, techniques for analysing continuous (also called non-count-based or non-binomial) proportions (e.g. percent cover, fraction time spent on an activity): beta and Dirichlet regression, and some of their most important extensions.\n\nA major distinction can be made between proportions arising from counts and those arising from continuous measurements. For proportions consisting of two categories, count-based data are best analysed using well-developed techniques such as logistic regression, while continuous proportions can be analysed with beta regression models. In the case of >2 categories, multinomial logistic regression or Dirichlet regression can be applied. Both beta and Dirichlet regression techniques model proportions at their original scale, which makes statistical inference more straightforward and produce less biased estimates relative to transformation-based solutions. Extensions to beta regression, such as models for variable dispersion, zero-one augmented data and mixed effects designs have been developed and are reviewed and applied to case studies. Finally, we briefly discuss some issues regarding model fitting, inference, and reporting that are particularly relevant to beta and Dirichlet regression.\n\nBeta regression and Dirichlet regression overcome some problems inherent in applying classic statistical approaches to proportional data. To facilitate the adoption of these techniques by practitioners in ecology and evolution, we present detailed, annotated demonstration scripts covering all variations of beta and Dirichlet regression discussed in the article, implemented in the freely available language for statistical computing, r.","accessed":{"date-parts":[["2022",6,15]]},"author":[{"family":"Douma","given":"Jacob C."},{"family":"Weedon","given":"James T."}],"citation-key":"doumaweedon_AnalysingContinuousProportions_2019","container-title":"Methods in Ecology and Evolution","container-title-short":"Methods Ecol Evol","DOI":"10.1111/2041-210X.13234","editor":[{"family":"Warton","given":"David"}],"ISSN":"2041-210X, 2041-210X","issue":"9","issued":{"date-parts":[["2019",9]]},"language":"en","note":"https://doi.org/10.5281/zenodo.3234670","page":"1412-1430","source":"DOI.org (Crossref)","title":"Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression","title-short":"Analysing continuous proportions in ecology and evolution","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13234","volume":"10"},
{"id":"drew_NeurovascularCouplingMotive_2022","accessed":{"date-parts":[["2023",6,23]]},"author":[{"family":"Drew","given":"Patrick J."}],"citation-key":"drew_NeurovascularCouplingMotive_2022","container-title":"Trends in Neurosciences","container-title-short":"Trends in Neurosciences","DOI":"10.1016/j.tins.2022.08.004","ISSN":"01662236","issue":"11","issued":{"date-parts":[["2022",11]]},"language":"en","page":"809-819","source":"DOI.org (Crossref)","title":"Neurovascular coupling: motive unknown","title-short":"Neurovascular coupling","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0166223622001618","volume":"45"},
- {"id":"drinka_HematocritElevationAssociated_2013","accessed":{"date-parts":[["2023",4,25]]},"author":[{"family":"Drinka","given":"Paul"}],"citation-key":"drinka_HematocritElevationAssociated_2013","container-title":"Journal of the American Medical Directors Association","container-title-short":"Journal of the American Medical Directors Association","DOI":"10.1016/j.jamda.2013.08.006","ISSN":"15258610","issue":"11","issued":{"date-parts":[["2013",11]]},"language":"en","page":"848","source":"DOI.org (Crossref)","title":"Hematocrit Elevation Associated With Testosterone Administration","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1525861013004647","volume":"14"},
{"id":"duboisadolphs_BuildingScienceIndividual_2016","abstract":"To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.","accessed":{"date-parts":[["2021",10,13]]},"author":[{"family":"Dubois","given":"Julien"},{"family":"Adolphs","given":"Ralph"}],"citation-key":"duboisadolphs_BuildingScienceIndividual_2016","container-title":"Trends in cognitive sciences","container-title-short":"Trends Cogn Sci","DOI":"10.1016/j.tics.2016.03.014","ISSN":"1364-6613","issue":"6","issued":{"date-parts":[["2016",6]]},"page":"425-443","PMCID":"PMC4886721","PMID":"27138646","source":"PubMed Central","title":"Building a science of individual differences from fMRI","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4886721/","volume":"20"},
{"id":"dunnsmyth_RandomizedQuantileResiduals_1996","accessed":{"date-parts":[["2022",6,16]]},"author":[{"family":"Dunn","given":"Peter K."},{"family":"Smyth","given":"Gordon K."}],"citation-key":"dunnsmyth_RandomizedQuantileResiduals_1996","container-title":"Journal of Computational and Graphical Statistics","container-title-short":"Journal of Computational and Graphical Statistics","DOI":"10.2307/1390802","ISSN":"10618600","issue":"3","issued":{"date-parts":[["1996",9]]},"page":"236","source":"DOI.org (Crossref)","title":"Randomized Quantile Residuals","type":"article-journal","URL":"https://www.jstor.org/stable/1390802?origin=crossref","volume":"5"},
{"id":"dworetskyetal_ProbabilisticMappingHuman_2021","abstract":"Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show \"core\" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.","author":[{"family":"Dworetsky","given":"Ally"},{"family":"Seitzman","given":"Benjamin A."},{"family":"Adeyemo","given":"Babatunde"},{"family":"Neta","given":"Maital"},{"family":"Coalson","given":"Rebecca S."},{"family":"Petersen","given":"Steven E."},{"family":"Gratton","given":"Caterina"}],"citation-key":"dworetskyetal_ProbabilisticMappingHuman_2021","container-title":"NeuroImage","container-title-short":"Neuroimage","DOI":"10.1016/j.neuroimage.2021.118164","ISSN":"1095-9572","issued":{"date-parts":[["2021",8,15]]},"language":"eng","page":"118164","PMCID":"PMC8296467","PMID":"34000397","source":"PubMed","title":"Probabilistic mapping of human functional brain networks identifies regions of high group consensus","type":"article-journal","volume":"237"},
@@ -116,7 +107,6 @@
{"id":"fristonprice_DegeneracyRedundancyCognitive_2003","accessed":{"date-parts":[["2023",10,24]]},"author":[{"family":"Friston","given":"Karl J."},{"family":"Price","given":"Cathy J."}],"citation-key":"fristonprice_DegeneracyRedundancyCognitive_2003","container-title":"Trends in Cognitive Sciences","container-title-short":"Trends in Cognitive Sciences","DOI":"10.1016/S1364-6613(03)00054-8","ISSN":"13646613","issue":"4","issued":{"date-parts":[["2003",4]]},"language":"en","page":"151-152","source":"DOI.org (Crossref)","title":"Degeneracy and redundancy in cognitive anatomy","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1364661303000548","volume":"7"},
{"id":"frostgoebel_MeasuringStructuralFunctional_2012","abstract":"The central question of the relationship between structure and function in the human brain is still not well understood. In order to investigate this fundamental relationship we create functional probabilistic maps from a large set of mapping experiments and compare the location of functionally localised regions across subjects using different whole-brain alignment schemes. To avoid the major problems associated with meta-analysis approaches, all subjects are scanned using the same paradigms, the same scanner and the same analysis pipeline. We show that an advanced, curvature driven cortex based alignment (CBA) scheme largely removes macro-anatomical variability across subjects. Remaining variability in the observed spatial location of functional regions, thus, reflects the “true” functional variability, i.e. the quantified variability is a good estimator of the underlying structural–functional correspondence. After localising 13 widely studied functional areas, we found a large variability in the degree to which functional areas respect macro-anatomical boundaries across the cortex. Some areas, such as the frontal eye fields (FEF) are strongly bound to a macro-anatomical location. Fusiform face area (FFA) on the other hand, varies in its location along the length of the fusiform gyrus even though the gyri themselves are well aligned across subjects. Language areas were found to vary greatly across subjects whilst a high degree of overlap was observed in sensory and motor areas. The observed differences in functional variability for different specialised areas suggest that a more complete estimation of the structure–function relationship across the whole cortex requires further empirical studies with an expanded test battery.","accessed":{"date-parts":[["2021",10,13]]},"author":[{"family":"Frost","given":"Martin A."},{"family":"Goebel","given":"Rainer"}],"citation-key":"frostgoebel_MeasuringStructuralFunctional_2012","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2011.08.035","ISSN":"1053-8119","issue":"2","issued":{"date-parts":[["2012",1,16]]},"language":"en","page":"1369-1381","source":"ScienceDirect","title":"Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment","title-short":"Measuring structural–functional correspondence","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S1053811911009281","volume":"59"},
{"id":"garrettetal_MomenttomomentBrainSignal_2013","abstract":"Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human life-span development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.","accessed":{"date-parts":[["2020",6,28]]},"author":[{"family":"Garrett","given":"Douglas D."},{"family":"Samanez-Larkin","given":"Gregory R."},{"family":"MacDonald","given":"Stuart W.S."},{"family":"Lindenberger","given":"Ulman"},{"family":"McIntosh","given":"Anthony R."},{"family":"Grady","given":"Cheryl L."}],"citation-key":"garrettetal_MomenttomomentBrainSignal_2013","container-title":"Neuroscience and biobehavioral reviews","container-title-short":"Neurosci Biobehav Rev","DOI":"10.1016/j.neubiorev.2013.02.015","ISSN":"0149-7634","issue":"4","issued":{"date-parts":[["2013",5]]},"page":"610-624","PMCID":"PMC3732213","PMID":"23458776","source":"PubMed Central","title":"Moment-to-moment brain signal variability: A next frontier in human brain mapping?","title-short":"Moment-to-moment brain signal variability","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3732213/","volume":"37"},
- {"id":"gelman_ProblemsPValuesAre_2016","author":[{"family":"Gelman","given":"Andrew"}],"citation-key":"gelman_ProblemsPValuesAre_2016","container-title":"The American Statistician","issue":"70","issued":{"date-parts":[["2016"]]},"language":"en","page":"129–133","source":"Zotero","title":"The Problems With P-Values are not Just With P-Values. Supplemental material to the ASA statement on p-values and statistical significance.","type":"article-journal","URL":"https://doi.org/10.1080/00031305.2016.1154108"},
{"id":"gelmanetal_BayesianDataAnalysis_2013","abstract":"Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.","author":[{"family":"Gelman","given":"Andrew"},{"family":"Carlin","given":"John B."},{"family":"Stern","given":"Hal S."},{"family":"Dunson","given":"David B."},{"family":"Vehtari","given":"Aki"},{"family":"Rubin","given":"Donald B."}],"citation-key":"gelmanetal_BayesianDataAnalysis_2013","edition":"3rd edition","event-place":"Boca Raton","ISBN":"978-1-4398-4095-5","issued":{"date-parts":[["2013",11,1]]},"language":"English","number-of-pages":"675","publisher":"Chapman and Hall/CRC","publisher-place":"Boca Raton","source":"Amazon","title":"Bayesian Data Analysis","type":"book","URL":"http://www.stat.columbia.edu/~gelman/book/"},
{"id":"gelmanetal_RegressionOtherStories_2020","accessed":{"date-parts":[["2022",1,29]]},"author":[{"family":"Gelman","given":"Andrew"},{"family":"Hill","given":"Jennifer"},{"family":"Vehtari","given":"Aki"}],"citation-key":"gelmanetal_RegressionOtherStories_2020","DOI":"10.1017/9781139161879","edition":"1","ISBN":"978-1-139-16187-9 978-1-107-02398-7 978-1-107-67651-0","issued":{"date-parts":[["2020",7,23]]},"note":"Homepage: https://avehtari.github.io/ROS-Examples/\n\nRepo: https://github.com/avehtari/ROS-Examples\n\nbrms and tidyverse: https://github.com/ASKurz/Working-through-Regression-and-other-stories","publisher":"Cambridge University Press","source":"DOI.org (Crossref)","title":"Regression and Other Stories","type":"book","URL":"https://www.cambridge.org/highereducation/product/9781139161879/book"},
{"id":"gelmanhill_DataAnalysisUsing_2006","abstract":"Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/","author":[{"family":"Gelman","given":"Andrew"},{"family":"Hill","given":"Jennifer"}],"citation-key":"gelmanhill_DataAnalysisUsing_2006","edition":"1st edition","event-place":"Cambridge; New York","ISBN":"978-0-521-68689-1","issued":{"date-parts":[["2006",12,18]]},"language":"English","number-of-pages":"648","publisher":"Cambridge University Press","publisher-place":"Cambridge; New York","source":"Amazon","title":"Data Analysis Using Regression and Multilevel/Hierarchical Models","type":"book","URL":"http://www.stat.columbia.edu/~gelman/arm/"},
@@ -131,7 +121,9 @@
{"id":"gordonnelson_ThreeTypesIndividual_2021","accessed":{"date-parts":[["2023",10,11]]},"author":[{"family":"Gordon","given":"Evan M."},{"family":"Nelson","given":"Steven M."}],"citation-key":"gordonnelson_ThreeTypesIndividual_2021","container-title":"Current Opinion in Behavioral Sciences","container-title-short":"Current Opinion in Behavioral Sciences","DOI":"10.1016/j.cobeha.2021.02.014","ISSN":"23521546","issued":{"date-parts":[["2021",8]]},"language":"en","page":"79-86","source":"DOI.org (Crossref)","title":"Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S235215462100036X","volume":"40"},
{"id":"gramfortetal_MEGEEGData_2013","abstract":"Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.","accessed":{"date-parts":[["2021",11,7]]},"author":[{"family":"Gramfort","given":"Alexandre"},{"family":"Luessi","given":"Martin"},{"family":"Larson","given":"Eric"},{"family":"Engemann","given":"Denis"},{"family":"Strohmeier","given":"Daniel"},{"family":"Brodbeck","given":"Christian"},{"family":"Goj","given":"Roman"},{"family":"Jas","given":"Mainak"},{"family":"Brooks","given":"Teon"},{"family":"Parkkonen","given":"Lauri"},{"family":"Hämäläinen","given":"Matti"}],"citation-key":"gramfortetal_MEGEEGData_2013","container-title":"Frontiers in Neuroscience","DOI":"10.3389/fnins.2013.00267","ISSN":"1662-453X","issued":{"date-parts":[["2013"]]},"page":"267","source":"Frontiers","title":"MEG and EEG data analysis with MNE-Python","type":"article-journal","URL":"https://www.frontiersin.org/article/10.3389/fnins.2013.00267","volume":"7"},
{"id":"grattonetal_BrainbehaviorCorrelationsTwo_2022","accessed":{"date-parts":[["2023",10,13]]},"author":[{"family":"Gratton","given":"Caterina"},{"family":"Nelson","given":"Steven M."},{"family":"Gordon","given":"Evan M."}],"citation-key":"grattonetal_BrainbehaviorCorrelationsTwo_2022","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2022.04.018","ISSN":"08966273","issue":"9","issued":{"date-parts":[["2022",5]]},"language":"en","page":"1446-1449","source":"DOI.org (Crossref)","title":"Brain-behavior correlations: Two paths toward reliability","title-short":"Brain-behavior correlations","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627322003634","volume":"110"},
+ {"id":"grattonetal_DefiningIndividualSpecificFunctional_2020","abstract":"Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new \"precision\" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.","accessed":{"date-parts":[["2020",6,28]]},"author":[{"family":"Gratton","given":"Caterina"},{"family":"Kraus","given":"Brian T."},{"family":"Greene","given":"Deanna J."},{"family":"Gordon","given":"Evan M."},{"family":"Laumann","given":"Timothy O."},{"family":"Nelson","given":"Steven M."},{"family":"Dosenbach","given":"Nico U. F."},{"family":"Petersen","given":"Steven E."}],"citation-key":"grattonetal_DefiningIndividualSpecificFunctional_2020","container-title":"Biological Psychiatry","container-title-short":"Biological Psychiatry","DOI":"10.1016/j.biopsych.2019.10.026","ISSN":"0006-3223, 1873-2402","issue":"1","issued":{"date-parts":[["2020",7,1]]},"language":"English","page":"28-39","PMID":"31916942","publisher":"Elsevier","source":"www.biologicalpsychiatryjournal.com","title":"Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry","type":"article-journal","URL":"https://www.biologicalpsychiatryjournal.com/article/S0006-3223(19)31829-3/abstract","volume":"88"},
{"id":"grattonetal_FunctionalBrainNetworks_2018","abstract":"The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.","accessed":{"date-parts":[["2020",6,28]]},"author":[{"family":"Gratton","given":"Caterina"},{"family":"Laumann","given":"Timothy O."},{"family":"Nielsen","given":"Ashley N."},{"family":"Greene","given":"Deanna J."},{"family":"Gordon","given":"Evan M."},{"family":"Gilmore","given":"Adrian W."},{"family":"Nelson","given":"Steven M."},{"family":"Coalson","given":"Rebecca S."},{"family":"Snyder","given":"Abraham Z."},{"family":"Schlaggar","given":"Bradley L."},{"family":"Dosenbach","given":"Nico U.F."},{"family":"Petersen","given":"Steven E."}],"citation-key":"grattonetal_FunctionalBrainNetworks_2018","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2018.03.035","ISSN":"08966273","issue":"2","issued":{"date-parts":[["2018",4]]},"language":"en","page":"439-452.e5","source":"DOI.org (Crossref)","title":"Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627318302411","volume":"98"},
+ {"id":"grechetal_ReviewSolvingInverse_2008","abstract":"Abstract\n In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided.\n This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.","accessed":{"date-parts":[["2024",1,23]]},"author":[{"family":"Grech","given":"Roberta"},{"family":"Cassar","given":"Tracey"},{"family":"Muscat","given":"Joseph"},{"family":"Camilleri","given":"Kenneth P"},{"family":"Fabri","given":"Simon G"},{"family":"Zervakis","given":"Michalis"},{"family":"Xanthopoulos","given":"Petros"},{"family":"Sakkalis","given":"Vangelis"},{"family":"Vanrumste","given":"Bart"}],"citation-key":"grechetal_ReviewSolvingInverse_2008","container-title":"Journal of NeuroEngineering and Rehabilitation","container-title-short":"J NeuroEngineering Rehabil","DOI":"10.1186/1743-0003-5-25","ISSN":"1743-0003","issue":"1","issued":{"date-parts":[["2008",12]]},"language":"en","page":"25","source":"DOI.org (Crossref)","title":"Review on solving the inverse problem in EEG source analysis","type":"article-journal","URL":"https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-5-25","volume":"5"},
{"id":"greeneetal_TaskinducedBrainState_2018","abstract":"Abstract\n Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.","accessed":{"date-parts":[["2023",10,16]]},"author":[{"family":"Greene","given":"Abigail S."},{"family":"Gao","given":"Siyuan"},{"family":"Scheinost","given":"Dustin"},{"family":"Constable","given":"R. Todd"}],"citation-key":"greeneetal_TaskinducedBrainState_2018","container-title":"Nature Communications","container-title-short":"Nat Commun","DOI":"10.1038/s41467-018-04920-3","ISSN":"2041-1723","issue":"1","issued":{"date-parts":[["2018",7,18]]},"language":"en","page":"2807","source":"DOI.org (Crossref)","title":"Task-induced brain state manipulation improves prediction of individual traits","type":"article-journal","URL":"https://www.nature.com/articles/s41467-018-04920-3","volume":"9"},
{"id":"greenland_InvitedCommentaryNeed_2017","accessed":{"date-parts":[["2024",1,4]]},"author":[{"family":"Greenland","given":"Sander"}],"citation-key":"greenland_InvitedCommentaryNeed_2017","container-title":"American Journal of Epidemiology","DOI":"10.1093/aje/kwx259","ISSN":"0002-9262, 1476-6256","issue":"6","issued":{"date-parts":[["2017",9,15]]},"language":"en","page":"639-645","source":"DOI.org (Crossref)","title":"Invited Commentary: The Need for Cognitive Science in Methodology","title-short":"Invited Commentary","type":"article-journal","URL":"https://academic.oup.com/aje/article/186/6/639/3886035","volume":"186"},
{"id":"greenland_ValidPValuesBehave_2019","abstract":"The present note explores sources of misplaced criticisms of P-values, such as conflicting definitions of “significance levels” and “P-values” in authoritative sources, and the consequent misinterpretation of P-values as error probabilities. It then discusses several properties of P-values that have been presented as fatal flaws: That P-values exhibit extreme variation across samples (and thus are “unreliable”), confound effect size with sample size, are sensitive to sample size, and depend on investigator sampling intentions. These properties are often criticized from a likelihood or Bayesian framework, yet they are exactly the properties P-values should exhibit when they are constructed and interpreted correctly within their originating framework. Other common criticisms are that P-values force users to focus on irrelevant hypotheses and overstate evidence against those hypotheses. These problems are not however properties of P-values but are faults of researchers who focus on null hypotheses and overstate evidence based on misperceptions that p = 0.05 represents enough evidence to reject hypotheses. Those problems are easily seen without use of Bayesian concepts by translating the observed P-value p into the Shannon information (S-value or surprisal) –log2(p).","accessed":{"date-parts":[["2022",1,12]]},"author":[{"family":"Greenland","given":"Sander"}],"citation-key":"greenland_ValidPValuesBehave_2019","container-title":"The American Statistician","container-title-short":"The American Statistician","DOI":"10.1080/00031305.2018.1529625","ISSN":"0003-1305, 1537-2731","issue":"sup1","issued":{"date-parts":[["2019",3,29]]},"language":"en","page":"106-114","source":"DOI.org (Crossref)","title":"Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values","type":"article-journal","URL":"https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1529625","volume":"73"},
@@ -144,7 +136,6 @@
{"id":"hagmannetal_MappingStructuralCore_2008","abstract":"Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.","accessed":{"date-parts":[["2020",8,16]]},"author":[{"family":"Hagmann","given":"Patric"},{"family":"Cammoun","given":"Leila"},{"family":"Gigandet","given":"Xavier"},{"family":"Meuli","given":"Reto"},{"family":"Honey","given":"Christopher J."},{"family":"Wedeen","given":"Van J."},{"family":"Sporns","given":"Olaf"}],"citation-key":"hagmannetal_MappingStructuralCore_2008","container-title":"PLOS Biology","container-title-short":"PLOS Biology","DOI":"10.1371/journal.pbio.0060159","ISSN":"1545-7885","issue":"7","issued":{"date-parts":[["2008",7,1]]},"language":"en","page":"e159","publisher":"Public Library of Science","source":"PLoS Journals","title":"Mapping the Structural Core of Human Cerebral Cortex","type":"article-journal","URL":"https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0060159","volume":"6"},
{"id":"halletal_InterpretingBOLDDialogue_2016","abstract":"Cognitive neuroscience depends on the use of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to probe brain function. Although commonly used as a surrogate measure of neuronal activity, BOLD signals actually reflect changes in brain blood oxygenation. Understanding the mechanisms linking neuronal activity to vascular perfusion is, therefore, critical in interpreting BOLD. Advances in cellular neuroscience demonstrating differences in this neurovascular relationship in different brain regions, conditions or pathologies are often not accounted for when interpreting BOLD. Meanwhile, within cognitive neuroscience, the increasing use of high magnetic field strengths and the development of model-based tasks and analyses have broadened the capability of BOLD signals to inform us about the underlying neuronal activity, but these methods are less well understood by cellular neuroscientists. In 2016, a Royal Society Theo Murphy Meeting brought scientists from the two communities together to discuss these issues. Here, we consolidate the main conclusions arising from that meeting. We discuss areas of consensus about what BOLD fMRI can tell us about underlying neuronal activity, and how advanced modelling techniques have improved our ability to use and interpret BOLD. We also highlight areas of controversy in understanding BOLD and suggest research directions required to resolve these issues.\n This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.","accessed":{"date-parts":[["2023",6,23]]},"author":[{"family":"Hall","given":"Catherine N."},{"family":"Howarth","given":"Clare"},{"family":"Kurth-Nelson","given":"Zebulun"},{"family":"Mishra","given":"Anusha"}],"citation-key":"halletal_InterpretingBOLDDialogue_2016","container-title":"Philosophical Transactions of the Royal Society B: Biological Sciences","container-title-short":"Phil. Trans. R. Soc. B","DOI":"10.1098/rstb.2015.0348","ISSN":"0962-8436, 1471-2970","issue":"1705","issued":{"date-parts":[["2016",10,5]]},"language":"en","page":"20150348","source":"DOI.org (Crossref)","title":"Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience","title-short":"Interpreting BOLD","type":"article-journal","URL":"https://royalsocietypublishing.org/doi/10.1098/rstb.2015.0348","volume":"371"},
{"id":"halletal_RelationshipMEGFMRI_2014","accessed":{"date-parts":[["2023",7,11]]},"author":[{"family":"Hall","given":"Emma L."},{"family":"Robson","given":"Siân E."},{"family":"Morris","given":"Peter G."},{"family":"Brookes","given":"Matthew J."}],"citation-key":"halletal_RelationshipMEGFMRI_2014","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2013.11.005","ISSN":"10538119","issued":{"date-parts":[["2014",11]]},"language":"en","page":"80-91","source":"DOI.org (Crossref)","title":"The relationship between MEG and fMRI","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811913010975","volume":"102"},
- {"id":"hallezetal_ReviewSolvingForward_2007","accessed":{"date-parts":[["2023",7,5]]},"author":[{"family":"Hallez","given":"Hans"},{"family":"Vanrumste","given":"Bart"},{"family":"Grech","given":"Roberta"},{"family":"Muscat","given":"Joseph"},{"family":"De Clercq","given":"Wim"},{"family":"Vergult","given":"Anneleen"},{"family":"D'Asseler","given":"Yves"},{"family":"Camilleri","given":"Kenneth P"},{"family":"Fabri","given":"Simon G"},{"family":"Van Huffel","given":"Sabine"},{"family":"Lemahieu","given":"Ignace"}],"citation-key":"hallezetal_ReviewSolvingForward_2007","container-title":"Journal of NeuroEngineering and Rehabilitation","container-title-short":"J NeuroEngineering Rehabil","DOI":"10.1186/1743-0003-4-46","ISSN":"1743-0003","issue":"1","issued":{"date-parts":[["2007",12]]},"language":"en","page":"46","source":"DOI.org (Crossref)","title":"Review on solving the forward problem in EEG source analysis","type":"article-journal","URL":"https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-4-46","volume":"4"},
{"id":"hardmeieretal_ReproducibilityFunctionalConnectivity_2014","accessed":{"date-parts":[["2023",12,29]]},"author":[{"family":"Hardmeier","given":"Martin"},{"family":"Hatz","given":"Florian"},{"family":"Bousleiman","given":"Habib"},{"family":"Schindler","given":"Christian"},{"family":"Stam","given":"Cornelis Jan"},{"family":"Fuhr","given":"Peter"}],"citation-key":"hardmeieretal_ReproducibilityFunctionalConnectivity_2014","container-title":"PLoS ONE","container-title-short":"PLoS ONE","DOI":"10.1371/journal.pone.0108648","editor":[{"family":"Ward","given":"Lawrence M."}],"ISSN":"1932-6203","issue":"10","issued":{"date-parts":[["2014",10,6]]},"language":"en","page":"e108648","source":"DOI.org (Crossref)","title":"Reproducibility of Functional Connectivity and Graph Measures Based on the Phase Lag Index (PLI) and Weighted Phase Lag Index (wPLI) Derived from High Resolution EEG","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pone.0108648","volume":"9"},
{"id":"harrisonetal_LargescaleProbabilisticFunctional_2015","accessed":{"date-parts":[["2023",10,20]]},"author":[{"family":"Harrison","given":"Samuel J."},{"family":"Woolrich","given":"Mark W."},{"family":"Robinson","given":"Emma C."},{"family":"Glasser","given":"Matthew F."},{"family":"Beckmann","given":"Christian F."},{"family":"Jenkinson","given":"Mark"},{"family":"Smith","given":"Stephen M."}],"citation-key":"harrisonetal_LargescaleProbabilisticFunctional_2015","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2015.01.013","ISSN":"10538119","issued":{"date-parts":[["2015",4]]},"language":"en","page":"217-231","source":"DOI.org (Crossref)","title":"Large-scale Probabilistic Functional Modes from resting state fMRI","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811915000208","volume":"109"},
{"id":"hartiglohse_DHARMaResidualDiagnostics_2022","abstract":"The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 'GLMMadaptive' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.","accessed":{"date-parts":[["2022",6,16]]},"author":[{"family":"Hartig","given":"Florian"},{"family":"Lohse","given":"Lukas"}],"citation-key":"hartiglohse_DHARMaResidualDiagnostics_2022","issued":{"date-parts":[["2022",1,16]]},"license":"GPL (≥ 3)","note":"Vignette: https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#glmmtmb\n\nWhy uniform distribution: https://github.com/florianhartig/DHARMa/issues/39","source":"R-Packages","title":"DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models","title-short":"DHARMa","type":"software","URL":"https://CRAN.R-project.org/package=DHARMa","version":"0.4.5"},
@@ -161,7 +152,6 @@
{"id":"honarietal_EvaluatingPhaseSynchronization_2021","accessed":{"date-parts":[["2023",8,22]]},"author":[{"family":"Honari","given":"Hamed"},{"family":"Choe","given":"Ann S."},{"family":"Lindquist","given":"Martin A."}],"citation-key":"honarietal_EvaluatingPhaseSynchronization_2021","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2020.117704","ISSN":"10538119","issued":{"date-parts":[["2021",3]]},"language":"en","page":"117704","source":"DOI.org (Crossref)","title":"Evaluating phase synchronization methods in fMRI: A comparison study and new approaches","title-short":"Evaluating phase synchronization methods in fMRI","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811920311897","volume":"228"},
{"id":"honeyetal_NetworkStructureCerebral_2007","abstract":"Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure–function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of individual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect individual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.","accessed":{"date-parts":[["2023",7,17]]},"author":[{"family":"Honey","given":"Christopher J."},{"family":"Kötter","given":"Rolf"},{"family":"Breakspear","given":"Michael"},{"family":"Sporns","given":"Olaf"}],"citation-key":"honeyetal_NetworkStructureCerebral_2007","container-title":"Proceedings of the National Academy of Sciences","container-title-short":"Proc. Natl. Acad. Sci. U.S.A.","DOI":"10.1073/pnas.0701519104","ISSN":"0027-8424, 1091-6490","issue":"24","issued":{"date-parts":[["2007",6,12]]},"language":"en","page":"10240-10245","source":"DOI.org (Crossref)","title":"Network structure of cerebral cortex shapes functional connectivity on multiple time scales","type":"article-journal","URL":"https://pnas.org/doi/full/10.1073/pnas.0701519104","volume":"104"},
{"id":"honeyetal_PredictingHumanRestingstate_2009","abstract":"In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks—including their spatial statistics and their persistence across time—can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (\n i\n ) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (\n ii\n ) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (\n iii\n ) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Honey","given":"C. J."},{"family":"Sporns","given":"O."},{"family":"Cammoun","given":"L."},{"family":"Gigandet","given":"X."},{"family":"Thiran","given":"J. P."},{"family":"Meuli","given":"R."},{"family":"Hagmann","given":"P."}],"citation-key":"honeyetal_PredictingHumanRestingstate_2009","container-title":"Proceedings of the National Academy of Sciences","container-title-short":"Proc. Natl. Acad. Sci. U.S.A.","DOI":"10.1073/pnas.0811168106","ISSN":"0027-8424, 1091-6490","issue":"6","issued":{"date-parts":[["2009",2,10]]},"language":"en","page":"2035-2040","source":"DOI.org (Crossref)","title":"Predicting human resting-state functional connectivity from structural connectivity","type":"article-journal","URL":"https://pnas.org/doi/full/10.1073/pnas.0811168106","volume":"106"},
- {"id":"hongetal_HowReducingModel_2011","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Hong","given":"Jun Hee"},{"family":"Kim","given":"Donghyeon"},{"family":"Jun","given":"Sung Chan"}],"citation-key":"hongetal_HowReducingModel_2011","container-title":"2011 8th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the 2011 8th International Conference on Bioelectromagnetism","DOI":"10.1109/NFSI.2011.5936812","event-place":"Banff, AB, Canada","event-title":"ICBEM)","ISBN":"978-1-4244-8282-5","issued":{"date-parts":[["2011",5]]},"page":"22-26","publisher":"IEEE","publisher-place":"Banff, AB, Canada","source":"DOI.org (Crossref)","title":"How reducing model mismatch is beneficial to EEG source localization: Simulation study","title-short":"How reducing model mismatch is beneficial to EEG source localization","type":"paper-conference","URL":"http://ieeexplore.ieee.org/document/5936812/"},
{"id":"horienetal_IndividualFunctionalConnectome_2019","abstract":"Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.","author":[{"family":"Horien","given":"Corey"},{"family":"Shen","given":"Xilin"},{"family":"Scheinost","given":"Dustin"},{"family":"Constable","given":"R. Todd"}],"citation-key":"horienetal_IndividualFunctionalConnectome_2019","container-title":"NeuroImage","container-title-short":"Neuroimage","DOI":"10.1016/j.neuroimage.2019.02.002","ISSN":"1095-9572","issued":{"date-parts":[["2019",4,1]]},"language":"eng","page":"676-687","PMCID":"PMC6422733","PMID":"30721751","source":"PubMed","title":"The individual functional connectome is unique and stable over months to years","type":"article-journal","volume":"189"},
{"id":"howarthetal_UpdatedEnergyBudgets_2012","abstract":"The brain's energy supply determines its information processing power, and generates functional imaging signals. The energy use on the different subcellular processes underlying neural information processing has been estimated previously for the grey matter of the cerebral and cerebellar cortex. However, these estimates need reevaluating following recent work demonstrating that action potentials in mammalian neurons are much more energy efficient than was previously thought. Using this new knowledge, this paper provides revised estimates for the energy expenditure on neural computation in a simple model for the cerebral cortex and a detailed model of the cerebellar cortex. In cerebral cortex, most signaling energy (50%) is used on postsynaptic glutamate receptors, 21% is used on action potentials, 20% on resting potentials, 5% on presynaptic transmitter release, and 4% on transmitter recycling. In the cerebellar cortex, excitatory neurons use 75% and inhibitory neurons 25% of the signaling energy, and most energy is used on information processing by non-principal neurons: Purkinje cells use only 15% of the signaling energy. The majority of cerebellar signaling energy use is on the maintenance of resting potentials (54%) and postsynaptic receptors (22%), while action potentials account for only 17% of the signaling energy use.","author":[{"family":"Howarth","given":"Clare"},{"family":"Gleeson","given":"Padraig"},{"family":"Attwell","given":"David"}],"citation-key":"howarthetal_UpdatedEnergyBudgets_2012","container-title":"Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism","container-title-short":"J Cereb Blood Flow Metab","DOI":"10.1038/jcbfm.2012.35","ISSN":"1559-7016","issue":"7","issued":{"date-parts":[["2012",7]]},"language":"eng","page":"1222-1232","PMCID":"PMC3390818","PMID":"22434069","source":"PubMed","title":"Updated energy budgets for neural computation in the neocortex and cerebellum","type":"article-journal","volume":"32"},
{"id":"hutcheonyarom_ResonanceOscillationIntrinsic_2000","abstract":"The realization that different behavioural and perceptual states of the brain are associated with different brain rhythms has sparked growing interest in the oscillatory behaviours of neurons. Recent research has uncovered a close association between electrical oscillations and resonance in neurons. Resonance is an easily measurable property that describes the ability of neurons to respond selectively to inputs at preferred frequencies. A variety of ionic mechanisms support resonance and oscillation in neurons. Understanding the basic principles involved in the production of resonance allows for a simplified classification of these mechanisms. The characterization of resonance and frequency preference captures those essential properties of neurons that can serve as a substrate for coordinating network activity around a particular frequency in the brain.","author":[{"family":"Hutcheon","given":"B."},{"family":"Yarom","given":"Y."}],"citation-key":"hutcheonyarom_ResonanceOscillationIntrinsic_2000","container-title":"Trends in Neurosciences","container-title-short":"Trends Neurosci","DOI":"10.1016/s0166-2236(00)01547-2","ISSN":"0166-2236","issue":"5","issued":{"date-parts":[["2000",5]]},"language":"eng","page":"216-222","PMID":"10782127","source":"PubMed","title":"Resonance, oscillation and the intrinsic frequency preferences of neurons","type":"article-journal","volume":"23"},
@@ -169,8 +159,6 @@
{"id":"idajietal_HarmoniMethodEliminating_2022","accessed":{"date-parts":[["2022",8,25]]},"author":[{"family":"Idaji","given":"Mina Jamshidi"},{"family":"Zhang","given":"Juanli"},{"family":"Stephani","given":"Tilman"},{"family":"Nolte","given":"Guido"},{"family":"Müller","given":"Klaus-Robert"},{"family":"Villringer","given":"Arno"},{"family":"Nikulin","given":"Vadim V."}],"citation-key":"idajietal_HarmoniMethodEliminating_2022","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2022.119053","ISSN":"10538119","issued":{"date-parts":[["2022",5]]},"language":"en","page":"119053","source":"DOI.org (Crossref)","title":"Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data","title-short":"Harmoni","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811922001823","volume":"252"},
{"id":"jalbrzikowskietal_FunctionalConnectomeFingerprinting_2020","abstract":"Pioneering studies have shown that individual correlation measures from resting-state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as \"connectotyping\" or functional connectome \"fingerprinting.\" We analyzed a unique dataset of 12-30 years old (N = 140) individuals who had two distinct resting state scans on the same day and again 12-18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5-years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's \"fingerprint\" contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome.","author":[{"family":"Jalbrzikowski","given":"Maria"},{"family":"Liu","given":"Fuchen"},{"family":"Foran","given":"William"},{"family":"Klei","given":"Lambertus"},{"family":"Calabro","given":"Finnegan J."},{"family":"Roeder","given":"Kathryn"},{"family":"Devlin","given":"Bernie"},{"family":"Luna","given":"Beatriz"}],"citation-key":"jalbrzikowskietal_FunctionalConnectomeFingerprinting_2020","container-title":"Human Brain Mapping","container-title-short":"Hum Brain Mapp","DOI":"10.1002/hbm.25118","ISSN":"1097-0193","issue":"15","issued":{"date-parts":[["2020",10,15]]},"language":"eng","page":"4187-4199","PMCID":"PMC7502841","PMID":"32652852","source":"PubMed","title":"Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart","type":"article-journal","volume":"41"},
{"id":"jamesetal_IntroductionStatisticalLearning_2021","accessed":{"date-parts":[["2021",12,27]]},"author":[{"family":"James","given":"Gareth"},{"family":"Witten","given":"Daniela"},{"family":"Hastie","given":"Trevor"},{"family":"Tibshirani","given":"Robert"}],"citation-key":"jamesetal_IntroductionStatisticalLearning_2021","edition":"2","event-place":"New York","issued":{"date-parts":[["2021",9,26]]},"language":"en","note":"https://www.statlearning.com","publisher":"Springer","publisher-place":"New York","source":"DOI.org (Crossref)","title":"An introduction to statistical learning with applications in R","title-short":"An introduction to statistical learning with applications in R","type":"book","URL":"https://www.tandfonline.com/doi/full/10.1080/24754269.2021.1980261"},
- {"id":"jinetal_RelationshipHematocritLevel_2015","accessed":{"date-parts":[["2023",4,25]]},"author":[{"family":"Jin","given":"Yuan-Ze"},{"family":"Zheng","given":"Dong-Han"},{"family":"Duan","given":"Zhi-Ying"},{"family":"Lin","given":"Ying-Zi"},{"family":"Zhang","given":"Xue-Ying"},{"family":"Wang","given":"Jing-Ru"},{"family":"Han","given":"Shuo"},{"family":"Wang","given":"Guo-Feng"},{"family":"Zhang","given":"Yi-Jing"}],"citation-key":"jinetal_RelationshipHematocritLevel_2015","container-title":"Journal of Clinical Laboratory Analysis","container-title-short":"J. Clin. Lab. Anal.","DOI":"10.1002/jcla.21767","ISSN":"08878013","issue":"4","issued":{"date-parts":[["2015",7]]},"language":"en","page":"289-293","source":"DOI.org (Crossref)","title":"Relationship Between Hematocrit Level and Cardiovascular Risk Factors in a Community-Based Population: HCT Level and Cardiovascular Risk Factors","title-short":"Relationship Between Hematocrit Level and Cardiovascular Risk Factors in a Community-Based Population","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1002/jcla.21767","volume":"29"},
- {"id":"jones_GolgiCajalNeuron_1999","abstract":"Camillo Golgi and Santiago Ramon y Cajal shared the Nobel Prize in 1906 for their work on the histology of the nerve cell, but both held diametrically opposed views about the Neuron Doctrine which emphasizes the structural, functional and developmental singularity of the nerve cell. Golgi's reticularist views remained entrenched and his work on the nervous system did not venture greatly into new territories after its original flowering, which had greater impact than is now commonly credited. Cajal, by contrast, by the time he was awarded the Nobel Prize, was already breaking new ground with a new staining technique in the field of peripheral nerve regeneration, seeing the reconstruction of a severed nerve by sprouting from the proximal stump as another manifestation of the Neuron Doctrine. Paradoxically, identical studies were going on simultaneously in Golgi's laboratory in the hands of Aldo Perroncito, but the findings did not seem to influence Golgi's thinking on the Neuron Doctrine.","author":[{"family":"Jones","given":"E. G."}],"citation-key":"jones_GolgiCajalNeuron_1999","container-title":"Journal of the History of the Neurosciences","container-title-short":"J Hist Neurosci","DOI":"10.1076/jhin.8.2.170.1838","ISSN":"0964-704X","issue":"2","issued":{"date-parts":[["1999",8]]},"language":"eng","page":"170-178","PMID":"11624298","source":"PubMed","title":"Golgi, Cajal and the Neuron Doctrine","type":"article-journal","volume":"8"},
{"id":"josseholmes_MeasuringMultivariateAssociation_2016","abstract":"Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients are being used by different research communities. Scientists use these coefficients to test whether two random vectors are linked. Once it has been ascertained that there is such association through testing, then a next step, often ignored, is to explore and uncover the association’s underlying patterns., This article provides a survey of various measures of dependence between random vectors and tests of independence and emphasizes the connections and differences between the various approaches. After providing definitions of the coefficients and associated tests, we present the recent improvements that enhance their statistical properties and ease of interpretation. We summarize multi-table approaches and provide scenarii where the indices can provide useful summaries of heterogeneous multi-block data. We illustrate these different strategies on several examples of real data and suggest directions for future research.","accessed":{"date-parts":[["2021",12,3]]},"author":[{"family":"Josse","given":"Julie"},{"family":"Holmes","given":"Susan"}],"citation-key":"josseholmes_MeasuringMultivariateAssociation_2016","container-title":"Statistics surveys","container-title-short":"Stat Surv","DOI":"10.1214/16-SS116","ISSN":"1935-7516","issued":{"date-parts":[["2016"]]},"page":"132-167","PMCID":"PMC5658146","PMID":"29081877","source":"PubMed Central","title":"Measuring multivariate association and beyond","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658146/","volume":"10"},
{"id":"kanekotsuda_ChaoticItinerancy_2003","abstract":"Chaotic itinerancy is universal dynamics in high-dimensional dynamical systems, showing itinerant motion among varieties of low-dimensional ordered states through high-dimensional chaos. Discovery, basic features, characterization, examples, and significance of chaotic itinerancy are surveyed.","accessed":{"date-parts":[["2023",8,15]]},"author":[{"family":"Kaneko","given":"Kunihiko"},{"family":"Tsuda","given":"Ichiro"}],"citation-key":"kanekotsuda_ChaoticItinerancy_2003","container-title":"Chaos: An Interdisciplinary Journal of Nonlinear Science","DOI":"10.1063/1.1607783","ISSN":"1054-1500, 1089-7682","issue":"3","issued":{"date-parts":[["2003",9,1]]},"language":"en","page":"926-936","source":"DOI.org (Crossref)","title":"Chaotic itinerancy","type":"article-journal","URL":"https://pubs.aip.org/cha/article/13/3/926/135161/Chaotic-itinerancy","volume":"13"},
{"id":"keiteletal_VisualCortexResponses_2017","accessed":{"date-parts":[["2022",8,24]]},"author":[{"family":"Keitel","given":"Christian"},{"family":"Thut","given":"Gregor"},{"family":"Gross","given":"Joachim"}],"citation-key":"keiteletal_VisualCortexResponses_2017","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2016.11.043","ISSN":"10538119","issued":{"date-parts":[["2017",2]]},"language":"en","page":"58-70","source":"DOI.org (Crossref)","title":"Visual cortex responses reflect temporal structure of continuous quasi-rhythmic sensory stimulation","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811916306620","volume":"146"},
@@ -191,6 +179,7 @@
{"id":"lietal_PerformingGrouplevelFunctional_2019","accessed":{"date-parts":[["2023",10,20]]},"author":[{"family":"Li","given":"Meiling"},{"family":"Wang","given":"Danhong"},{"family":"Ren","given":"Jianxun"},{"family":"Langs","given":"Georg"},{"family":"Stoecklein","given":"Sophia"},{"family":"Brennan","given":"Brian P."},{"family":"Lu","given":"Jie"},{"family":"Chen","given":"Huafu"},{"family":"Liu","given":"Hesheng"}],"citation-key":"lietal_PerformingGrouplevelFunctional_2019","container-title":"PLOS Biology","container-title-short":"PLoS Biol","DOI":"10.1371/journal.pbio.2007032","editor":[{"family":"Rushworth","given":"Matthew"}],"ISSN":"1545-7885","issue":"3","issued":{"date-parts":[["2019",3,25]]},"language":"en","page":"e2007032","source":"DOI.org (Crossref)","title":"Performing group-level functional image analyses based on homologous functional regions mapped in individuals","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pbio.2007032","volume":"17"},
{"id":"liuetal_ComparisonEEGSource_2023","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Liu","given":"Chang"},{"family":"Downey","given":"Ryan J."},{"family":"Mu","given":"Yiru"},{"family":"Richer","given":"Natalie"},{"family":"Hwang","given":"Jungyun"},{"family":"Shah","given":"Valay A."},{"family":"Sato","given":"Sumire D."},{"family":"Clark","given":"David J."},{"family":"Hass","given":"Chris J."},{"family":"Manini","given":"Todd M."},{"family":"Seidler","given":"Rachael D."},{"family":"Ferris","given":"Daniel P."}],"citation-key":"liuetal_ComparisonEEGSource_2023","container-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","container-title-short":"IEEE Trans. Neural Syst. Rehabil. Eng.","DOI":"10.1109/TNSRE.2023.3281356","ISSN":"1534-4320, 1558-0210","issued":{"date-parts":[["2023"]]},"page":"2591-2602","source":"DOI.org (Crossref)","title":"Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults","type":"article-journal","URL":"https://ieeexplore.ieee.org/document/10138592/","volume":"31"},
{"id":"liuetal_DetectingLargeScaleBrain_2018","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Liu","given":"Quanying"},{"family":"Ganzetti","given":"Marco"},{"family":"Wenderoth","given":"Nicole"},{"family":"Mantini","given":"Dante"}],"citation-key":"liuetal_DetectingLargeScaleBrain_2018","container-title":"Frontiers in Neuroinformatics","container-title-short":"Front. Neuroinform.","DOI":"10.3389/fninf.2018.00004","ISSN":"1662-5196","issued":{"date-parts":[["2018",3,2]]},"page":"4","source":"DOI.org (Crossref)","title":"Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization","title-short":"Detecting Large-Scale Brain Networks Using EEG","type":"article-journal","URL":"http://journal.frontiersin.org/article/10.3389/fninf.2018.00004/full","volume":"12"},
+ {"id":"liuetal_GlobalSignalFMRI_2017","accessed":{"date-parts":[["2024",1,23]]},"author":[{"family":"Liu","given":"Thomas T."},{"family":"Nalci","given":"Alican"},{"family":"Falahpour","given":"Maryam"}],"citation-key":"liuetal_GlobalSignalFMRI_2017","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2017.02.036","ISSN":"10538119","issued":{"date-parts":[["2017",4]]},"language":"en","page":"213-229","source":"DOI.org (Crossref)","title":"The global signal in fMRI: Nuisance or Information?","title-short":"The global signal in fMRI","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811917301477","volume":"150"},
{"id":"liuetal_LargescaleSpontaneousFluctuations_2010","accessed":{"date-parts":[["2023",6,13]]},"author":[{"family":"Liu","given":"Zhongming"},{"family":"Fukunaga","given":"Masaki"},{"family":"De Zwart","given":"Jacco A."},{"family":"Duyn","given":"Jeff H."}],"citation-key":"liuetal_LargescaleSpontaneousFluctuations_2010","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2010.01.092","ISSN":"10538119","issue":"1","issued":{"date-parts":[["2010",5]]},"language":"en","page":"102-111","source":"DOI.org (Crossref)","title":"Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811910001151","volume":"51"},
{"id":"luck_IntroductionEventrelatedPotential_2014","abstract":"The event-related potential (ERP) technique, in which neural responses to specific events are extracted from the EEG, provides a powerful noninvasive tool for exploring the human brain. This volume describes practical methods for ERP research along with the underlying theoretical rationale. It offers researchers and students an essential guide to designing, conducting, and analyzing ERP experiments. This second edition has been completely updated, with additional material, new chapters, and more accessible explanations. Freely available supplementary material, including several online-only chapters, offer expanded or advanced treatment of selected topics. The first half of the book presents essential background information, describing the origins of ERPs, the nature of ERP components, and the design of ERP experiments. The second half of the book offers a detailed treatment of the main steps involved in conducting ERP experiments, covering such topics as recording the EEG, filtering the EEG and ERP waveforms, and quantifying amplitudes and latencies. Throughout, the emphasis is on rigorous experimental design and relatively simple analyses. New material in the second edition includes entire chapters devoted to components, artifacts, measuring amplitudes and latencies, and statistical analysis; updated coverage of recording technologies; concrete examples of experimental design; and many more figures. Online chapters cover such topics as overlap, localization, writing and reviewing ERP papers, and setting up and running an ERP lab.","author":[{"family":"Luck","given":"Steven J."}],"call-number":"QP376.5 .L83 2014","citation-key":"luck_IntroductionEventrelatedPotential_2014","edition":"Second edition","event-place":"Cambridge, Massachusetts","ISBN":"978-0-262-52585-5","issued":{"date-parts":[["2014"]]},"number-of-pages":"406","publisher":"The MIT Press","publisher-place":"Cambridge, Massachusetts","source":"Library of Congress ISBN","title":"An introduction to the event-related potential technique","type":"book"},
{"id":"lundbergetal_WhatYourEstimand_2021","abstract":"We make only one point in this article. Every quantitative study must be able to answer the question: what is your estimand? The estimand is the target quantity—the purpose of the statistical analysis. Much attention is already placed on how to do estimation; a similar degree of care should be given to defining the thing we are estimating. We advocate that authors state the central quantity of each analysis—the theoretical estimand—in precise terms that exist outside of any statistical model. In our framework, researchers do three things: (1) set a theoretical estimand, clearly connecting this quantity to theory; (2) link to an empirical estimand, which is informative about the theoretical estimand under some identification assumptions; and (3) learn from data. Adding precise estimands to research practice expands the space of theoretical questions, clarifies how evidence can speak to those questions, and unlocks new tools for estimation. By grounding all three steps in a precise statement of the target quantity, our framework connects statistical evidence to theory.","accessed":{"date-parts":[["2023",8,23]]},"author":[{"family":"Lundberg","given":"Ian"},{"family":"Johnson","given":"Rebecca"},{"family":"Stewart","given":"Brandon M."}],"citation-key":"lundbergetal_WhatYourEstimand_2021","container-title":"American Sociological Review","container-title-short":"Am Sociol Rev","DOI":"10.1177/00031224211004187","ISSN":"0003-1224, 1939-8271","issue":"3","issued":{"date-parts":[["2021",6]]},"language":"en","page":"532-565","source":"DOI.org (Crossref)","title":"What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory","title-short":"What Is Your Estimand?","type":"article-journal","URL":"http://journals.sagepub.com/doi/10.1177/00031224211004187","volume":"86"},
@@ -201,8 +190,8 @@
{"id":"mareketal_ReproducibleBrainWideAssociation_2020","abstract":"Magnetic resonance imaging (MRI) continues to drive many important neuroscientific advances. However, progress in uncovering reproducible associations between individual differences in brain structure/function and behavioral phenotypes (e.g., cognition, mental health) may have been undermined by typical neuroimaging sample sizes (median N=25) 1,2. Leveraging the Adolescent Brain Cognitive Development (ABCD) Study 3 (N=11,878), we estimated the effect sizes and reproducibility of these brain-wide associations studies (BWAS) as a function of sample size. The very largest, replicable brain-wide associations for univariate and multivariate methods were r=0.14 and r=0.34, respectively. In smaller samples, typical for brain-wide association studies (BWAS), irreproducible, inflated effect sizes were ubiquitous, no matter the method (univariate, multivariate). Until sample sizes started to approach consortium-levels, BWAS were underpowered and statistical errors assured. Multiple factors contribute to replication failures 4-6; here, we show that the pairing of small brain-behavioral phenotype effect sizes with sampling variability is a key element in wide-spread BWAS replication failure. Brain-behavioral phenotype associations stabilize and become more reproducible with sample sizes of N⪆2,000. While investigator-initiated brain-behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible human brain-wide association studies.","accessed":{"date-parts":[["2020",10,5]]},"author":[{"family":"Marek","given":"Scott"},{"family":"Tervo-Clemmens","given":"Brenden"},{"family":"Calabro","given":"Finnegan J."},{"family":"Montez","given":"David F."},{"family":"Kay","given":"Benjamin P."},{"family":"Hatoum","given":"Alexander S."},{"family":"Donohue","given":"Meghan Rose"},{"family":"Foran","given":"William"},{"family":"Miller","given":"Ryland L."},{"family":"Feczko","given":"Eric"},{"family":"Miranda-Dominguez","given":"Oscar"},{"family":"Graham","given":"Alice M."},{"family":"Earl","given":"Eric A."},{"family":"Perrone","given":"Anders J."},{"family":"Cordova","given":"Michaela"},{"family":"Doyle","given":"Olivia"},{"family":"Moore","given":"Lucille A."},{"family":"Conan","given":"Greg"},{"family":"Uriarte","given":"Johnny"},{"family":"Snider","given":"Kathy"},{"family":"Tam","given":"Angela"},{"family":"Chen","given":"Jianzhong"},{"family":"Newbold","given":"Dillan J."},{"family":"Zheng","given":"Annie"},{"family":"Seider","given":"Nicole A."},{"family":"Van","given":"Andrew N."},{"family":"Laumann","given":"Timothy O."},{"family":"Thompson","given":"Wesley K."},{"family":"Greene","given":"Deanna J."},{"family":"Petersen","given":"Steven E."},{"family":"Nichols","given":"Thomas E."},{"family":"Yeo","given":"B. T. Thomas"},{"family":"Barch","given":"Deanna M."},{"family":"Garavan","given":"Hugh"},{"family":"Luna","given":"Beatriz"},{"family":"Fair","given":"Damien A."},{"family":"Dosenbach","given":"Nico U. F."}],"citation-key":"mareketal_ReproducibleBrainWideAssociation_2020","container-title":"bioRxiv","DOI":"10.1101/2020.08.21.257758","issued":{"date-parts":[["2020",8,22]]},"language":"en","license":"© 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.","page":"2020.08.21.257758","publisher":"Cold Spring Harbor Laboratory","section":"New Results","source":"www.biorxiv.org","title":"Towards Reproducible Brain-Wide Association Studies","type":"article-journal","URL":"https://www.biorxiv.org/content/10.1101/2020.08.21.257758v1"},
{"id":"mareketal_SpatialTemporalOrganization_2018","accessed":{"date-parts":[["2024",1,12]]},"author":[{"family":"Marek","given":"Scott"},{"family":"Siegel","given":"Joshua S."},{"family":"Gordon","given":"Evan M."},{"family":"Raut","given":"Ryan V."},{"family":"Gratton","given":"Caterina"},{"family":"Newbold","given":"Dillan J."},{"family":"Ortega","given":"Mario"},{"family":"Laumann","given":"Timothy O."},{"family":"Adeyemo","given":"Babatunde"},{"family":"Miller","given":"Derek B."},{"family":"Zheng","given":"Annie"},{"family":"Lopez","given":"Katherine C."},{"family":"Berg","given":"Jeffrey J."},{"family":"Coalson","given":"Rebecca S."},{"family":"Nguyen","given":"Annie L."},{"family":"Dierker","given":"Donna"},{"family":"Van","given":"Andrew N."},{"family":"Hoyt","given":"Catherine R."},{"family":"McDermott","given":"Kathleen B."},{"family":"Norris","given":"Scott A."},{"family":"Shimony","given":"Joshua S."},{"family":"Snyder","given":"Abraham Z."},{"family":"Nelson","given":"Steven M."},{"family":"Barch","given":"Deanna M."},{"family":"Schlaggar","given":"Bradley L."},{"family":"Raichle","given":"Marcus E."},{"family":"Petersen","given":"Steven E."},{"family":"Greene","given":"Deanna J."},{"family":"Dosenbach","given":"Nico U.F."}],"citation-key":"mareketal_SpatialTemporalOrganization_2018","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2018.10.010","ISSN":"08966273","issue":"4","issued":{"date-parts":[["2018",11]]},"language":"en","page":"977-993.e7","source":"DOI.org (Crossref)","title":"Spatial and Temporal Organization of the Individual Human Cerebellum","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627318308985","volume":"100"},
{"id":"marinoetal_HemodynamicCorrelatesElectrophysiological_2019","accessed":{"date-parts":[["2022",8,1]]},"author":[{"family":"Marino","given":"Marco"},{"family":"Arcara","given":"Giorgio"},{"family":"Porcaro","given":"Camillo"},{"family":"Mantini","given":"Dante"}],"citation-key":"marinoetal_HemodynamicCorrelatesElectrophysiological_2019","container-title":"Frontiers in Neuroscience","container-title-short":"Front. Neurosci.","DOI":"10.3389/fnins.2019.01060","ISSN":"1662-453X","issued":{"date-parts":[["2019",10,4]]},"page":"1060","source":"DOI.org (Crossref)","title":"Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network","type":"article-journal","URL":"https://www.frontiersin.org/article/10.3389/fnins.2019.01060/full","volume":"13"},
+ {"id":"matkovicetal_StaticDynamicFMRIderived_2023","abstract":"Abstract\n Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson’s/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.","accessed":{"date-parts":[["2024",1,24]]},"author":[{"family":"Matkovič","given":"Andraž"},{"family":"Anticevic","given":"Alan"},{"family":"Murray","given":"John D."},{"family":"Repovš","given":"Grega"}],"citation-key":"matkovicetal_StaticDynamicFMRIderived_2023","container-title":"Network Neuroscience","DOI":"10.1162/netn_a_00325","ISSN":"2472-1751","issue":"4","issued":{"date-parts":[["2023",12,22]]},"language":"en","page":"1266-1301","source":"DOI.org (Crossref)","title":"Static and dynamic fMRI-derived functional connectomes represent largely similar information","type":"article-journal","URL":"https://direct.mit.edu/netn/article/7/4/1266/116419/Static-and-dynamic-fMRI-derived-functional","volume":"7"},
{"id":"mayeretal_ExploratoryAnalysisMultiple_2011","abstract":"The integration of multiple high-dimensional data sets (omics data) has been a very active but challenging area of bioinformatics research in recent years. Various adaptations of non-standard multivariate statistical tools have been suggested that allow to analyze and visualize such data sets simultaneously. However, these methods typically can deal with two data sets only, whereas systems biology experiments often generate larger numbers of high-dimensional data sets. For this reason, we suggest an explorative analysis of similarity between data sets as an initial analysis steps. This analysis is based on the RV coefficient, a matrix correlation, that can be interpreted as a generalization of the squared correlation from two single variables to two sets of variables. It has been shown before however that the high-dimensionality of the data introduces substantial bias to the RV.We therefore introduce an alternative version, the adjusted RV, which is unbiased in the case of independent data sets. We can also show that in many situations, particularly for very high-dimensional data sets, the adjusted RV is a better estimator than previously RV versions in terms of the mean square error and the power of the independence test based on it. We demonstrate the usefulness of the adjusted RV by applying it to data set of 19 different multivariate data sets from a systems biology experiment. The pairwise RV values between the data sets define a similarity matrix that we can use as an input to a hierarchical clustering or a multi-dimensional scaling. We show that this reveals biological meaningful subgroups of data sets in our study.","accessed":{"date-parts":[["2022",4,12]]},"author":[{"family":"Mayer","given":"Claus-Dieter"},{"family":"Lorent","given":"Julie"},{"family":"Horgan","given":"Graham W."}],"citation-key":"mayeretal_ExploratoryAnalysisMultiple_2011","container-title":"Statistical Applications in Genetics and Molecular Biology","DOI":"10.2202/1544-6115.1540","ISSN":"1544-6115","issue":"1","issued":{"date-parts":[["2011",3,2]]},"language":"en","source":"www.degruyter.com","title":"Exploratory Analysis of Multiple Omics Datasets Using the Adjusted RV Coefficient","type":"article-journal","URL":"https://www.degruyter.com/document/doi/10.2202/1544-6115.1540/html","volume":"10"},
- {"id":"mccannbeltrachini_ImpactSkullSutures_2022","abstract":"Abstract\n \n Objective\n . Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions.\n Approach\n . Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model.\n Main results\n . Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines.\n Significance\n . It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"McCann","given":"Hannah"},{"family":"Beltrachini","given":"Leandro"}],"citation-key":"mccannbeltrachini_ImpactSkullSutures_2022","container-title":"Journal of Neural Engineering","container-title-short":"J. Neural Eng.","DOI":"10.1088/1741-2552/ac43f7","ISSN":"1741-2560, 1741-2552","issue":"1","issued":{"date-parts":[["2022",2,1]]},"page":"016014","source":"DOI.org (Crossref)","title":"Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1741-2552/ac43f7","volume":"19"},
{"id":"mccullochneuhaus_MisspecifyingShapeRandom_2011","abstract":"Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitudinal data, we examine the impact of random effects distribution misspecification on a variety of inferences, including prediction, inference about covariate effects, prediction of random effects and estimation of random effects variances. We describe examples, theoretical calculations and simulations to elucidate situations in which the specification is and is not important. A key conclusion is the large degree of robustness of maximum likelihood for a wide variety of commonly encountered situations.","accessed":{"date-parts":[["2022",6,16]]},"author":[{"family":"McCulloch","given":"Charles E."},{"family":"Neuhaus","given":"John M."}],"citation-key":"mccullochneuhaus_MisspecifyingShapeRandom_2011","container-title":"Statistical Science","container-title-short":"Statist. Sci.","DOI":"10.1214/11-STS361","ISSN":"0883-4237","issue":"3","issued":{"date-parts":[["2011",8,1]]},"source":"arXiv.org","title":"Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter","title-short":"Misspecifying the Shape of a Random Effects Distribution","type":"article-journal","URL":"http://arxiv.org/abs/1201.1980","volume":"26"},
{"id":"mehrkanoonetal_IntrinsicCouplingModes_2014","accessed":{"date-parts":[["2023",12,10]]},"author":[{"family":"Mehrkanoon","given":"Saeid"},{"family":"Breakspear","given":"Michael"},{"family":"Britz","given":"Juliane"},{"family":"Boonstra","given":"Tjeerd W."}],"citation-key":"mehrkanoonetal_IntrinsicCouplingModes_2014","container-title":"Brain Connectivity","container-title-short":"Brain Connectivity","DOI":"10.1089/brain.2014.0280","ISSN":"2158-0014, 2158-0022","issue":"10","issued":{"date-parts":[["2014",12]]},"language":"en","page":"812-825","source":"DOI.org (Crossref)","title":"Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography","type":"article-journal","URL":"http://www.liebertpub.com/doi/10.1089/brain.2014.0280","volume":"4"},
{"id":"meyerschvaneveldt_FacilitationRecognizingPairs_1971","abstract":"Presented 2 strings of letters simultaneously, with 1 string displayed visually above the other, to high school students (n = 24). In exp. I, ss responded \"yes\" if both strings were words, otherwise responding \"no.\" in exp. Ii, ss responded \"same\" if the 2 strings were either both words or both nonwords, otherwise responding \"different.\" \"yes\" responses and \"same\" responses were faster for pairs of commonly associated words than for pairs of unassociated words. \"same\" responses were slowest for pairs of nonwords. \"no\" responses were faster when the top string in the display was a nonword, whereas \"different\" responses were faster when the top string was a word. Results support a retrieval model involving a dependence between separate successive decisions about whether each of the 2 strings is a word. Possible mechanisms that underlie this dependence are discussed.","author":[{"family":"Meyer","given":"D. E."},{"family":"Schvaneveldt","given":"R. W."}],"citation-key":"meyerschvaneveldt_FacilitationRecognizingPairs_1971","container-title":"Journal of Experimental Psychology","container-title-short":"J Exp Psychol","DOI":"10.1037/h0031564","ISSN":"0022-1015","issue":"2","issued":{"date-parts":[["1971",10]]},"language":"eng","page":"227-234","PMID":"5134329","source":"PubMed","title":"Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations","title-short":"Facilitation in recognizing pairs of words","type":"article-journal","volume":"90"},
@@ -229,12 +218,11 @@
{"id":"ogawaetal_BrainMagneticResonance_1990","abstract":"Paramagnetic deoxyhemoglobin in venous blood is a naturally occurring contrast agent for magnetic resonance imaging (MRI). By accentuating the effects of this agent through the use of gradient-echo techniques in high fields, we demonstrate in vivo images of brain microvasculature with image contrast reflecting the blood oxygen level. This blood oxygenation level-dependent (BOLD) contrast follows blood oxygen changes induced by anesthetics, by insulin-induced hypoglycemia, and by inhaled gas mixtures that alter metabolic demand or blood flow. The results suggest that BOLD contrast can be used to provide in vivo real-time maps of blood oxygenation in the brain under normal physiological conditions. BOLD contrast adds an additional feature to magnetic resonance imaging and complements other techniques that are attempting to provide positron emission tomography-like measurements related to regional neural activity.","author":[{"family":"Ogawa","given":"S."},{"family":"Lee","given":"T. M."},{"family":"Kay","given":"A. R."},{"family":"Tank","given":"D. W."}],"citation-key":"ogawaetal_BrainMagneticResonance_1990","container-title":"Proceedings of the National Academy of Sciences of the United States of America","container-title-short":"Proc Natl Acad Sci U S A","DOI":"10.1073/pnas.87.24.9868","ISSN":"0027-8424","issue":"24","issued":{"date-parts":[["1990",12]]},"language":"eng","page":"9868-9872","PMCID":"PMC55275","PMID":"2124706","source":"PubMed","title":"Brain magnetic resonance imaging with contrast dependent on blood oxygenation","type":"article-journal","volume":"87"},
{"id":"olguin-rodriguezetal_CharacteristicFluctuationsStable_2018","accessed":{"date-parts":[["2022",7,14]]},"author":[{"family":"Olguín-Rodríguez","given":"Paola V."},{"family":"Arzate-Mena","given":"J. Daniel"},{"family":"Corsi-Cabrera","given":"Maria"},{"family":"Gast","given":"Heidemarie"},{"family":"Marín-García","given":"Arlex"},{"family":"Mathis","given":"Johannes"},{"family":"Ramos Loyo","given":"Julieta"},{"family":"Rio-Portilla","given":"Irma Yolanda","non-dropping-particle":"del"},{"family":"Rummel","given":"Christian"},{"family":"Schindler","given":"Kaspar"},{"family":"Müller","given":"Markus"}],"citation-key":"olguin-rodriguezetal_CharacteristicFluctuationsStable_2018","container-title":"Brain Connectivity","container-title-short":"Brain Connectivity","DOI":"10.1089/brain.2018.0609","ISSN":"2158-0014, 2158-0022","issue":"8","issued":{"date-parts":[["2018",10]]},"language":"en","page":"457-474","source":"DOI.org (Crossref)","title":"Characteristic Fluctuations Around Stable Attractor Dynamics Extracted from Highly Nonstationary Electroencephalographic Recordings","type":"article-journal","URL":"https://www.liebertpub.com/doi/10.1089/brain.2018.0609","volume":"8"},
{"id":"palva_PhaseSynchronyNeuronal_2005","accessed":{"date-parts":[["2022",8,24]]},"author":[{"family":"Palva","given":"J. M."}],"citation-key":"palva_PhaseSynchronyNeuronal_2005","container-title":"Journal of Neuroscience","container-title-short":"Journal of Neuroscience","DOI":"10.1523/JNEUROSCI.4250-04.2005","ISSN":"0270-6474, 1529-2401","issue":"15","issued":{"date-parts":[["2005",4,13]]},"language":"en","page":"3962-3972","source":"DOI.org (Crossref)","title":"Phase Synchrony among Neuronal Oscillations in the Human Cortex","type":"article-journal","URL":"https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.4250-04.2005","volume":"25"},
- {"id":"pangetal_GeometricConstraintsHuman_2023","abstract":"Abstract\n \n The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. The classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected by a complex array of axonal fibres\n 1–3\n . However, predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity\n 4–6\n , suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity\n 7,8\n . Here, we confirm these theoretical predictions by analysing human magnetic resonance imaging data acquired under spontaneous and diverse task-evoked conditions. Specifically, we show that cortical and subcortical activity can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brain’s geometry (that is, its shape) rather than from modes of complex interregional connectivity, as classically assumed. We then use these geometric modes to show that task-evoked activations across over 10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning over 60 mm. Finally, we confirm predictions that the close link between geometry and function is explained by a dominant role for wave-like activity, showing that wave dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views and identify a previously underappreciated role of geometry in shaping function, as predicted by a unifying and physically principled model of brain-wide dynamics.","accessed":{"date-parts":[["2023",6,14]]},"author":[{"family":"Pang","given":"James C."},{"family":"Aquino","given":"Kevin M."},{"family":"Oldehinkel","given":"Marianne"},{"family":"Robinson","given":"Peter A."},{"family":"Fulcher","given":"Ben D."},{"family":"Breakspear","given":"Michael"},{"family":"Fornito","given":"Alex"}],"citation-key":"pangetal_GeometricConstraintsHuman_2023","container-title":"Nature","container-title-short":"Nature","DOI":"10.1038/s41586-023-06098-1","ISSN":"0028-0836, 1476-4687","issue":"7965","issued":{"date-parts":[["2023",6,15]]},"language":"en","page":"566-574","source":"DOI.org (Crossref)","title":"Geometric constraints on human brain function","type":"article-journal","URL":"https://www.nature.com/articles/s41586-023-06098-1","volume":"618"},
{"id":"parraetal_DementiaLatinAmerica_2018","abstract":"The demographic structure of Latin American countries (LAC) is fast approaching that of developing countries, and the predicted prevalence of dementia in the former already exceeds the latter. Dementia has been declared a global challenge, yet regions around the world show differences in both the nature and magnitude of such a challenge. This article provides evidence and insights on barriers which, if overcome, would enable the harmonization of strategies to tackle the dementia challenge in LAC. First, we analyze the lack of available epidemiologic data, the need for standardizing clinical practice and improving physician training, and the existing barriers regarding resources, culture, and stigmas. We discuss how these are preventing timely care and research. Regarding specific health actions, most LAC have minimal mental health facilities and do not have specific mental health policies or budgets specific to dementia. In addition, local regulations may need to consider the regional context when developing treatment and prevention strategies. The support needed nationally and internationally to enable a smooth and timely transition of LAC to a position that integrates global strategies is highlighted. We focus on shared issues of poverty, cultural barriers, and socioeconomic vulnerability. We identify avenues for collaboration aimed to study unique populations, improve valid assessment methods, and generate opportunities for translational research, thus establishing a regional network. The issues identified here point to future specific actions aimed at tackling the dementia challenge in LAC.","accessed":{"date-parts":[["2023",6,29]]},"author":[{"family":"Parra","given":"Mario A."},{"family":"Baez","given":"Sandra"},{"family":"Allegri","given":"Ricardo"},{"family":"Nitrini","given":"Ricardo"},{"family":"Lopera","given":"Francisco"},{"family":"Slachevsky","given":"Andrea"},{"family":"Custodio","given":"Nilton"},{"family":"Lira","given":"David"},{"family":"Piguet","given":"Olivier"},{"family":"Kumfor","given":"Fiona"},{"family":"Huepe","given":"David"},{"family":"Cogram","given":"Patricia"},{"family":"Bak","given":"Thomas"},{"family":"Manes","given":"Facundo"},{"family":"Ibanez","given":"Agustin"}],"citation-key":"parraetal_DementiaLatinAmerica_2018","container-title":"Neurology","container-title-short":"Neurology","DOI":"10.1212/WNL.0000000000004897","ISSN":"0028-3878, 1526-632X","issue":"5","issued":{"date-parts":[["2018",1,30]]},"language":"en","page":"222-231","source":"DOI.org (Crossref)","title":"Dementia in Latin America: Assessing the present and envisioning the future","title-short":"Dementia in Latin America","type":"article-journal","URL":"https://www.neurology.org/lookup/doi/10.1212/WNL.0000000000004897","volume":"90"},
- {"id":"pattersonetal_StressinducedHemoconcentrationBlood_1995","accessed":{"date-parts":[["2023",4,25]]},"author":[{"family":"Patterson","given":"Stephen M."},{"family":"Matthews","given":"Karen A."},{"family":"Allen","given":"Michael T."},{"family":"Owens","given":"Jane F."}],"citation-key":"pattersonetal_StressinducedHemoconcentrationBlood_1995","container-title":"Health Psychology","container-title-short":"Health Psychology","DOI":"10.1037/0278-6133.14.4.319","ISSN":"1930-7810, 0278-6133","issue":"4","issued":{"date-parts":[["1995"]]},"language":"en","page":"319-324","source":"DOI.org (Crossref)","title":"Stress-induced hemoconcentration of blood cells and lipids in healthy women during acute psychological stress.","type":"article-journal","URL":"http://doi.apa.org/getdoi.cfm?doi=10.1037/0278-6133.14.4.319","volume":"14"},
{"id":"penttonenbuzsaki_NaturalLogarithmicRelationship_2003","accessed":{"date-parts":[["2023",8,10]]},"author":[{"family":"Penttonen","given":"Markku"},{"family":"Buzsáki","given":"György"}],"citation-key":"penttonenbuzsaki_NaturalLogarithmicRelationship_2003","container-title":"Thalamus and Related Systems","container-title-short":"THL","DOI":"10.1017/S1472928803000074","ISSN":"1472-9288, 1744-8107","issue":"02","issued":{"date-parts":[["2003",4]]},"language":"en","page":"145","source":"DOI.org (Crossref)","title":"Natural logarithmic relationship between brain oscillators","type":"article-journal","URL":"http://www.journals.cambridge.org/abstract_S1472928803000074","volume":"2"},
{"id":"perrinetal_SphericalSplinesScalp_1989","abstract":"Description of mapping methods using spherical splines, both to interpolate scalp potentials (SPs), and to approximate scalp current densities (SCDs). Compared to a previously published method using thin plate splines, the advantages are a very simple derivation of the SCD approximation, faster computing times, and greater accuracy in areas with few electrodes.","accessed":{"date-parts":[["2021",11,11]]},"author":[{"family":"Perrin","given":"F."},{"family":"Pernier","given":"J."},{"family":"Bertrand","given":"O."},{"family":"Echallier","given":"J. F."}],"citation-key":"perrinetal_SphericalSplinesScalp_1989","container-title":"Electroencephalography and Clinical Neurophysiology","container-title-short":"Electroencephalography and Clinical Neurophysiology","DOI":"10.1016/0013-4694(89)90180-6","ISSN":"0013-4694","issue":"2","issued":{"date-parts":[["1989",2,1]]},"language":"en","page":"184-187","source":"ScienceDirect","title":"Spherical splines for scalp potential and current density mapping","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/0013469489901806","volume":"72"},
{"id":"pervaizetal_OptimisingNetworkModelling_2020","accessed":{"date-parts":[["2023",10,24]]},"author":[{"family":"Pervaiz","given":"Usama"},{"family":"Vidaurre","given":"Diego"},{"family":"Woolrich","given":"Mark W."},{"family":"Smith","given":"Stephen M."}],"citation-key":"pervaizetal_OptimisingNetworkModelling_2020","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2020.116604","ISSN":"10538119","issued":{"date-parts":[["2020",5]]},"language":"en","page":"116604","source":"DOI.org (Crossref)","title":"Optimising network modelling methods for fMRI","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811920300914","volume":"211"},
+ {"id":"pessoa_UnderstandingBrainNetworks_2014","abstract":"What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal \"true\" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different \"slices\" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.","author":[{"family":"Pessoa","given":"Luiz"}],"citation-key":"pessoa_UnderstandingBrainNetworks_2014","container-title":"Physics of Life Reviews","container-title-short":"Phys Life Rev","DOI":"10.1016/j.plrev.2014.03.005","ISSN":"1873-1457","issue":"3","issued":{"date-parts":[["2014",9]]},"language":"eng","page":"400-435","PMCID":"PMC4157099","PMID":"24819881","source":"PubMed","title":"Understanding brain networks and brain organization","type":"article-journal","volume":"11"},
{"id":"petersensporns_BrainNetworksCognitive_2015","accessed":{"date-parts":[["2023",6,9]]},"author":[{"family":"Petersen","given":"Steven E."},{"family":"Sporns","given":"Olaf"}],"citation-key":"petersensporns_BrainNetworksCognitive_2015","container-title":"Neuron","container-title-short":"Neuron","DOI":"10.1016/j.neuron.2015.09.027","ISSN":"08966273","issue":"1","issued":{"date-parts":[["2015",10]]},"language":"en","page":"207-219","source":"DOI.org (Crossref)","title":"Brain Networks and Cognitive Architectures","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0896627315008168","volume":"88"},
{"id":"picchionietal_SleepFunctionalConnectome_2013","accessed":{"date-parts":[["2023",6,9]]},"author":[{"family":"Picchioni","given":"Dante"},{"family":"Duyn","given":"Jeff H."},{"family":"Horovitz","given":"Silvina G."}],"citation-key":"picchionietal_SleepFunctionalConnectome_2013","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2013.05.067","ISSN":"10538119","issued":{"date-parts":[["2013",10]]},"language":"en","page":"387-396","source":"DOI.org (Crossref)","title":"Sleep and the functional connectome","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811913005673","volume":"80"},
{"id":"poldracketal_LongtermNeuralPhysiological_2015","abstract":"Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.","accessed":{"date-parts":[["2021",10,22]]},"author":[{"family":"Poldrack","given":"Russell A."},{"family":"Laumann","given":"Timothy O."},{"family":"Koyejo","given":"Oluwasanmi"},{"family":"Gregory","given":"Brenda"},{"family":"Hover","given":"Ashleigh"},{"family":"Chen","given":"Mei-Yen"},{"family":"Gorgolewski","given":"Krzysztof J."},{"family":"Luci","given":"Jeffrey"},{"family":"Joo","given":"Sung Jun"},{"family":"Boyd","given":"Ryan L."},{"family":"Hunicke-Smith","given":"Scott"},{"family":"Simpson","given":"Zack Booth"},{"family":"Caven","given":"Thomas"},{"family":"Sochat","given":"Vanessa"},{"family":"Shine","given":"James M."},{"family":"Gordon","given":"Evan M."},{"family":"Snyder","given":"Abraham Z."},{"family":"Adeyemo","given":"Babatunde"},{"family":"Petersen","given":"Steven E."},{"family":"Glahn","given":"David C."},{"family":"Reese Mckay","given":"D."},{"family":"Curran","given":"Joanne E."},{"family":"Göring","given":"Harald H. H."},{"family":"Carless","given":"Melanie A."},{"family":"Blangero","given":"John"},{"family":"Dougherty","given":"Robert"},{"family":"Leemans","given":"Alexander"},{"family":"Handwerker","given":"Daniel A."},{"family":"Frick","given":"Laurie"},{"family":"Marcotte","given":"Edward M."},{"family":"Mumford","given":"Jeanette A."}],"citation-key":"poldracketal_LongtermNeuralPhysiological_2015","container-title":"Nature Communications","container-title-short":"Nat Commun","DOI":"10.1038/ncomms9885","ISSN":"2041-1723","issue":"1","issued":{"date-parts":[["2015",12,9]]},"language":"en","license":"2015 The Author(s)","note":"Bandiera_abtest: a\nCc_license_type: cc_by\nCg_type: Nature Research Journals\nPrimary_atype: Research\nSubject_term: Computational neuroscience;Dynamic networks;Psychiatric disorders\nSubject_term_id: computational-neuroscience;dynamic-networks;psychiatric-disorders","number":"1","page":"8885","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Long-term neural and physiological phenotyping of a single human","type":"article-journal","URL":"https://www.nature.com/articles/ncomms9885","volume":"6"},
@@ -253,7 +241,6 @@
{"id":"rolandzilles_StructuralDivisionsFunctional_1998","abstract":"The question of what is a cortical area needs a thorough definition of borders both in the microstructural and the functional domains. Microstructural parcellation of the human cerebral cortex should be made on multiple criteria based on quantitative measurements of microstructural variables, such as neuron densities, neurotransmitter receptor densities, enzyme densities, etc. Because of the inter-individual variations of extent and topography of microstructurally defined areas, the final microstructurally defined areas appear as population maps. In the functional domain, columns, patches and blobs signifying synaptically active parts of the cortex appear as cortical functional fields. These fields are the largest functional entities of the cerebral cortex according to the cortical field hypothesis. In its strong version, the cortical field hypothesis postulates that all neurons and synapses within the fields perform a co-operative computation. A number of such fields together provide the functional contribution of the cerebral cortex. The functional parcellation of the human cerebral cortex must be based on field population maps, which after intersection analysis appear as functional domains. The major structural-functional hypothesis to be examined is whether these functional domains are equi-territorial to the microstructurally defined meta-maps. The cortical hypothesis predicts that, if two brain tasks make use of one or several identical or largely overlapping fields, they cannot be performed simultaneously without errors or increases in latency. Evidence for such interference is presented. This evidence represents a restriction in the parallel processing of the human brain. In the posterior part of the brain not only visual cortical areas may qualify for parallel processing, but also the somatosensory cortices appear to have separate functional streams for the detection of microgeometry and macrogeometry.","author":[{"family":"Roland","given":"P. E."},{"family":"Zilles","given":"K."}],"citation-key":"rolandzilles_StructuralDivisionsFunctional_1998","container-title":"Brain Research. Brain Research Reviews","container-title-short":"Brain Res Brain Res Rev","DOI":"10.1016/s0165-0173(97)00058-1","issue":"2-3","issued":{"date-parts":[["1998",5]]},"language":"eng","page":"87-105","PMID":"9651489","source":"PubMed","title":"Structural divisions and functional fields in the human cerebral cortex","type":"article-journal","volume":"26"},
{"id":"rosenbergetal_BehavioralNeuralSignatures_2020","abstract":"Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here we establish associations between working memory, cognitive abilities, and functional MRI activation in data from over 11,500 9–11-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity specifically during a working memory challenge indexes working memory performance. This relationship is domain-specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task, and reward processing during a monetary incentive delay task does not track memory abilities. Together these results inform our understanding of individual differences in working memory in childhood and lay the groundwork for characterizing the ways in which they change across adolescence.\nSignificance statement\nWorking memory is a foundational cognitive ability that changes over time and varies across individuals. Here we analyze data from over 11,500 9–11-year-olds to establish relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges. Our results lay the groundwork for assessing longitudinal changes in working memory and predicting later academic and other real-world outcomes.","accessed":{"date-parts":[["2020",6,15]]},"author":[{"family":"Rosenberg","given":"Monica D."},{"family":"Martinez","given":"Steven A."},{"family":"Rapuano","given":"Kristina M."},{"family":"Conley","given":"May I."},{"family":"Cohen","given":"Alexandra O."},{"family":"Cornejo","given":"M. Daniela"},{"family":"Hagler","given":"Donald J."},{"family":"Meredith","given":"Wesley J."},{"family":"Anderson","given":"Kevin M."},{"family":"Wager","given":"Tor D."},{"family":"Feczko","given":"Eric"},{"family":"Earl","given":"Eric"},{"family":"Fair","given":"Damien A."},{"family":"Barch","given":"Deanna M."},{"family":"Watts","given":"Richard"},{"family":"Casey","given":"B. J."}],"citation-key":"rosenbergetal_BehavioralNeuralSignatures_2020","container-title":"Journal of Neuroscience","container-title-short":"J. Neurosci.","DOI":"10.1523/JNEUROSCI.2841-19.2020","ISSN":"0270-6474, 1529-2401","issued":{"date-parts":[["2020",5,25]]},"language":"en","license":"Copyright © 2020 the authors","PMID":"32451322","publisher":"Society for Neuroscience","section":"Research Report: Regular Manuscript","source":"www.jneurosci.org","title":"Behavioral and neural signatures of working memory in childhood","type":"article-journal","URL":"https://www.jneurosci.org/content/early/2020/05/19/JNEUROSCI.2841-19.2020"},
{"id":"rosenbergetal_NeuromarkerSustainedAttention_2016","accessed":{"date-parts":[["2023",10,24]]},"author":[{"family":"Rosenberg","given":"Monica D"},{"family":"Finn","given":"Emily S"},{"family":"Scheinost","given":"Dustin"},{"family":"Papademetris","given":"Xenophon"},{"family":"Shen","given":"Xilin"},{"family":"Constable","given":"R Todd"},{"family":"Chun","given":"Marvin M"}],"citation-key":"rosenbergetal_NeuromarkerSustainedAttention_2016","container-title":"Nature Neuroscience","container-title-short":"Nat Neurosci","DOI":"10.1038/nn.4179","ISSN":"1097-6256, 1546-1726","issue":"1","issued":{"date-parts":[["2016",1]]},"language":"en","page":"165-171","source":"DOI.org (Crossref)","title":"A neuromarker of sustained attention from whole-brain functional connectivity","type":"article-journal","URL":"https://www.nature.com/articles/nn.4179","volume":"19"},
- {"id":"rushtonbarth_WhatEvidenceGender_2010","accessed":{"date-parts":[["2023",4,25]]},"author":[{"family":"Rushton","given":"D. Hugh"},{"family":"Barth","given":"Julian H."}],"citation-key":"rushtonbarth_WhatEvidenceGender_2010","container-title":"Critical Reviews in Oncology/Hematology","container-title-short":"Critical Reviews in Oncology/Hematology","DOI":"10.1016/j.critrevonc.2009.03.010","ISSN":"10408428","issue":"1","issued":{"date-parts":[["2010",1]]},"language":"en","page":"1-9","source":"DOI.org (Crossref)","title":"What is the evidence for gender differences in ferritin and haemoglobin?","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1040842809000766","volume":"73"},
{"id":"ryynanenetal_EffectElectrodeDensity_2004","accessed":{"date-parts":[["2022",7,13]]},"author":[{"family":"Ryynanen","given":"O.R.M."},{"family":"Hyttinen","given":"J.A.K."},{"family":"Laarne","given":"P.H."},{"family":"Malmivuo","given":"J.A."}],"citation-key":"ryynanenetal_EffectElectrodeDensity_2004","container-title":"IEEE Transactions on Biomedical Engineering","container-title-short":"IEEE Trans. Biomed. Eng.","DOI":"10.1109/TBME.2004.828036","ISSN":"0018-9294","issue":"9","issued":{"date-parts":[["2004",9]]},"language":"en","page":"1547-1554","source":"DOI.org (Crossref)","title":"Effect of Electrode Density and Measurement Noise on the Spatial Resolution of Cortical Potential Distribution","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/1325815/","volume":"51"},
{"id":"ryynanenetal_EffectMeasurementNoise_2006","accessed":{"date-parts":[["2022",7,13]]},"author":[{"family":"Ryynanen","given":"O.R.M."},{"family":"Hyttinen","given":"J.A.K."},{"family":"Malmivuo","given":"J.A."}],"citation-key":"ryynanenetal_EffectMeasurementNoise_2006","container-title":"IEEE Transactions on Biomedical Engineering","container-title-short":"IEEE Trans. Biomed. Eng.","DOI":"10.1109/TBME.2006.873744","ISSN":"0018-9294, 1558-2531","issue":"9","issued":{"date-parts":[["2006",9]]},"page":"1851-1858","source":"DOI.org (Crossref)","title":"Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull","type":"article-journal","URL":"http://ieeexplore.ieee.org/document/1673627/","volume":"53"},
{"id":"sadaghianikleinschmidt_BrainNetworksAOscillations_2016","accessed":{"date-parts":[["2022",7,8]]},"author":[{"family":"Sadaghiani","given":"Sepideh"},{"family":"Kleinschmidt","given":"Andreas"}],"citation-key":"sadaghianikleinschmidt_BrainNetworksAOscillations_2016","container-title":"Trends in Cognitive Sciences","container-title-short":"Trends in Cognitive Sciences","DOI":"10.1016/j.tics.2016.09.004","ISSN":"13646613","issue":"11","issued":{"date-parts":[["2016",11]]},"language":"en","page":"805-817","source":"DOI.org (Crossref)","title":"Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control","title-short":"Brain Networks and α-Oscillations","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1364661316301474","volume":"20"},
@@ -312,6 +299,7 @@
{"id":"wardetal_IndividualDifferencesHaemoglobin_2020","accessed":{"date-parts":[["2023",4,25]]},"author":[{"family":"Ward","given":"Phillip G.D."},{"family":"Orchard","given":"Edwina R."},{"family":"Oldham","given":"Stuart"},{"family":"Arnatkevičiūtė","given":"Aurina"},{"family":"Sforazzini","given":"Francesco"},{"family":"Fornito","given":"Alex"},{"family":"Storey","given":"Elsdon"},{"family":"Egan","given":"Gary F."},{"family":"Jamadar","given":"Sharna D."}],"citation-key":"wardetal_IndividualDifferencesHaemoglobin_2020","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2020.117196","ISSN":"10538119","issued":{"date-parts":[["2020",11]]},"language":"en","page":"117196","source":"DOI.org (Crossref)","title":"Individual differences in haemoglobin concentration influence bold fMRI functional connectivity and its correlation with cognition","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811920306820","volume":"221"},
{"id":"wassersteinetal_MovingWorld05_2019","accessed":{"date-parts":[["2021",3,16]]},"author":[{"family":"Wasserstein","given":"Ronald L."},{"family":"Schirm","given":"Allen L."},{"family":"Lazar","given":"Nicole A."}],"citation-key":"wassersteinetal_MovingWorld05_2019","container-title":"The American Statistician","DOI":"10.1080/00031305.2019.1583913","ISSN":"0003-1305","issue":"sup1","issued":{"date-parts":[["2019",3,29]]},"page":"1-19","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"Moving to a World Beyond “p < 0.05”","type":"article-journal","URL":"https://doi.org/10.1080/00031305.2019.1583913","volume":"73"},
{"id":"wassersteinlazar_ASAStatementPValues_2016","accessed":{"date-parts":[["2021",3,16]]},"author":[{"family":"Wasserstein","given":"Ronald L."},{"family":"Lazar","given":"Nicole A."}],"citation-key":"wassersteinlazar_ASAStatementPValues_2016","container-title":"The American Statistician","DOI":"10.1080/00031305.2016.1154108","ISSN":"0003-1305","issue":"2","issued":{"date-parts":[["2016",4,2]]},"page":"129-133","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"The ASA Statement on p-Values: Context, Process, and Purpose","title-short":"The ASA Statement on p-Values","type":"article-journal","URL":"https://doi.org/10.1080/00031305.2016.1154108","volume":"70"},
+ {"id":"whittingstalletal_EffectsDipolePosition_2003","accessed":{"date-parts":[["2024",1,23]]},"author":[{"family":"Whittingstall","given":"Kevin"},{"family":"Stroink","given":"Gerhard"},{"family":"Gates","given":"Larry"},{"family":"Connolly","given":"Jf"},{"family":"Finley","given":"Allen"}],"citation-key":"whittingstalletal_EffectsDipolePosition_2003","container-title":"BioMedical Engineering OnLine","container-title-short":"BioMed Eng OnLine","DOI":"10.1186/1475-925X-2-14","ISSN":"1475-925X","issue":"1","issued":{"date-parts":[["2003",12]]},"language":"en","page":"14","source":"DOI.org (Crossref)","title":"Effects of dipole position, orientation and noise on the accuracy of EEG source localization","type":"article-journal","URL":"https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-2-14","volume":"2"},
{"id":"whittingtonetal_FutureNeuronalOscillation_2018","abstract":"Neuronal oscillations represent the most obvious feature of electrical activity in the brain. They are linked in general with global brain state (awake, asleep, etc.) and specifically with organisation of neuronal outputs during sensory perception and cognitive processing. Oscillations can be generated by individual neurons on the basis of interaction between inputs and intrinsic conductances but are far more commonly seen at the local network level in populations of interconnected neurons with diverse arrays of functional properties. It is at this level that the brain’s rich and diverse library of oscillatory time constants serve to temporally organise large-scale neural activity patterns. The discipline is relatively mature at the microscopic (cell, local network) level – although novel discoveries are still commonplace – but requires a far greater understanding of mesoscopic and macroscopic brain dynamics than we currently hold. Without this, extrapolation from the temporal properties of neurons and their communication strategies up to whole brain function will remain largely theoretical. However, recent advances in large-scale neuronal population recordings and more direct, higher fidelity, non-invasive measurement of whole brain function suggest much progress is just around the corner.","accessed":{"date-parts":[["2023",7,6]]},"author":[{"family":"Whittington","given":"Miles A."},{"family":"Traub","given":"Roger D."},{"family":"Adams","given":"Natalie E."}],"citation-key":"whittingtonetal_FutureNeuronalOscillation_2018","container-title":"Brain and Neuroscience Advances","container-title-short":"Brain and Neuroscience Advances","DOI":"10.1177/2398212818794827","ISSN":"2398-2128, 2398-2128","issued":{"date-parts":[["2018",1]]},"language":"en","page":"239821281879482","source":"DOI.org (Crossref)","title":"A future for neuronal oscillation research","type":"article-journal","URL":"http://journals.sagepub.com/doi/10.1177/2398212818794827","volume":"2"},
{"id":"widmannetal_DigitalFilterDesign_2015","abstract":"Background\nFiltering is a ubiquitous step in the preprocessing of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Besides the intended effect of the attenuation of signal components considered as noise, filtering can also result in various unintended adverse filter effects (distortions such as smoothing) and filter artifacts.\nMethod\nWe give some practical guidelines for the evaluation of filter responses (impulse and frequency response) and the selection of filter types (high-pass/low-pass/band-pass/band-stop; finite/infinite impulse response, FIR/IIR) and filter parameters (cutoff frequencies, filter order and roll-off, ripple, delay and causality) to optimize signal-to-noise ratio and avoid or reduce signal distortions for selected electrophysiological applications.\nResults\nVarious filter implementations in common electrophysiology software packages are introduced and discussed. Resulting filter responses are compared and evaluated.\nConclusion\nWe present strategies for recognizing common adverse filter effects and filter artifacts and demonstrate them in practical examples. Best practices and recommendations for the selection and reporting of filter parameters, limitations, and alternatives to filtering are discussed.","accessed":{"date-parts":[["2021",11,23]]},"author":[{"family":"Widmann","given":"Andreas"},{"family":"Schröger","given":"Erich"},{"family":"Maess","given":"Burkhard"}],"citation-key":"widmannetal_DigitalFilterDesign_2015","collection-title":"Cutting-edge EEG Methods","container-title":"Journal of Neuroscience Methods","container-title-short":"Journal of Neuroscience Methods","DOI":"10.1016/j.jneumeth.2014.08.002","ISSN":"0165-0270","issued":{"date-parts":[["2015",7,30]]},"language":"en","page":"34-46","source":"ScienceDirect","title":"Digital filter design for electrophysiological data – a practical approach","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0165027014002866","volume":"250"},
{"id":"wirsichetal_RelationshipEEGFMRI_2021","accessed":{"date-parts":[["2022",7,14]]},"author":[{"family":"Wirsich","given":"Jonathan"},{"family":"Jorge","given":"João"},{"family":"Iannotti","given":"Giannina Rita"},{"family":"Shamshiri","given":"Elhum A"},{"family":"Grouiller","given":"Frédéric"},{"family":"Abreu","given":"Rodolfo"},{"family":"Lazeyras","given":"François"},{"family":"Giraud","given":"Anne-Lise"},{"family":"Gruetter","given":"Rolf"},{"family":"Sadaghiani","given":"Sepideh"},{"family":"Vulliémoz","given":"Serge"}],"citation-key":"wirsichetal_RelationshipEEGFMRI_2021","container-title":"NeuroImage","container-title-short":"NeuroImage","DOI":"10.1016/j.neuroimage.2021.117864","ISSN":"10538119","issued":{"date-parts":[["2021",5]]},"language":"en","page":"117864","source":"DOI.org (Crossref)","title":"The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1053811921001415","volume":"231"},
@@ -322,6 +310,5 @@
{"id":"yeoetal_OrganizationHumanCerebral_2011","abstract":"Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.","accessed":{"date-parts":[["2023",6,8]]},"author":[{"family":"Yeo","given":"B. T. Thomas"},{"family":"Krienen","given":"Fenna M."},{"family":"Sepulcre","given":"Jorge"},{"family":"Sabuncu","given":"Mert R."},{"family":"Lashkari","given":"Danial"},{"family":"Hollinshead","given":"Marisa"},{"family":"Roffman","given":"Joshua L."},{"family":"Smoller","given":"Jordan W."},{"family":"Zöllei","given":"Lilla"},{"family":"Polimeni","given":"Jonathan R."},{"family":"Fischl","given":"Bruce"},{"family":"Liu","given":"Hesheng"},{"family":"Buckner","given":"Randy L."}],"citation-key":"yeoetal_OrganizationHumanCerebral_2011","container-title":"Journal of Neurophysiology","container-title-short":"Journal of Neurophysiology","DOI":"10.1152/jn.00338.2011","ISSN":"0022-3077, 1522-1598","issue":"3","issued":{"date-parts":[["2011",9]]},"language":"en","page":"1125-1165","source":"DOI.org (Crossref)","title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity","type":"article-journal","URL":"https://www.physiology.org/doi/10.1152/jn.00338.2011","volume":"106"},
{"id":"yuste_NeuronDoctrineNeural_2015","abstract":"For over a century, the neuron doctrine--which states that the neuron is the structural and functional unit of the nervous system--has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.","author":[{"family":"Yuste","given":"Rafael"}],"citation-key":"yuste_NeuronDoctrineNeural_2015","container-title":"Nature Reviews. Neuroscience","container-title-short":"Nat Rev Neurosci","DOI":"10.1038/nrn3962","ISSN":"1471-0048","issue":"8","issued":{"date-parts":[["2015",8]]},"language":"eng","page":"487-497","PMID":"26152865","source":"PubMed","title":"From the neuron doctrine to neural networks","type":"article-journal","volume":"16"},
{"id":"zhangetal_IllusionPredictabilityScientific_2022","abstract":"Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three pre-registered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both, and answered questions about the size and importance of findings they were shown. Our results, comprised of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.","accessed":{"date-parts":[["2023",4,27]]},"author":[{"family":"Zhang","given":"Sam"},{"family":"Heck","given":"Patrick Ryan"},{"family":"Meyer","given":"Michelle"},{"family":"Chabris","given":"Christopher F."},{"family":"Goldstein","given":"Daniel G."},{"family":"Hofman","given":"Jake M."}],"citation-key":"zhangetal_IllusionPredictabilityScientific_2022","DOI":"10.31235/osf.io/5tcgs","issued":{"date-parts":[["2022",4,29]]},"source":"OSF Preprints","title":"An illusion of predictability in scientific results","type":"article","URL":"https://osf.io/preprints/socarxiv/5tcgs/"},
- {"id":"zilles_BrodmannPioneerHuman_2018","accessed":{"date-parts":[["2023",6,14]]},"author":[{"family":"Zilles","given":"Karl"}],"citation-key":"zilles_BrodmannPioneerHuman_2018","container-title":"Brain","DOI":"10.1093/brain/awy273","ISSN":"0006-8950, 1460-2156","issue":"11","issued":{"date-parts":[["2018",11,1]]},"language":"en","page":"3262-3278","source":"DOI.org (Crossref)","title":"Brodmann: a pioneer of human brain mapping—his impact on concepts of cortical organization","title-short":"Brodmann","type":"article-journal","URL":"https://academic.oup.com/brain/article/141/11/3262/5144588","volume":"141"},
{"id":"zillesamunts_IndividualVariabilityNot_2013","accessed":{"date-parts":[["2023",5,31]]},"author":[{"family":"Zilles","given":"Karl"},{"family":"Amunts","given":"Katrin"}],"citation-key":"zillesamunts_IndividualVariabilityNot_2013","container-title":"Trends in Cognitive Sciences","container-title-short":"Trends in Cognitive Sciences","DOI":"10.1016/j.tics.2013.02.003","ISSN":"13646613","issue":"4","issued":{"date-parts":[["2013",4]]},"language":"en","page":"153-155","source":"DOI.org (Crossref)","title":"Individual variability is not noise","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1364661313000454","volume":"17"}
]
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