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Copy pathcalc_cortical_parc_metrics.m
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calc_cortical_parc_metrics.m
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function metric_parcel = calc_cortical_parc_metrics(parc, metric, data_input, ROI_mask)
% calc_cortical_parc_metrics.m
%
% Calculate cortical parcellation performance via metric
%
% Inputs: parc : parcellation [Nx1]
% N = number of vertices
% metric : performance metric to calculate (string)
% Possible metrics:
% 'pca_timeseries',
% 'pca_zFC_vertex_parcel', 'pca_FC_vertex_parcel',
% 'pca_zFC_ROI', 'pca_FC_ROI', 'pca_zFC', 'pca_FC',
% 'mean_zFC', 'mean_FC',
% 'mean_task', 'std_task',
% 'mean_myelin', 'std_myelin',
% 'mean_thickness', 'std_thickness', 'mean_S'
% data_input : data to calculate metric from
% ROI_mask : mask of ROI
% this is only needed for 'pca_zFC_ROI' and 'pca_FC_ROI'
%
% Output: metric_parcel : metric value per parcel
%
% Original: James Pang, Monash University, 2021
%%
warning('off', 'all')
num_vertices = size(parc,1);
parcels = unique(parc(parc>0));
num_parcels = length(parcels);
if nargin<4
ROI_mask = ones(num_vertices,1);
end
if strcmpi(metric, 'pca_timeseries')
metric_parcel = zeros(num_parcels,1);
size_data = size(data_input.data);
data = data_input.data;
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if size_data(1)==num_vertices
data_parcel = data(ind_parcel,:)';
elseif size_data(2)==num_vertices
data_parcel = data(:,ind_parcel);
end
data_parcel_normalized = utils.calc_normalize_timeseries(data_parcel);
data_parcel_normalized(isnan(data_parcel_normalized)) = 0;
warning off
[~,~,~,~,temp_explained] = pca(data_parcel_normalized, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
end
elseif strcmpi(metric, 'pca_zFC_vertex_parcel') || strcmpi(metric, 'pca_FC_vertex_parcel')
metric_parcel = zeros(num_parcels,1);
data_type = data_input.data_type;
size_data = size(data_input.data);
data = data_input.data;
% normalize
if strcmpi(data_type, 'timeseries')
if size_data(1)==num_vertices
data = utils.calc_normalize_timeseries(data');
T = size_data(2);
elseif size_data(2)==num_vertices
data = utils.calc_normalize_timeseries(data);
T = size_data(1);
end
data = data';
end
data_parcellated = utils.calc_parcellate(parc, data);
FC_combined = {};
zFC_combined = {};
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if strcmpi(data_type, 'timeseries')
data_parcel = data(ind_parcel,:);
FC = data_parcellated*data_parcel';
FC = FC/T;
zFC = atanh(FC);
elseif strcmpi(data_type, 'zFC')
FC = tanh(data_parcellated(:,ind_parcel));
zFC = data_parcellated(:,ind_parcel);
elseif strcmpi(data_type, 'FC')
FC = data_parcellated(:,ind_parcel);
zFC = atanh(data_parcellated(:,ind_parcel));
end
FC(isnan(FC)) = 0;
zFC(isnan(zFC)) = 0;
FC_combined{parcel_ind} = FC;
zFC_combined{parcel_ind} = zFC;
end
for parcel_ind = 1:num_parcels
warning('off', 'all')
if strcmpi(metric, 'pca_zFC_vertex_parcel')
% warning('off', 'all')
[~,~,~,~,temp_explained] = pca(zFC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
elseif strcmpi(metric, 'pca_FC_vertex_parcel')
% warning off
[~,~,~,~,temp_explained] = pca(FC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
end
end
elseif strcmpi(metric, 'pca_zFC_ROI') || strcmpi(metric, 'pca_FC_ROI')
metric_parcel = zeros(num_parcels,1);
data_type = data_input.data_type;
size_data = size(data_input.data);
data = data_input.data;
ind_ROI = find(ROI_mask);
FC_combined = {};
zFC_combined = {};
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if strcmpi(data_type, 'timeseries')
if size_data(1)==num_vertices
data_parcel = data(ind_parcel,:)';
data_ROI = data(ind_ROI,:)';
T = size_data(2);
elseif size_data(2)==num_vertices
data_parcel = data(:,ind_parcel);
data_ROI = data(:,ind_ROI);
T = size_data(1);
end
data_parcel_normalized = utils.calc_normalize_timeseries(data_parcel);
data_parcel_normalized(isnan(data_parcel_normalized)) = 0;
data_ROI_normalized = utils.calc_normalize_timeseries(data_ROI);
data_ROI_normalized(isnan(data_ROI_normalized)) = 0;
FC = data_ROI_normalized'*data_parcel_normalized;
FC = FC/T;
zFC = atanh(FC);
elseif strcmpi(data_type, 'zFC')
FC = tanh(data(ind_ROI,ind_parcel));
zFC = data(ind_ROI,ind_parcel);
elseif strcmpi(data_type, 'FC')
FC = data(ind_ROI,ind_parcel);
zFC = atanh(data(ind_ROI,ind_parcel));
end
FC(isnan(FC)) = 0;
zFC(isnan(zFC)) = 0;
FC_combined{parcel_ind} = FC;
zFC_combined{parcel_ind} = zFC;
end
parfor parcel_ind = 1:num_parcels
warning('off', 'all')
if strcmpi(metric, 'pca_zFC_ROI')
% warning('off', 'all')
[~,~,~,~,temp_explained] = pca(zFC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
elseif strcmpi(metric, 'pca_FC_ROI')
% warning off
[~,~,~,~,temp_explained] = pca(FC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
end
end
elseif strcmpi(metric, 'pca_zFC') || strcmpi(metric, 'pca_FC') || strcmpi(metric, 'mean_zFC') || strcmpi(metric, 'mean_FC')
metric_parcel = zeros(num_parcels,1);
data_type = data_input.data_type;
size_data = size(data_input.data);
data = data_input.data;
FC_combined = {};
zFC_combined = {};
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if strcmpi(data_type, 'timeseries')
if size_data(1)==num_vertices
data_parcel = data(ind_parcel,:)';
T = size_data(2);
elseif size_data(2)==num_vertices
data_parcel = data(:,ind_parcel);
T = size_data(1);
end
data_parcel_normalized = utils.calc_normalize_timeseries(data_parcel);
data_parcel_normalized(isnan(data_parcel_normalized)) = 0;
FC = data_parcel_normalized'*data_parcel_normalized;
FC = FC/T;
zFC = atanh(FC);
elseif strcmpi(data_type, 'zFC')
FC = tanh(data(ind_parcel,ind_parcel));
zFC = data(ind_parcel,ind_parcel);
elseif strcmpi(data_type, 'FC')
FC = data(ind_parcel,ind_parcel);
zFC = atanh(data(ind_parcel,ind_parcel));
end
FC(isnan(FC)) = 0;
zFC(isnan(zFC)) = 0;
FC_combined{parcel_ind} = FC;
zFC_combined{parcel_ind} = zFC;
end
parfor parcel_ind = 1:num_parcels
if strcmpi(metric, 'pca_zFC')
warning off
[~,~,~,~,temp_explained] = pca(zFC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
elseif strcmpi(metric, 'pca_FC')
warning off
[~,~,~,~,temp_explained] = pca(FC_combined{parcel_ind}, 'Rows', 'complete');
metric_parcel(parcel_ind) = double(temp_explained(1));
elseif strcmpi(metric, 'mean_zFC')
temp = zFC_combined{parcel_ind};
triu_ind = find(triu(ones(size(temp)),1));
metric_parcel(parcel_ind) = mean(temp(triu_ind));
elseif strcmpi(metric, 'mean_FC')
temp = FC_combined{parcel_ind};
triu_ind = find(triu(ones(size(temp)),1));
metric_parcel(parcel_ind) = mean(temp(triu_ind));
end
end
elseif strcmpi(metric, 'mean_task') || strcmpi(metric, 'std_task')
contrasts = fieldnames(data_input);
num_contrasts = length(contrasts);
metric_parcel = zeros(num_parcels,num_contrasts);
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
for contrast_ind = 1:num_contrasts
contrast = contrasts{contrast_ind};
data_input.(contrast)(isnan(data_input.(contrast))) = 0;
if strcmpi(metric, 'mean_task')
metric_parcel(parcel_ind,contrast_ind) = mean(data_input.(contrast)(ind_parcel));
elseif strcmpi(metric, 'std_task')
metric_parcel(parcel_ind,contrast_ind) = std(data_input.(contrast)(ind_parcel));
end
end
end
elseif strcmpi(metric, 'mean_myelin') || strcmpi(metric, 'std_myelin')
data_input(isnan(data_input)) = 0;
metric_parcel = zeros(num_parcels,1);
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if strcmpi(metric, 'mean_myelin')
metric_parcel(parcel_ind) = mean(data_input(ind_parcel));
elseif strcmpi(metric, 'std_myelin')
metric_parcel(parcel_ind) = std(data_input(ind_parcel));
end
end
elseif strcmpi(metric, 'mean_thickness') || strcmpi(metric, 'std_thickness')
data_input(isnan(data_input)) = 0;
metric_parcel = zeros(num_parcels,1);
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
if strcmpi(metric, 'mean_thickness')
metric_parcel(parcel_ind) = mean(data_input(ind_parcel));
elseif strcmpi(metric, 'std_thickness')
metric_parcel(parcel_ind) = std(data_input(ind_parcel));
end
end
elseif strcmpi(metric, 'mean_S')
metric_parcel = zeros(num_parcels,1);
S_combined = {};
for parcel_ind = 1:num_parcels
parcel_interest = parcels(parcel_ind);
ind_parcel = find(parc==parcel_interest);
S = data_input(ind_parcel,ind_parcel);
S(isnan(S)) = 0;
S_combined{parcel_ind} = S;
end
parfor parcel_ind = 1:num_parcels
temp = S_combined{parcel_ind};
triu_ind = find(triu(ones(size(temp)),1));
metric_parcel(parcel_ind) = mean(temp(triu_ind));
end
end