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- the structure of raw brain imaging data;
- loading, manipulating, showing and saving brain images;
- coordinate systems and transforms for brain images;
- working with multiple time courses.
- writing and running diagnostics;
- time-course interpolation to allow for slice-wise acquisition of brain volumes;
- cost-functions and numerical optimization for image alignment;
- spatial transformations for image alignment: translations, rotations, zooms and shears;
- individual variability of sulci and other brain structure; methods of reducing variability by automated alignment and warping; remaining variation and statistical analysis;
- smoothing / blurring prior to statistical analysis; cost and benefit;
- multiple regression for modeling the effect of the experiment on time courses of brain activity;
- specifying a model of neural activity; transformation of neural activity to predicted FMRI signal using a hemodynamic response function; modeling the hemodynamic response;
- estimating multiple regression models; hypothesis testing on multiple regression models; the General Linear Model as generalization of multiple regression;
- inference on maps of statistics; correction for multiple comparisons; family-wise error; false discovery rate.
- collaborating with peers and mentors;
- role of working practice in quality, reproducibility, collaboration;
- choosing and learning simple tools;
- version control with git;
- sharing code with https://github.com;
- scripting and coding with Python;
- pair coding and code review;
- testing;
- documentation.