Syllabus

Imaging data

• 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.

Analysis concepts

• 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.

Collaboration, correctness and reproducibility

• 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 github;

• scripting and coding with Python;

• pair coding and code review;

• testing;

• documentation.