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.