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.