\(\newcommand{L}[1]{\| #1 \|}\newcommand{VL}[1]{\L{ \vec{#1} }}\newcommand{R}[1]{\operatorname{Re}\,(#1)}\newcommand{I}[1]{\operatorname{Im}\, (#1)}\)

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 https://github.com;
  • scripting and coding with Python;
  • pair coding and code review;
  • testing;
  • documentation.