# Syllabus

## Contents

# 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.