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Syllabus
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Imaging data
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* 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.

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Analysis concepts
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* 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.

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Collaboration, correctness and reproducibility
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* 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.