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

Classes and labsΒΆ

The initial plan for this course was that we would use the classes (on Monday, for 3 hours) for the main teaching, and the labs (Thursday, 90 minutes) for practical exercises, but in fact we ended up using both sessions for teaching and exercises.

For a separate list of classes, see Classes. For a separate list of labs, see Labs.

  • Introduction, Python, images
  • Basic Python exercises
  • Basic numpy exercises
  • Arrays, images and plotting
  • Git walk-through
  • 4D arrays, time series and diagnostics
  • Outlier detection and git / github workflow
  • Vectors, projection and PCA
  • Git / github workflow, the Python path
  • Correlation, regression, statistics on brain images
  • Some git, some multiple regression
  • Exploring the general linear model
  • More on github workflow
  • Project pitch, ANOVA with the GLM
  • F tests; convolution; project template
  • The HRF, modeling and statistical maps
  • Multiple comparison correction
  • Multiple comparison correction, whole brain analysis
  • Slice timing and motion correction
  • Optimization and image registration
  • Affine and cross-modality registration
  • Affine transformations
  • Cross-subject registration
  • Exploring cross-subject registration
  • Scripting using nipype

PSYCH 214 Fall 2016

Navigation

  • Syllabus
  • Preparation
  • Logistics
  • Classes and labs
  • Projects
  • Course material by topic
  • Exercises and homework
  • Example datasets
  • Bibliography

  • Website downloads
  • Dataset downloads
  • Github organization

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  • Documentation overview
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