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