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Classes and labs

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