2. Functional alignment: handling functional variability

Functional alignment seek to handle functional variablity by learning a transformation from one subject to another. It can be useful to better transfer knowledge of functional characteristics across subjects or to improve decoding

• 2.1. An introduction to functional alignment
  • 2.1.1. Inter subject variability in fMRI
    • 2.1.1.1. Loading and preparing the data
    • 2.1.1.2. Measure correlation between subjects images
  • 2.1.2. Functional alignment
    • 2.1.2.1. Learning the transformation and predicting new data
    • 2.1.2.2. Measuring similarity after alignment
• 2.2. Functional alignment pipeline
  • 2.2.1. Local functional alignment
  • 2.2.2. Alignment methods on a region
    • 2.2.2.1. Orthogonal alignment (Procrustes)
    • 2.2.2.2. Ridge alignment
    • 2.2.2.3. Optimal Transport alignment
  • 2.2.3. Comparing those methods on a region of interest
    • 2.2.3.1. Loading the data
    • 2.2.3.2. Extract a mask for the visual cortex from Yeo Atlas
    • 2.2.3.3. Define a masker
    • 2.2.3.4. Prepare the data
    • 2.2.3.5. Define the estimators, fit them and do a prediction