Functional connectivity and resting state

Functional connectivity and resting-state data can be studied in many different way. Nilearn provides tools to construct “connectomes” that capture functional interactions between regions, or to extract regions and networks, via resting-state networks or parcellations.

• Extracting times series to build a functional connectome
  • Time-series from a brain parcellation or “MaxProb” atlas
    • Brain parcellations
    • Extracting signals on a parcellation
  • Time-series from a probabilistic atlas
    • Probabilistic atlases
    • Extracting signals from a probabilistic atlas
  • A functional connectome: a graph of interactions
  • A functional connectome: extracting coordinates of regions
• Connectome extraction: inverse covariance for direct connections
  • Sparse inverse covariance for functional connectomes
  • Sparse inverse covariance on multiple subjects
  • Comparing the different approaches on simulated data
  • Linking total and direct interactions at the group level
• Extracting functional brain networks: ICA and related
  • Multi-subject ICA: CanICA
    • Objective
    • Fitting CanICA model with nilearn
    • Visualizing results
    • Interpreting such components
  • An alternative to ICA: Dictionary learning
• Region Extraction for better brain parcellations
  • Fetching movie-watching based functional datasets
  • Brain maps using Dictionary learning
  • Visualization of Dictionary learning maps
  • Region Extraction with Dictionary learning maps
  • Visualization of Region Extraction results
  • Computing functional connectivity matrices
  • Visualization of functional connectivity matrices
  • Validating results
• Clustering to parcellate the brain in regions
  • Data loading: movie-watching data
  • Applying clustering
  • Using and visualizing the resulting parcellation
    • Visualizing the parcellation
    • Compressed representation