6. 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.

• 6.1. Extracting times series to build a functional connectome
  • 6.1.1. Time-series from a brain parcellation or “MaxProb” atlas
    • 6.1.1.1. Brain parcellations
    • 6.1.1.2. Extracting signals on a parcellation
  • 6.1.2. Time-series from a probabilistic atlas
    • 6.1.2.1. Probabilistic atlases
    • 6.1.2.2. Extracting signals from a probabilistic atlas
  • 6.1.3. A functional connectome: a graph of interactions
  • 6.1.4. A functional connectome: extracting coordinates of regions
    • 6.1.4.1. References
• 6.2. Connectome extraction: inverse covariance for direct connections
  • 6.2.1. Sparse inverse covariance for functional connectomes
  • 6.2.2. Sparse inverse covariance on multiple subjects
  • 6.2.3. Comparing the different approaches on simulated data
  • 6.2.4. Linking total and direct interactions at the group level
    • 6.2.4.1. References
• 6.3. Extracting functional brain networks: ICA and related
  • 6.3.1. Multi-subject ICA: CanICA
    • 6.3.1.1. Objective
    • 6.3.1.2. Fitting CanICA model with nilearn
    • 6.3.1.3. Visualizing results
    • 6.3.1.4. Interpreting such components
  • 6.3.2. An alternative to ICA: Dictionary learning
    • 6.3.2.1. References
• 6.4. Region Extraction for better brain parcellations
  • 6.4.1. Fetching movie-watching based functional datasets
  • 6.4.2. Brain maps using Dictionary learning
  • 6.4.3. Visualization of Dictionary learning maps
  • 6.4.4. Region Extraction with Dictionary learning maps
  • 6.4.5. Visualization of Region Extraction results
  • 6.4.6. Computing functional connectivity matrices
  • 6.4.7. Visualization of functional connectivity matrices
  • 6.4.8. Validating results
    • 6.4.8.1. References
• 6.5. Clustering to parcellate the brain in regions
  • 6.5.1. Data loading: movie-watching data
  • 6.5.2. Applying clustering
  • 6.5.3. Using and visualizing the resulting parcellation
    • 6.5.3.1. Visualizing the parcellation
    • 6.5.3.2. Compressed representation
    • 6.5.3.3. References