3. 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.

• 3.1. Extracting times series to build a functional connectome
  • 3.1.1. Time-series from a brain parcellation or “MaxProb” atlas
    • 3.1.1.1. Brain parcellations
    • 3.1.1.2. Extracting signals on a parcellation
  • 3.1.2. Time-series from a probabilistic atlas
    • 3.1.2.1. Probabilistic atlases
    • 3.1.2.2. Extracting signals from a probabilistic atlas
  • 3.1.3. A functional connectome: a graph of interactions
• 3.2. Connectome extraction: inverse covariance for direct connections
  • 3.2.1. Sparse inverse covariance for functional connectomes
  • 3.2.2. Sparse inverse covariance on multiple subjects
  • 3.2.3. Comparing the different approaches on simulated data
• 3.3. Extracting resting-state networks with ICA
  • 3.3.1. Data preparation: retrieving example data
  • 3.3.2. Applying CanICA
  • 3.3.3. Visualizing the results
• 3.4. Parcellating the brain in regions
  • 3.4.1. A resting-state dataset
  • 3.4.2. Preprocessing: loading and masking
  • 3.4.3. Applying Ward clustering
    • 3.4.3.1. Running the Ward algorithm
  • 3.4.4. Post-Processing and visualizing the parcels
    • 3.4.4.1. Unmasking
    • 3.4.4.2. Label visualization
    • 3.4.4.3. Compressed picture