Gaël Varoquaux: Nilearn 0.2: more powerful machine learning for neuroimaging

Highlights

Better documentation with narrative examples

The example can now be broken down into blocks (as here) for a better narration (thanks to sphinx-gallery).

Space net: spatial regularizations in decoding

The “SpaceNet” decoder does spatial regularizations such as TV-l1 or Graph-Net to identify predictive regions in decoding.

Dictionnary learning for resting-state parcellations

Dictionnary learning is a promising alternative to ICA to learn networks.

Plotting sets of probabilistic maps

With a simple function, you can plot outlines for multiple maps.

Separating regions out of maps

We have a set of functions to separate regions on maps or turn networks into a probabilistic parcellation.

Classification on connectomes

We now have advanced connectivity measures to do comparisons across connectomes for classification.