Gaël Varoquaux

Sun 13 December 2015

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Nilearn 0.2: more powerful machine learning for neuroimaging

After 6 months of efforts, We just released version 0.2 of nilearn, dedicated to making machine learning in neuroimaging easier and more powerful.

This release integrates the features of the july sprint, and more.

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.


Thanks

Thanks to Alexandre Abraham who lead the effort, and all the contributors.

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