Nilearn

Nilearn makes it easy to use many advanced machine learning, pattern recognition and multivariate statistical techniques on neuroimaging data for applications such as MVPA (Mutli-Voxel Pattern Analysis), decoding, predictive modelling, functional connectivity, brain parcellations, connectomes.

Nilearn can readily be used on task fMRI, resting-state, or VBM data.

For a machine-learning expert, the value of nilearn can be seen as domain-specific feature engineering construction, that is, shaping neuroimaging data into a feature matrix well suited to statistical learning, or vice versa.

Launch online
Source Code
Contributors
SB MR KC KD JMH JD GV ED DB KJG BC AG

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Collaboratory

The EBRAINS Collaboratory offers researchers and developers a secure environment to work with others. You control the level of collaboration by sharing your projects with specific users, teams or all of the Internet. Many researchers are sharing their work already; several services, tools, datasets and other resources are publicly available, and many more are available for registered users.

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