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The neuromaps toolbox is designed to help researchers make easy, statistically-rigorous comparisons between brain maps (or brain annotations). Documentation can be found here.

The accompanying paper is published in Nature Methods (postprint).

Features

  • A growing library of brain maps (“annotations”) in their original coordinate space, including microstructure, function, electrophysiology, receptors, and more

  • Robust transforms between MNI-152, fsaverage, fsLR, and CIVET spaces

  • Integrated spatial null models for statistically assessing correspondences between brain maps

https://github.com/netneurolab/neuromaps/raw/main/docs/_static/neuromaps_features.png

Installation requirements

Currently, neuromaps works with Python 3.7+ and requires a few dependencies:

  • matplotlib

  • nibabel (>=3.0)

  • nilearn (>=0.7)

  • numpy (>=1.14)

  • scikit-learn (>=0.17)

  • scipy

You can get started by installing neuromaps from the source repository with:

git clone https://github.com/netneurolab/neuromaps
cd neuromaps
pip install .

You will also need to have Connectome Workbench installed and available on your path in order to use most of the transformation / resampling functionality of neuromaps.

Citation

If you use the neuromaps toolbox, please cite our paper. Importantly, neuromaps implements and builds on tools that have been previously developed, and we redistribute data that was acquired elsewhere. Please be sure to cite the appropriate literature when using neuromaps, which we detail below.

  • If you use volume-to-surface transformations (registration fusion), please cite Buckner et al 2011 (original proposition) and Wu et al 2018 (first implementation of MNI152 to fsaverage transformation).

  • If you use surface-to-surface transformations (multimodal surface matching), please cite Robinson et al 2014 and Robinson et al 2018.

  • If you use data included in neuromaps, please cite the the original papers that publish the data. A table with references for each brain annotation can be found in our wiki, or more specifically, at this link.

  • If you use the spatial null models, there is an associated citation with each type of null model. They can be found in the docstring of the function, and also here.

License information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License cc-by-nc-sa. The full license can be found in the LICENSE file in the neuromaps distribution.

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