neuromaps 0.0.7

A toolbox for projecting, resampling, and comparing brain maps

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).

Check all the brain maps we have here!

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

Installation requirements

Currently, neuromaps works with Python 3.8+. You can install stable versions of neuromaps from PyPI with pip install neuromaps. However, we recommend installing from the source repository to get the latest features and bug fixes.

You can install neuromaps from the source repository with pip install git+https://github.com/netneurolab/neuromaps.git or by cloning the repository and installing from the local directory:

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

Importantly, neuromaps implements and builds on tools that have been previously developed, and we redistribute data that was acquired elsewhere. If you use the neuromaps toolbox, please ensure proper attribution of the original data sources. Here’s a quick checklist:

• Cite the neuromaps paper.

• Cite the original papers that publish the data you are using. A complete list with references for each brain annotation can be found in the documentation, or in this Google Sheet. We also provide a standalone bibliography file and a helper function to generate the citations.

• Cite the transformations used

  • Volume-to-surface transformations (registration fusion): Buckner et al 2011 (original proposition) and Wu et al 2018 (first implementation of MNI152 to fsaverage transformation).

  • Surface-to-surface transformations (multimodal surface matching): Robinson et al 2014 and Robinson et al 2018.

• Cite the spatial null models used (see API documentation)

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.