
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

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.
Contents
- Installation and setup
- User guide
- Examples
- Contributing a new brain map
- Citing neuromaps
- Reference API
neuromaps.datasets
- Dataset fetchersneuromaps.images
- Image and surface handlingneuromaps.nulls
- Null modelsneuromaps.parcellate
- Parcellation utilitiesneuromaps.plotting
- Plotting functionsneuromaps.points
- Triangle mesh utilitiesneuromaps.resampling
- Resampling workflowsneuromaps.stats
- Statistical functionsneuromaps.transforms
- Transformations between spaces