neuromaps toolbox is designed to help researchers make easy,
statistically-rigorous comparisons between brain maps (or brain annotations).
Documentation can be found here.
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
neuromaps works with Python 3.7+ and requires a few
You can get started by installing
neuromaps from the source repository
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
If you use the
neuromaps toolbox, please cite our paper.
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 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.
This work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
The full license can be found in the
LICENSE file in the
- Installation and setup
- User guide
- Contributing a new brain map
- Citing neuromaps
- Reference API
neuromaps.datasets- Dataset fetchers
neuromaps.images- Image and surface handling
neuromaps.nulls- Null models
neuromaps.parcellate- Parcellation utilities
neuromaps.plotting- Plotting functions
neuromaps.points- Triangle mesh utilities
neuromaps.resampling- Resampling workflows
neuromaps.stats- Statistical functions
neuromaps.transforms- Transformations between spaces