Spatial (alteration) patterns observed in MRI images may often reflect function and dysfunction of underlying biological systems. This applies alike to function and structure, on the surface or in the volumetric space, and to typical as well as disordered brain-anatomical and functional patterns.
In recent years, several methods have been developed to compare spatial patterns between brain maps. In the simplest case, two brain maps are correlated with each other at the voxel- or parcel-level. The resulting correlation coefficient reflects the degree to which the two maps share a spatial pattern. We refer to this spatial correlation as "colocalization". The NiSpace toolbox aims to provide the most comprehensive, yet easy-to-use and flexible framework for colocalization estimation, significance testing, and visualization to date.
NiSpace is under development and its documentation currently is (very) incomplete. We welcome anyone who would like to give it a try! If you encounter bugs or have a question, feel free to open a GitHub issue or contact us via email!
There are of course many other related tools available, of which a few are listed below:
| Name | Target Problem | Significance Testing | Volume/Surface | Interface |
|---|---|---|---|---|
| JuSpace | Colocalization between nuclear imaging and case-control-difference maps | group permutation, null maps | volume | MATLAB-GUI |
| neuromaps | Providing various reference brain maps, as well as advanced imaging space transformation and null map estimation functions | null maps | surface, volume | Python-API |
| ENIGMA Toolbox | Relationships between effect-size maps and various reference datasets | null maps | surface | Python/MATLAB-API |
| BrainSpace | Focus on gradient mapping but includes null map generation functions | null maps | surface, volume | Python/MATLAB-API |
| Imaging Transcriptomics Toolbox | Gene-Set-Enrichment-Analysis on neuroimaging data using Allen Brain Atlas | surface (volume) | Python-API | |
| GAMBA | Gene-Set-Enrichment-Analysis on neuroimaging data using Allen Brain Atlas | gene-set permutation, null maps | volume (surface) | Web-GUI/MATLAB-API |
| GCEA | Gene-Set-Enrichment-Analysis on neuroimaging data using Allen Brain Atlas | gene-set permutation, null maps | volume (surface) | MATLAB-API |
NiSpace tries to incorporate most of the functionality of these tools in a unified framework. It took many ideas and implementations from the toolboxes listed above. Two prior tools developed by me (Leon Lotter) – JuSpyce (basis for NiSpace, Python) and ABAnnotate (easy-to-use neuroimaging gene-set enrichment based on GCEA, MATLAB) – were discontinued in favor of NiSpace.
| Name | Target Problem | Significance Testing | Volume/Surface | Interface |
|---|---|---|---|---|
| NiSpace | Colocalization between two or multiple brain maps in single-map, case-control, and set-enrichment settings. Generalizes set-enrichment approach to all kinds of reference maps. Incorporates advanced imaging space transformation through neuromaps. Includes a large range of reference datasets | null maps, group permutation, set permutation | volume, surface | Python-API (GUI planned) |
You can install the development version of NiSpace in a Python 3.9+ environment via command line using pip:
pip install git+https://github.com/LeonDLotter/NiSpace.git@devFor reproducibility, consider installing a specific commit:
pip install git+https://github.com/LeonDLotter/NiSpace.git@{commit_hash}There is no paper for NiSpace yet. Please cite at least the following when you use our tools in your work:
See the documentation's citation section for more information.
Do you have questions, comments or suggestions, or would like to contribute to the toolbox? Feel free to open an issue on GitHub or contact me! If you would like to have a new parcellation, reference dataset, or reference collection added to the toolbox, please open a GitHub issue using the "New Dataset" template.