BrainSpace
BrainSpace is a lightweight cross-platform toolbox primarily intended for macroscale gradient mapping and analysis of neuroimaging and connectome level data. The current version of BrainSpace is available in Python and MATLAB, programming languages widely used by the neuroimaging and network neuroscience communities. The toolbox also contains several maps that allow for exploratory analysis of gradient correspondence with other brain-derived features, together with tools to generate spatial null models.
For installation instructions, examples and documentation of BrainSpace see our documentation.
Display-independent surface reports
BrainSpace can export browser-readable HTML surface reports without opening a VTK render window. This is useful on remote servers, CI runners, notebooks, and containers where interactive VTK rendering is unavailable.
Watch the BrainSpace threemica bridge preview video. The preview is generated from BrainSpace’s bundled Conte69/fsLR-32k reference surfaces and maps; it does not contain participant data.
The native BrainSpace HTML writer keeps the dependency surface small:
from brainspace.plotting import write_surface_report
report = write_surface_report(
surfaces={"lh": surf_lh, "rh": surf_rh},
arrays={"Gradient 1": gradient_1},
filename="surface_report.html",
title="BrainSpace surface report",
)The optional threemica bridge preserves Zhengchen Cai’s original viewer and converts BrainSpace surfaces/maps into the BIDS-style derivative layout that threemica reads:
from brainspace.plotting import write_brainspace_threemica_report
reports = write_brainspace_threemica_report(
"brainspace_threemica_report",
maps=["thickness", "fc_gradient1"],
custom_maps={
"aligned_gradient": {
"values": aligned_gradients,
"label": "Aligned gradient",
"unit": "score",
"cmap": "diverging",
},
},
)Happy gradient analysis!
License
The BrainSpace source code is available under the BSD (3-Clause) license.
Support
If you have problems installing the software or questions about usage and documentation, or something else related to BrainSpace, you can post to the Issues section of our repository.
Paper
If you consider using BrainSpace, please cite our manuscript: Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, Xu T, Hong S, Langs G, Valk S, Misic B, Milham M, Margulies D, Smallwood J, Bernhardt B (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103.
Core development team
Reinder Vos de Wael, MICA Lab - Montreal Neurological Institute
Oualid Benkarim, MICA Lab - Montreal Neurological Institute
Boris Bernhardt, MICA Lab - Montreal Neurological Institute
3 maintainers
Metadata
3 maintainers
Release files for brainspace 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| brainspace-0.3.0.tar.gz | 60.5 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| brainspace-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 121.2 MB
Release files / brainspace-0.3.0.tar.gz
| Download URL | brainspace-0.3.0.tar.gz |
|---|---|
| Size | 60.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
75f2159bacd09bbd687382653562d62be1ef8e84e2989eb4b293d4a866f2460e
|
|
BLAKE2b-256 checksum How to use checksums |
97272c92b79337662e16e23ad7c7bba3f46f894cbdbf5c09289c61853f77d737
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Release files / brainspace-0.3.0-py3-none-any.whl
| Download URL | brainspace-0.3.0-py3-none-any.whl |
|---|---|
| Size | 60.7 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
aec3854a9ad2ed8f05ee4da04943307833135320c990688ba7f16def1ce0132c
|
|
BLAKE2b-256 checksum How to use checksums |
c222af1f49ce443949b60b4034e13c7a5365a3494badd91548dc854057ca6c7e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Security for brainspace 0.3.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
