nilearn
Statistical learning for neuroimaging in Python
Decision gist · record as of 2026-08-14
Yes. Nilearn is actively maintained, has low install friction, carries a permissive license, and addresses a specific scientific need with established community support. It is well-suited for researchers and developers doing neuroimaging analysis in Python. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Optional plotting features require matplotlib >= 3.8.0 or plotly + kaleido for advanced visualization.
- Low install friction with a pure-Python wheel distribution.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only preservation of copyright and license notices.
last release 2026-07-02 (43 days) · last repo commit 2026-08-14 · 1,424 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 287,281 downloads/mo, #8,035 on PyPI
Alternatives
Verify before relying
pip install nilearn
import nilearn
from nilearn import image
# Load and analyze brain imaging data using nilearn's statistical tools- Whether the package's GLM analysis and multivariate statistics capabilities cover your specific neuroimaging use case
- Performance characteristics when working with large-scale brain imaging datasets
- Availability and quality of community support beyond the documented drop-in hours
What it is and what it does
Nilearn is a Python library for statistical and machine-learning analysis of brain imaging data. It integrates scikit-learn's multivariate statistics with neuroimaging-specific tools, supporting general linear model (GLM) analysis on brain volumes and surfaces. The package is designed for researchers and developers working with fMRI, structural imaging, or other neuroimaging modalities who need accessible statistical and predictive modeling capabilities.
The library depends on core scientific Python packages (numpy, scipy, pandas, scikit-learn, nibabel) for numerical computation and neuroimaging file handling. It includes optional plotting support via matplotlib and plotly. The project maintains active development with regular releases, comprehensive documentation, and a community-oriented approach including weekly drop-in hours for user support.
Use it for
- Perform GLM-based statistical analysis on fMRI data to identify brain regions associated with experimental conditions
- Build predictive models for brain decoding tasks using multivariate pattern analysis with scikit-learn integration
- Analyze functional connectivity between brain regions and classify brain states from imaging data
- Visualize and explore brain volume and surface data with integrated plotting tools
- Conduct group-level statistical inference on neuroimaging datasets across multiple subjects
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Nilearn is actively maintained, has low install friction, carries a permissive license, and addresses a specific scientific need with established community support. It is well-suited for researchers and developers doing neuroimaging analysis in Python. No known security vulnerabilities.
Install
nilearn on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with a recent release 43 days ago and ongoing repository activity. Requires Python 3.10 or later.
Requires Python 3.10 or later. Optional plotting features require matplotlib >= 3.8.0 or plotly + kaleido for advanced visualization.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only preservation of copyright and license notices.
Quickstart
pip install nilearn
import nilearn
from nilearn import image
# Load and analyze brain imaging data using nilearn's statistical toolsVerify before relying
- Whether the package's GLM analysis and multivariate statistics capabilities cover your specific neuroimaging use case
- Performance characteristics when working with large-scale brain imaging datasets
- Availability and quality of community support beyond the documented drop-in hours
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesjinja2joblibnibabelnumpypackagingpandasrequestsscikit-learnscipy |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 287,281 / month, #8,035 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: nilearn-0.14.0-py3-none-any.whl
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