# nilearn/nilearn

Machine learning for NeuroImaging in Python

Repository: https://github.com/nilearn/nilearn
Canonical: https://ross.abutalabs.com/products/nilearn
Homepage: http://nilearn.github.io
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, machine-learning, fmri, neuroimaging, mvpa, decoding, brain-connectivity, brain-imaging, brain-mri
Last push: 2026-08-26T15:05:57+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 67, longevity 100
- inputs: {"age_days": 5715, "days_push": 7, "days_rel": 62, "gap_med": 99.0, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1425, forks 675 (observed 2026-08-28T04:04:41.512971+00:00)

## What it is
Nilearn is a Python library providing statistical and machine-learning tools for analyzing brain imaging data such as fMRI and MRI volumes and surfaces. It builds on scikit-learn to support GLM analysis, decoding, classification, predictive modeling, and brain connectivity analysis.

## Use cases
- run GLM analysis on fMRI data in Python
- decode cognitive states from brain images with machine learning
- compute brain connectivity matrices from resting-state fMRI
- visualize and plot brain volumes and surfaces
- apply scikit-learn models to neuroimaging data
- classify MRI scans for predictive modeling

## When to choose
- you need machine learning or statistical analysis of fMRI/MRI data in Python
- you want scikit-learn-compatible tools tailored to neuroimaging
- you need GLM-based first-level analysis of BOLD data
- you want easy plotting of brain volumes and surfaces

## When to avoid
- you need general-purpose image processing unrelated to brain imaging
- you work with non-neuroimaging medical imaging like CT or ultrasound
- you need a GUI-based neuroimaging analysis tool
- you need raw DICOM conversion pipelines (use dcm2niix instead)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, image-processing, data-visualization
- domain: machine-learning, data-science, healthcare
- platform: python, cross-platform
- tags: neuroimaging, fmri, mri, brain-imaging, decoding, glm, connectivity-analysis, scikit-learn, mvpa

## Member repositories
- nilearn/nilearn (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:41.512971+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:37:31.780902+00:00, confidence not recorded.
  - readme: https://github.com/nilearn/nilearn (fetched 2026-08-28T04:04:41.512971+00:00, sha f3fd131e5d03)
  - homepage: http://nilearn.github.io (fetched 2026-08-29T11:49:26.970742+00:00, sha 60c3ee0c41bf)
  - registry_pypi: https://pypi.org/pypi/nilearn/json (fetched 2026-08-29T11:49:26.979624+00:00, sha 54438f9d500c)
- Data as of 2026-08-30T08:39:29.467469+00:00.