nilearn/nilearn

Health v2 · maintenance only

Adoption not part of the score

1425 stars · 675 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

machine-learning data-science image-processing data-visualization machine-learning data-science healthcare python cross-platform neuroimaging fmri mri brain-imaging decoding glm connectivity-analysis scikit-learn mvpa

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nilearn/nilearn")