COR Brief
Data & Analytics

Nilearn

Nilearn is an open-source Python package focused on the visualization and analysis of human brain MRI data. It offers statistical and machine-learning tools tailored for brain mapping, connectivity estimation, and predictive modeling. The package supports analysis of both brain volumes and surfaces, integrating with Python's scientific ecosystem including scikit-learn and pandas. Nilearn provides automatic dataset fetching for several preprocessed neuroimaging datasets and supports multiple brain atlases and parcellations. It is actively maintained with regular releases and backed by an engaged community offering documentation and support resources.

Updated Jan 23, 2026open-source

Nilearn is a Python-based open-source tool for brain MRI data analysis and visualization.

Pricing
open-source
Category
Data & Analytics
Company
Interactive PresentationOpen Fullscreen ↗
01
Tools for displaying brain maps from Nifti-like images using matplotlib and plotly.
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Includes SearchLight, SpaceNet classifiers and regressors, and linear models for decoding brain data.
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Helper functions to download preprocessed neuroimaging datasets such as ABIDE, ADHD, and Haxby.
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Supports AAL templates, Yeo 2011 parcellations, Power atlas (264 ROIs), and Seitzman atlas (300 ROIs).
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Compatible with pandas and scikit-learn for data extraction and machine learning workflows.

Neuroimaging Research

Researchers analyzing brain MRI data can use Nilearn to perform statistical mapping and predictive modeling.

Educational Purposes

Graduate students in neuroscience or computational biology can leverage built-in datasets and documentation to learn neuroimaging analysis.

1
Set up Virtual Environment
Create a Python virtual environment using venv or conda to isolate dependencies.
2
Install Nilearn
Run 'pip install nilearn' to install the latest stable release.
3
Load Nilearn
Import the package in a Python session or Jupyter notebook with 'import nilearn'.
4
Access Built-in Datasets
Use Nilearn's helper functions to download and load preprocessed neuroimaging datasets for practice.
5
Consult Documentation and Community
Refer to the official user guide and API references; participate in weekly drop-in hours for support.
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Strategic Context for Nilearn

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Pricing
Model: open-source

Nilearn is free and open-source with no commercial pricing.

Assessment
Strengths
  • Free and open-source with no licensing costs or restrictions.
  • Python-native implementation enabling integration with scientific Python libraries.
  • Automatic downloading of multiple preprocessed neuroimaging datasets.
  • Active community support including weekly drop-in hours.
  • Comprehensive and instructive documentation.
Limitations
  • Primarily designed for users comfortable with Python programming; no graphical user interface.