nilearn/nilearn

Machine learning for NeuroImaging in Python

What it solves

Nilearn provides a set of tools for the statistical analysis of brain volumes and surfaces, making these complex neuroimaging data analyses approachable and versatile.

How it works

It implements General Linear Model (GLM) based analysis and integrates with the scikit-learn Python toolbox to provide multivariate statistics. This allows researchers to perform predictive modeling, classification, decoding, and connectivity analysis on brain data.

Who it’s for

Researchers and data scientists working with neuroimaging data who need tools for statistical modeling and machine learning applied to brain volumes and surfaces.

Highlights

  • Statistical and machine-learning tools for brain data analysis.
  • Support for General Linear Model (GLM) based analysis.
  • Integration with scikit-learn for multivariate statistics.
  • Optional plotting dependencies for visualizing results using matplotlib and plotly.

Related

  • Project
    tslearn-team/tslearn

    tslearn is a Python library that brings scikit‑learn‑style machine‑learning tools to time‑series data, offering preprocessing, classification, clustering, regression, and similarity metrics, all compatible with standard scikit‑learn pipelines.

  • Project
    Quantco/glum

    A high-performance Python library for Generalized Linear Models (GLM) that supports a wide range of distributions and regularization techniques with a scikit-learn-like API.

  • Project
    brainiak/brainiak

    A Python-based analysis kit for neuroscience research, focusing on the processing and analysis of functional Magnetic Resonance Imaging (fMRI) data.

  • Project
    rllm-team/rllm

    A PyTorch library for Relational Table Learning (RTL) that standardizes and combines GNNs, LLMs, and TNNs to analyze data across linked tables.

  • Project
    gallantlab/pycortex

    A software library for visualizing fMRI and other volumetric neuroimaging data on cortical surfaces.