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Lacuna

A Python package for advanced brain lesion analysis.

Lacuna bridges the gap between individual lesion masks and normative brain data. It provides a reproducible workflow for lesion network mapping and regional damage quantification, using BIDS-style naming conventions for input and output organization.

Lacuna

This project is under active development and has not yet been fully validated. Use with caution.

Analyses

  • Functional network mapping

    Map the functional brain circuitry linked to a lesion using resting-state functional connectivity.

  • Structural network mapping

    Map the structural disconnectivity of a lesion using normative tractogram data.

  • Regional damage

    Quantify regional damage by measuring lesion overlap with standard brain parcellation atlases.

Install

pip install git+https://github.com/m-petersen/lacuna

Usage

# Create a tutorial dataset with synthetic lesion masks
lacuna tutorial my_dataset

# Fetch the HCP1065 tractogram
lacuna fetch hcp1065 --output-dir connectomes

# Run structural network mapping
lacuna run snm my_dataset output \
    --connectome-path connectomes/hcp1065.tck \
    --mask-space MNI152NLin6Asym

For the full walkthrough, see the Getting started tutorial. Note that Lacuna expects lesion masks to be in MNI space.

Documentation

  • Tutorials

    Step-by-step Jupyter notebook tutorials covering each analysis type.

  • How-to guides

    Practical guides for specific tasks beyond core analysis workflows.

  • Reference

    CLI commands, options, and auto-generated API documentation.

  • Explanation

    Background knowledge for using the package.