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Brain Predictability toolbox: a Python library for neuroimaging-based machine learning.

Sage Hahn¹, De Kang Yuan¹, Wesley K Thompson²

¹Department of Psychiatry and Complex Systems, University of Vermont, Burlington, VT 05401, USA.

Bioinformatics (Oxford, England)

|November 20, 2020

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The Brain Predictability toolbox (BPt) offers unified machine learning tools for brain data analysis. This open-source Python package handles diverse datasets for neuroimaging research.

Area of Science:

Neuroscience
Computer Science
Data Science

Background:

Machine learning (ML) is increasingly used in neuroscience research.
Analyzing diverse neuroimaging and tabular data presents challenges.
A unified framework is needed to streamline ML applications in brain science.

Purpose of the Study:

To introduce the Brain Predictability toolbox (BPt), a novel ML framework.
To provide a unified solution for analyzing both neuroimaging and tabulated brain data.
To facilitate ML-based investigations on large human datasets.

Main Methods:

BPt is an open-source Python package (3.6+).
It integrates tools for tabulated data (e.g., behavioral, physiological) and neuroimaging data (e.g., brain volumes, surfaces).

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Documentation and source code are available on GitHub, with a Dockerized GUI under development.

Main Results:

BPt offers a versatile platform for various neuroimaging ML questions.
The toolbox supports the analysis of complex, multi-modal datasets.
It is designed for scalability, particularly for large human cohort studies.

Conclusions:

BPt provides a unified and accessible framework for ML in neuroimaging.
The toolbox enhances the ability to address complex research questions using brain data.
Active development and available resources support its adoption and further research.