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

TL;DR

The Brain Predictability toolbox (BPt) is an open-source Python library that provides a unified framework for applying machine learning to neuroimaging and related data, facilitating research on large human datasets.

Contribution

It introduces a comprehensive, open-source Python toolkit for machine learning analysis of neuroimaging and associated data, supporting diverse research questions.

Findings

Supports analysis of large neuroimaging datasets

Integrates tabulated and neuroimaging data types

Facilitates machine learning research in neuroimaging

Abstract

Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric, behavioral, and physiological variables) and neuroimaging specific derived data (e.g., brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging based ML questions, in particular, those queried from large human datasets. Availability and Implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app. Contact Please contact Sage Hahn at sahahn@uvm.edu