GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

TL;DR

GraphVar 2.0 is an enhanced, user-friendly MATLAB toolbox that enables flexible machine learning analysis of functional brain connectivity, integrating customizable models, validation, and comprehensive visualization tools for neuroimaging data.

Contribution

It introduces machine learning capabilities and extensive customization options to the existing GraphVar toolbox, facilitating advanced analysis of functional connectivity measures.

Findings

Supports customizable ML across connectivity matrices and variables

Provides parametric and nonparametric testing of model performance

Offers high-quality data export and visualization features

Abstract

Background: We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. New Method: GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of network measures and additional variables, allowing for a flexibility in neuroimaging applications. Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, network metrics and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. Comparison with existing methods: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated cueing for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. Conclusions: GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.