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

Journal of Neuroscience Methods
L WallerJ D Kruschwitz

Abstract

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. GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. 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, graph measures 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. 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 e...Continue Reading

Citations

Mar 13, 2020·Human Brain Mapping·Zhen ZhouDinggang Shen
Jun 30, 2019·Frontiers in Neuroscience·Farzad V FarahaniNichole R Lighthall
Oct 9, 2020·Brain Connectivity·Gowtham Krishnan MurugesanJoseph A Maldjian
Dec 18, 2020·Frontiers in Neuroinformatics·Ali Noroozi, Mansoor Rezghi
Jun 18, 2021·Computational Intelligence and Neuroscience·Simon WeinElmar W Lang
Jul 13, 2021·Human Brain Mapping·Avraam D MarimpisRainer Goebel

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