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Blind Subgrouping of Task-based FMRI

Published online by Cambridge University Press:  01 January 2025

Zachary F. Fisher ,
Jonathan Parsons ,
Kathleen M. Gates  and
Joseph B. Hopfinger Open the ORCID record for Joseph B. Hopfinger [Opens in a new window]
Zachary F. Fisher*
Affiliation:
The Pennsylvania State University
Jonathan Parsons
Affiliation:
Duke University
Kathleen M. Gates
Affiliation:
The University of North Carolina at Chapel Hill
Joseph B. Hopfinger
Affiliation:
The University of North Carolina at Chapel Hill
*
Correspondence should be made to Zachary F. Fisher, Quantitative Developmental Systems Methodology Core, Department of Human Development and Family Studies, The Pennsylvania State University, Health and Human Development Building, University Park, PA16802, USA. Email: fisherz@psu.edu
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Abstract

Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

Keywords

multivariate time seriessubgroupingcluster analysisfMRIidiographicindividual-level
Type
Theory & Methods
Information
Psychometrika , Volume 88 , Issue 2 , June 2023 , pp. 434 - 455
Copyright
Copyright © 2023 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

We acknowledge the primary funding source for this project: National Institute of Health-National Institute of Biomedical Imaging and Bioengineering (R01-EB02290).

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