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Detecting Changes in Correlation Networks with Application to Functional Connectivity of FMRI Data

Published online by Cambridge University Press:  01 January 2025

Changryong Baek Open the ORCID record for Changryong Baek [Opens in a new window] ,
Benjamin Leinwand ,
Kristen A. Lindquist ,
Seok-Oh Jeong ,
Joseph Hopfinger ,
Katheleen M. Gates  and
Vladas Pipiras
Changryong Baek*
Affiliation:
Sungkyunkwan University
Benjamin Leinwand
Affiliation:
Stevens institute of technology
Kristen A. Lindquist
Affiliation:
University of North Carolina at Chapel Hill
Seok-Oh Jeong
Affiliation:
Hankuk University of Foreign Studies
Joseph Hopfinger
Affiliation:
University of North Carolina at Chapel Hill
Katheleen M. Gates
Affiliation:
University of North Carolina at Chapel Hill
Vladas Pipiras
Affiliation:
University of North Carolina at Chapel Hill
*
Correspondence should be made to Changryong Baek, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongrogu,Seoul 03063, South Korea. Email: crbaek@skku.edu
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Abstract

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person’s psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

Keywords

high-dimensional time seriescovariancescorrelationsnetworksblock multiplier bootstrapdynamic factor modelsprincipal componentshypothesis tests
Type
Theory & Methods
Information
Psychometrika , Volume 88 , Issue 2 , June 2023 , pp. 636 - 655
Copyright
Copyright © 2023 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-023-09908-7.

The work of Baek was supported in part by the National Research Foundation of Korea (NRF-2019R1F1A1057104, NRF-2022R1F1A1066209). The work of Hopfinger and Gates and data acquisition was supported by the National Institutes of Health - National Institute of Biomedical Imaging and Bioengineering (R01 EB021299). Pipiras’s research was partially supported by the Grant DMS 1712966.

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