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

Changryong Baek (), Benjamin Leinwand, Kristen A. Lindquist, Seok-Oh Jeong, Joseph Hopfinger, Katheleen M. Gates and Vladas Pipiras
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Changryong Baek: Sungkyunkwan University
Benjamin Leinwand: Stevens institute of technology
Kristen A. Lindquist: University of North Carolina at Chapel Hill
Seok-Oh Jeong: Hankuk University of Foreign Studies
Joseph Hopfinger: University of North Carolina at Chapel Hill
Katheleen M. Gates: University of North Carolina at Chapel Hill
Vladas Pipiras: University of North Carolina at Chapel Hill

Psychometrika, 2023, vol. 88, issue 2, No 11, 636-655

Abstract: 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 series; covariances; correlations; networks; block multiplier bootstrap; dynamic factor models; principal components; hypothesis tests (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-023-09908-7

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