TY - JOUR
T1 - Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data
AU - Baek, Changryong
AU - Leinwand, Benjamin
AU - Lindquist, Kristen A.
AU - Jeong, Seok Oh
AU - Hopfinger, Joseph
AU - Gates, Katheleen M.
AU - Pipiras, Vladas
N1 - Publisher Copyright:
© 2023, The Author(s) under exclusive licence to The Psychometric Society.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - block multiplier bootstrap
KW - correlations
KW - covariances
KW - dynamic factor models
KW - high-dimensional time series
KW - hypothesis tests
KW - networks
KW - principal components
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U2 - 10.1007/s11336-023-09908-7
DO - 10.1007/s11336-023-09908-7
M3 - Article
C2 - 36892727
AN - SCOPUS:85149457091
SN - 0033-3123
VL - 88
SP - 636
EP - 655
JO - Psychometrika
JF - Psychometrika
IS - 2
ER -