Cited by 237Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

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Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

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Kim, Jieun, Zhu, Wei, Chang, Linda, Bentler, Peter M., Ernst, Thomas·2007·Human Brain Mapping

DOI 10.1002/hbm.20259PMC PMC6871502

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Abstract

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two‐stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject‐level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject‐level path coefficients are merged with subject‐level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach. Hum. Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.

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Authors: Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T.
Affiliations: State University of New York (US)
University of Hawaiʻi at Mānoa (US)
University of California, Los Angeles (US)
ORCID: Kim, Jieun
Zhu, Wei
Bentler, Peter M.
Venue: Human Brain Mapping
Publisher: Wiley
Published: 2006-05-22
Volume: 28
Issue: 2
Pages: 85-93
ISSN: 1065-9471 (ISSN-L)
1097-0193
DOI: 10.1002/hbm.20259
PMC: PMC6871502
OpenAlex: W2118791853
Cited by: 236
References: 48
Citations by year: 2026: 3 · 2025: 8 · 2024: 15 · 2023: 14 · 2022: 12 · 2021: 15 · 2020: 19 · 2019: 15 · 2018: 11 · 2017: 17 · 2016: 19 · 2015: 15 · 2014: 5 · 2013: 11 · 2012: 8
FWCI: 4.78
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Human Brain Mapping (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: U.S. Department of Energy
National Institutes of Health
National Institute on Aging (2 P30 AG08051-11, P30AG008051)
National Institute on Drug Abuse (K24DA016170, DA01070, DA-00017, K02DA016991, P01DA001070)
National Center for Research Resources (3M01 RR0101710-06S1)
Biological and Environmental Research
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: structural equation modeling, functional MRI, brain connectivity, effective connectivity, multivariate autoregressive model, path analysis, visual attention, dynamic causal modeling, multivariate time series
MeSH terms: Attention, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Oxygen, Photic Stimulation, Time Factors, Models, Statistical, Multivariate Analysis, Linear Models, Nonlinear Dynamics

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Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

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