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Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data
Open accessAbstract
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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“Variation in the connection weights of specific connections between two given ROIs has long been a focal point of fMRI research [17].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [17]
In the citing paper
“Unified SEM contains both lagged and contemporaneous directed relations among regions, making it ideal for biological systems that likely contain these effects as detailed in [18].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [17]
In the citing paper
“Support for this notion is found in fMRI data, where individual differences in brain patterns relating to indices such as performance [17], [52] and gender [13] have been found in normative samples.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [17]
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Metadata
- 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)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2006-05-22
- Volume
- 28
- Issue
- 2
- Pages
- 85-93
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- 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
- 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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