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Hum Brain Mapp. 2007 Feb;28(2):85-93.

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

Author information

1
Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York, USA. kimjieun@nidcd.nih.gov

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.

PMID:
16718669
DOI:
10.1002/hbm.20259
[Indexed for MEDLINE]

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