Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis

  • Authors:

  • Gang Chen;Daniel R. Glen;Ziad S. Saad;J. Paul Hamilton;Moriah E. Thomason;Ian H. Gotlib;Robert W. Cox

  • Affiliations:

  • Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA;Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA;Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA;Mood and Anxiety Disorders Laboratory, Department of Psychology, Stanford University, Stanford, CA 94305-2130, USA;Mood and Anxiety Disorders Laboratory, Department of Psychology, Stanford University, Stanford, CA 94305-2130, USA;Mood and Anxiety Disorders Laboratory, Department of Psychology, Stanford University, Stanford, CA 94305-2130, USA;Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2011

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Abstract

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and their interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoid some prevalent pitfalls that can occur when VAR and SEM are utilized separately.