title: Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis.
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.
authors.affiliation: Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA. gangchen@mail.nih.gov
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Glen
authors.forename: Ziad S
authors.initials: ZS
authors.lastname: Saad
authors.forename: J
authors.initials: J
authors.lastname: Paul Hamilton
authors.forename: Moriah E
authors.initials: ME
authors.lastname: Thomason
authors.forename: Ian H
authors.initials: IH
authors.lastname: Gotlib
authors.forename: Robert W
authors.initials: RW
authors.lastname: Cox
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted