title: Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.
abstract: In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Statistics, Rice University, Houston, Texas.
authors.forename: Sharon
authors.initials: S
authors.lastname: Chiang
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
authors.forename: Michele
authors.initials: M
authors.lastname: Guindani
authors.affiliation: Department of Neurology, University of California Los Angeles, Los Angeles, California.
authors.forename: Hsiang J
authors.initials: HJ
authors.lastname: Yeh
authors.affiliation: Department of Neurology, Baylor College of Medicine, Houston, Texas.
authors.forename: Zulfi
authors.initials: Z
authors.lastname: Haneef
authors.affiliation: Department of Neurology, University of California Los Angeles, Los Angeles, California.
authors.forename: John M
authors.initials: JM
authors.lastname: Stern
authors.affiliation: Department of Statistics, Rice University, Houston, Texas.
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003198:Computer Simulation; D004833:Epilepsy, Temporal Lobe / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological
keywords: Bayesian hierarchical model; functional magnetic resonance imaging (fMRI); spatial prior; structural MRI; variable selection; vector autoregressive (VAR) model