Bayesian vector autoregressive model for multi‐subject effective connectivity inference using multi‐modal neuroimaging data

• Published in: Human brain mapping Vol. 38; no. 3; pp. 1311 - 1332
• Main Authors: Chiang, Sharon, Guindani, Michele, Yeh, Hsiang J., Haneef, Zulfi, Stern, John M., Vannucci, Marina
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2017; John Wiley and Sons Inc
• Subjects: Adult; Bayes Theorem; Bayesian hierarchical model; Brain - diagnostic imaging; Brain Mapping; Computer Simulation; Epilepsy, Temporal Lobe - diagnostic imaging; Female; functional magnetic resonance imaging (fMRI); Humans; Magnetic Resonance Imaging; Male; Models, Neurological; spatial prior; structural MRI; variable selection; vector autoregressive (VAR) model; functional magnetic resonance imaging (fMRI); Bayesian hierarchical model; structural MRI; variable selection; spatial prior; vector autoregressive (VAR) model
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.23456

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.