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

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• 作者：Sharon Chiang ; Michele Guindani ; Hsiang J. Yeh ; Zulfi Haneef ; John M. Stern and Marina Vannucci
• 刊名：Human Brain Mapping
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：38
• 期：3
• 页码：1311-1332
• 全文大小：1880K
• ISSN：1097-0193

文摘

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.