Inferring consistent functional interaction patterns from natural stimulus FMRI data

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• 作者：Jiehuan Sun^a ; ^e ; ¹ ; Xintao Hu^b ; ¹ ; Xiu Huang^a ; Yang Liu^a ; ^c ; Kaiming Li^b ; ^d ; Xiang Li^d ; Junwei Han^b ; Lei Guo^b ; Tianming Liu^d ; ^{tliu@cs.uga.edu} ; Jing Zhang^a ; ^{jing.zhang.jz349@yale.edu}
• 关键词：Natural stimulus fMRI ; DTI ; Functional interaction
• 刊名：NeuroImage
• 出版年：2012
• 期刊代码：83_10538119
• 类别：neu
• 出版时间：16 July, 2012
• 卷：61
• 期：4
• 页码：987-999
• 文件大小：2555 K

摘要

There has been increasing interest in how the human brain responds to natural stimulus such as video watching in the neuroimaging field. Along this direction, this paper presents our effort in inferring consistent and reproducible functional interaction patterns under natural stimulus of video watching among known functional brain regions identified by task-based fMRI. Then, we applied and compared four statistical approaches, including Bayesian network modeling with searching algorithms: greedy equivalence search (GES), Peter and Clark (PC) analysis, independent multiple greedy equivalence search (IMaGES), and the commonly used Granger causality analysis (GCA), to infer consistent and reproducible functional interaction patterns among these brain regions. It is interesting that a number of reliable and consistent functional interaction patterns were identified by the GES, PC and IMaGES algorithms in different participating subjects when they watched multiple video shots of the same semantic category. These interaction patterns are meaningful given current neuroscience knowledge and are reasonably reproducible across different brains and video shots. In particular, these consistent functional interaction patterns are supported by structural connections derived from diffusion tensor imaging (DTI) data, suggesting the structural underpinnings of consistent functional interactions. Our work demonstrates that specific consistent patterns of functional interactions among relevant brain regions might reflect the brain's fundamental mechanisms of online processing and comprehension of video messages.