Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models.
Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models.
复制标题
通过动态贝叶斯变量分区模型推断功能交互和转换模式
DOI：
10.1002/hbm.22404
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发表时间：
2014-07
影响因子：
4.8
通讯作者：
Liu, Tianming
中科院分区：
文献类型：
--
作者：
Zhang, Jing;Li, Xiang;Li, Cong;Lian, Zhichao;Huang, Xiu;Zhong, Guocheng;Zhu, Dajiang;Li, Kaiming;Jin, Changfeng;Hu, Xintao;Han, Junwei;Guo, Lei;Hu, Xiaoping;Li, Lingjiang;Liu, Tianming
Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi‐stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by finite‐state transition machines. Results on both simulated and experimental datasets demonstrated the effectiveness and accuracy of the DBVPM in dividing temporally transiting functional interaction patterns. The application of DBVPM on a post‐traumatic stress disorder (PTSD) dataset revealed substantially different multivariate functional interaction signatures and temporal transitions in the default mode and emotion networks of PTSD patients, in comparison with those in healthy controls. This result demonstrated the utility of DBVPM in elucidating salient features that cannot be revealed by static pair‐wise functional connectivity analysis. Hum Brain Mapp 35:3314–3331, 2014. © 2013 Wiley Periodicals, Inc.
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影响因子：
5.7
作者：
Dauwels, J.;Vialatte, F.;Cichocki, A.
通讯作者：
Cichocki, A.
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V
影响因子：
5.7
作者：
Friston, KJ
通讯作者：
Friston, KJ
DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.
影响因子：
4.8
作者：
Friston, Karl J.
通讯作者：
Friston, Karl J.