Inferring functional interaction and transition patterns via dynamic bayesian variable partition models

Human Brain Mapping

Volumn 35, Issue 7, 2014, Pages 3314-3331

  Zhang, Jing ^a   Li, Xiang ^b   Li, Cong ^a   Lian, Zhichao ^a   Huang, Xiu ^a   Zhong, Guocheng ^a   Zhu, Dajiang ^b   Li, Kaiming ^c   Jin, Changfeng ^d   Hu, Xintao ^e   Han, Junwei ^e   Guo, Lei ^e   Hu, Xiaoping ^c   Li, Lingjiang ^d   Liu, Tianming ^b  

^a YALE UNIVERSITY   (United States)

^b Department of Computer Science and Bioimaging Research Center University of Georgia ^*  (United States)

^c EMORY UNIVERSITY   (United States)

^d CENTRAL SOUTH UNIVERSITY   (China)

^e NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Bayesian graphic models; Dynamics; Functional interaction

Indexed keywords

ACCURACY; ARTICLE; BAYES THEOREM; BRAIN MAPPING; CONTROLLED STUDY; DEFAULT MODE NETWORK; DYNAMIC BAYESIAN VARIABLE PARTITION MODEL; EMOTION; FINITE ELEMENT ANALYSIS; HUMAN; LEARNING; NEUROIMAGING; POSTTRAUMATIC STRESS DISORDER; PRIORITY JOURNAL; BIOLOGICAL MODEL; BRAIN; COMPUTER SIMULATION; NERVE TRACT; NONLINEAR SYSTEM; PHYSIOLOGY;

BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NEURAL PATHWAYS; NONLINEAR DYNAMICS;

EID: 84902206379     PISSN: 10659471     EISSN: 10970193     Source Type: Journal    
DOI: 10.1002/hbm.22404     Document Type: Article

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