A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data

Frontiers in Computational Neuroscience

Volumn 8, Issue OCT, 2014, Pages

  Xu, Lele ^a   Fan, Tingting ^a   Wu, Xia ^a,b   Chen, KeWei ^c   Guo, Xiaojuan ^a   Zhang, Jiacai ^a   Yao, Li ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b SHANGHAI INSTITUTE OF TECHNICAL PHYSICS   (China)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Causal structure; Effective connectivity; Functional magnetic resonance imaging (fMRI); Group analysis; Linear non Gaussian acyclic model (LiNGAM); Pooling LiNGAM (pLiNGAM)

Indexed keywords

EFFECTIVE CONNECTIVITIES; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GROUP ANALYSIS; NON-GAUSSIAN; POOLING-LINGAM (PLINGAM);

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; DEFAULT MODE NETWORK; FEASIBILITY STUDY; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; IMAGE PROCESSING; INFORMATION PROCESSING; LINEAR NON GAUSSIAN ACYCLIC MODEL; MALE; NEUROIMAGING; NORMAL HUMAN; PROCESS MODEL; STATISTICAL DISTRIBUTION; YOUNG ADULT;

EID: 84907738774     PISSN: 16625188     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2014.00125     Document Type: Article

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