Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks

Neuroscience Letters

Volumn 513, Issue 1, 2012, Pages 62-66

  Li, Juan ^a   Li, Rui ^c   Chen, Kewei ^a,b   Yao, Li ^a   Wu, Xia ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b BANNER ALZHEIMER S INSTITUTE   (United States)

^c INSTITUTE OF PSYCHOLOGY   (China)

Author keywords

Bayesian networks (BN); Default mode network (DMN); Effective connectivity; Instantaneous connectivity; Posterior cingulate cortex (PCC); Temporal connectivity

Indexed keywords

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; BILATERAL INFERIOR PARIETAL CORTEX; BRAIN FUNCTION; BRAIN REGION; DEFAULT MODE NETWORK; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GAUSSIAN BAYESIAN NETWORK; GAUSSIAN DYNAMIC BAYESIAN NETWORK; HUMAN; HUMAN EXPERIMENT; KERNEL METHOD; LEARNING; MALE; MEDIAL PREFRONTAL CORTEX; NERVE CELL NETWORK; NORMAL HUMAN; PARIETAL CORTEX; POSTERIOR CINGULATE; PREFRONTAL CORTEX; PRIORITY JOURNAL; SENSORIMOTOR INTEGRATION; TASK PERFORMANCE; TEMPORAL SUMMATION;

BAYES THEOREM; CEREBRAL CORTEX; DATA INTERPRETATION, STATISTICAL; FEMALE; HIPPOCAMPUS; HUMANS; LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MARKOV CHAINS; NERVE NET; NEURAL PATHWAYS; NORMAL DISTRIBUTION; YOUNG ADULT;

EID: 84862818706     PISSN: 03043940     EISSN: 18727972     Source Type: Journal    
DOI: 10.1016/j.neulet.2012.02.008     Document Type: Article

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