Probabilistic framework for brain connectivity from functional MR images

IEEE Transactions on Medical Imaging

Volumn 27, Issue 6, 2008, Pages 825-833

  Rajapakse, Jagath C ^a,b,c   Wang, Yang ^d   Zheng, Xuebin ^d   Zhou, Juan ^d  

^a IEEE

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Conditional random fields; Dynamic Bayesian networks (DBNs); Effective connectivity; Functional magnetic resonance imaging (fMRI); Graphical models; Markov random field (MRF)

Indexed keywords

GRAPHIC METHODS; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MATHEMATICAL MODELS;

CONDITIONAL RANDOM FIELDS; DYNAMIC BAYESIAN NETWORKS (DBN); FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI); MARKOV RANDOM FIELD (MRF);

BAYESIAN NETWORKS;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; EVOKED RESPONSE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NERVE CELL NETWORK; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; EVOKED POTENTIALS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 44449087381     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2008.915672     Document Type: Article

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