A bayesian mixture approach to modeling spatial activation patterns in multisite fMRI data

IEEE Transactions on Medical Imaging

Volumn 29, Issue 6, 2010, Pages 1260-1274

  Kim, Seyoung ^a   Smyth, Padhraic ^b   Stern, Hal ^c  

^a Carnegie Mellon University   (United States)

^b Irvine   (United States)

^c Ringgold Standard Institution Informatics   (United States)

Author keywords

Brain activation; Functional magnetic resonance imaging (fMRI); Hierarchical model

Indexed keywords

ACTIVATION PATTERNS; BAYESIAN FRAMEWORKS; BAYESIAN MIXTURE; BRAIN ACTIVATION; CLINICAL STUDY; DATA SETS; DIRECTLY MODEL; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GAUSSIANS; HIERARCHICAL DIRICHLET PROCESS; HIERARCHICAL MODEL; MIXTURE-OF-EXPERTS MODEL; MULTI-SITE; MULTIPLE IMAGE; NON-PARAMETRIC BAYESIAN; POSTERIOR DISTRIBUTIONS; PROBABILISTIC MODELS; RANDOM EFFECTS; SHAPE PARAMETERS;

ACTIVATION ANALYSIS; BAYESIAN NETWORKS; HIERARCHICAL SYSTEMS; IMAGE PROCESSING; MAGNETIC MATERIALS; MAGNETIC RESONANCE; TARGET TRACKING;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; EVOKED RESPONSE; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN; BRAIN MAPPING; EVOKED POTENTIALS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77953169978     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2044045     Document Type: Article

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