A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4191 LNCS - II, Issue , 2006, Pages 217-224

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

^a Ringgold Standard Institution Informatics   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; DATA REDUCTION; FEATURE EXTRACTION; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS;

ACTIVATED VOXELS; ACTIVATION CLUSTERS; ACTIVATION PATTERNS;

BIOMEDICAL ENGINEERING;

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

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

EID: 79551688170     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11866763_27     Document Type: Conference Paper

References (7)

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