A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

NeuroImage

Volumn 41, Issue 3, 2008, Pages 941-969

  Makni, Salima ^a   Idier, Jérôme ^b   Vincent, Thomas ^c,d   Thirion, Bertrand ^e   Dehaene Lambertz, Ghislaine ^d,f   Ciuciu, Philippe ^c,d  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

^c DIF   (France)

^d IFR 49   (France)

^e INRIA   (France)

^f NONE   (France)

Author keywords

Bayes factor; Bayesian modelling; Detection estimation; fMRI; Gamma Gaussian mixture model; Markov Chain Monte Carlo methods; Model comparison

Indexed keywords

ARTICLE; BAYES THEOREM; BAYESIAN LEARNING; BRAIN REGION; CONCEPTUAL FRAMEWORK; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRAY MATTER; HEMODYNAMIC RESPONSE FUNCTION; HEMODYNAMICS; HUMAN; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; PARCEL BASED HEMODYNAMIC RESPONSE FUNCTION; PRIORITY JOURNAL; PROBABILITY; PROBLEM SOLVING; REGION BASED JOINT DETECTION ESTIMATION FRAMEWORK; SENSITIVITY AND SPECIFICITY; SIGNAL NOISE RATIO; STATISTICAL MODEL; WHITE NOISE;

ALGORITHMS; BAYES THEOREM; BRAIN; BRAIN MAPPING; CEREBROVASCULAR CIRCULATION; HEMODYNAMICS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL;

EID: 44649099442     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2008.02.017     Document Type: Article

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