A sparse and spatially constrained generative regression model for fMRI data analysis

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 1, 2012, Pages 58-67

  Oikonomou, Vangelis P ^a   Blekas, Konstantinos ^a   Astrakas, Loukas ^b  

^a UNIVERSITY OF IOANNINA   (Greece)

^b UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Expectation maximization (EM) algorithm; functional magnetic resonance imaging (fMRI) analysis; general linear regression model (GLM); Markov random field (MRF); relevance vector machine (RVM)

Indexed keywords

EXPECTATION-MAXIMIZATION ALGORITHMS; FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI) ANALYSIS; GENERAL LINEAR REGRESSION MODEL (GLM); MARKOV RANDOM FIELD; RELEVANCE VECTOR MACHINE;

ADAPTIVE SYSTEMS; ALGORITHMS; DISTRIBUTION FUNCTIONS; IMAGE SEGMENTATION; LINEAR REGRESSION; MAXIMUM PRINCIPLE; RANDOM VARIABLES; STRUCTURAL FRAMES; TIME SERIES;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BAYES THEOREM; DATA ANALYSIS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; LINEAR REGRESSION ANALYSIS; MATHEMATICAL MODEL; PROBABILITY; SIMULATION;

BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NERVE NET; REGRESSION ANALYSIS;

EID: 84555197073     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2010.2104321     Document Type: Article

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