A model selection method for nonlinear system identification based fMRI effective connectivity analysis

IEEE Transactions on Medical Imaging

Volumn 30, Issue 7, 2011, Pages 1365-1380

  Li, Xingfeng ^a,b   Coyle, Damien ^b   Maguire, Liam ^b   McGinnity, Thomas M ^b   Benali, Habib ^a,c  

^a INSERM   (France)

^b UNIVERSITY OF ULSTER   (United Kingdom)

^c LINeM   (France)

Author keywords

Functional magnetic resonance imaging (fMRI) effective connectivity; least angle regression; model selection; nonlinear system identification theory

Indexed keywords

AKAIKE'S INFORMATION CRITERIONS; BLOCK DESIGNS; CAUSAL MODEL; CONNECTIVITY ANALYSIS; COVARIATES; CROSS-VALIDATION METHODS; EXPERIMENT DESIGN; FUNCTIONAL MAGNETIC RESONANCE IMAGING; LEAST ANGLE REGRESSION; LEAVE-ONE-OUT; MODEL SELECTION; MODEL SELECTION METHODS; MULTIPLE-INPUT-MULTIPLE-OUTPUT SYSTEMS; NON-LINEAR MODEL; NON-LINEAR SYSTEM IDENTIFICATION; NONLINEAR IDENTIFICATIONS; REAL DATA SETS; SIMULATION COMPARISON; SINGLE-INPUT SINGLE-OUTPUT; VISUAL PATHWAYS;

ALGORITHMS; DIFFERENTIAL EQUATIONS; MAGNETIC RESONANCE IMAGING; MIMO SYSTEMS; NONLINEAR ANALYSIS; NONLINEAR EQUATIONS; NONLINEAR SYSTEMS; RESONANCE;

COMPUTER SIMULATION;

ADULT; ALGORITHM; AMBLYOPIA; ARTICLE; BRAIN; COMPUTER SIMULATION; FACTUAL DATABASE; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY; NONLINEAR SYSTEM; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY;

ADULT; ALGORITHMS; AMBLYOPIA; BRAIN; COMPUTER SIMULATION; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NONLINEAR DYNAMICS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 79959792791     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2011.2116034     Document Type: Article

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