A nonlinear identification method to study effective connectivity in functional MRI

Medical Image Analysis

Volumn 14, Issue 1, 2010, Pages 30-38

  Li, Xingfeng ^a,b,c,d   Marrelec, Guillaume ^a,b,c   Hess, Robert F ^d   Benali, Habib ^a,b,c  

^a INSERM   (France)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c UNIVERSITÉ PARIS SACLAY   (France)

^d MCGILL UNIVERSITY   (Canada)

Author keywords

Effective connectivity; fMRI; Granger causality test; Nonlinear autoregressive exogenous model; Nonlinear system identification

Indexed keywords

APRIORI; AUTOREGRESSIVE EXOGENOUS MODELS; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL MRI; GRANGER CAUSALITY TEST; HUMAN VISUAL CORTEX; LEAST SQUARES METHODS; LINEAR METHODS; NEURONAL POPULATIONS; NON-LINEAR SYSTEM IDENTIFICATION; NONLINEAR IDENTIFICATIONS; NONLINEAR INTERACTIONS; STRUCTURAL INFORMATION;

MAGNETIC RESONANCE IMAGING; RESONANCE; STATISTICAL TESTS;

NONLINEAR SYSTEMS;

ARTICLE; CELL POPULATION; CELL STRUCTURE; CONTROLLED STUDY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; NERVE CELL; NONLINEAR REGRESSION ANALYSIS; NONLINEAR SYSTEM; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; THEORY;

ALGORITHMS; EVOKED POTENTIALS, VISUAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NEURAL PATHWAYS; NONLINEAR DYNAMICS; VISUAL CORTEX; VISUAL PERCEPTION;

EID: 70649106878     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2009.09.005     Document Type: Article

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