Sparse network-based models for patient classification using fMRI

NeuroImage

Volumn 105, Issue , 2015, Pages 493-506

  Rosa, Maria J ^a,b   Portugal, Liana ^a,c   Hahn, Tim ^d   Fallgatter, Andreas J ^e   Garrido, Marta I ^f,g   Shawe Taylor, John ^a   Mourao Miranda, Janaina ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c FLUMINENSE FEDERAL UNIVERSITY   (Brazil)

^d GOETHE UNIVERSITY   (Germany)

^e UNIVERSITY HOSPITAL TÜBINGEN   (Germany)

^f UNIVERSITY OF QUEENSLAND   (Australia)

^g Australian Research Council Centre of Excellence for Integrative Brain Function   (Australia)

Author keywords

Classification; fMRI; Functional connectivity; Gaussian graphical models; Graphical LASSO; L1 norm SVM; Major depressive disorder; Reproducibility stability; Sparse models

Indexed keywords

ADULT; ARTICLE; BIOLOGICAL MODEL; BRAIN FUNCTION; BRAIN REGION; CLINICAL ARTICLE; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DATA BASE; ECHO PLANAR IMAGING; EMOTIONALITY; FACIAL EXPRESSION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MAJOR DEPRESSION; MALE; MENTAL TASK; NERVE CELL NETWORK; NUCLEAR MAGNETIC RESONANCE SCANNER; PATIENT CODING; PATTERN RECOGNITION; PREDICTION; REPRODUCIBILITY; SEXISM; STATISTICAL MODEL; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN MAPPING; DEPRESSIVE DISORDER, MAJOR; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; PROCEDURES;

ADULT; BRAIN; BRAIN MAPPING; DEPRESSIVE DISORDER, MAJOR; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 84914821073     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.11.021     Document Type: Article

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