Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition

Schizophrenia Bulletin

Volumn 39, Issue 5, 2013, Pages 1105-1114

  Borgwardt, Stefan ^a,b   Koutsouleris, Nikolaos ^c   Aston, Jacqueline ^a   Studerus, Erich ^a   Smieskova, Renata ^a,b   Riecher Rössler, Anita ^a   Meisenzahl, Eva M ^c  

^a UNIVERSITY OF BASEL   (Switzerland)

^b UNIVERSITY HOSPITAL BASEL   (Switzerland)

^c LUDWIG MAXIMILIANS UNIVERSITY   (Germany)

Author keywords

at risk mental state; early prediction of psychosis; machine learning; multivariate analysis; support vector machine; voxel based morphometry

Indexed keywords

ADOLESCENT; ARTICLE; CEREBELLUM; CHILD; CINGULATE GYRUS; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISCRIMINANT ANALYSIS; FEMALE; FRONTAL LOBE; HIGH RISK PATIENT; HUMAN; MALE; NEUROANATOMY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PRODROMAL SYMPTOM; PSYCHOSIS; SCHOOL CHILD; SUBCORTEX; SUPPORT VECTOR MACHINE; TEMPORAL LOBE;

AT-RISK MENTAL STATE; EARLY PREDICTION OF PSYCHOSIS; MACHINE LEARNING; MULTIVARIATE ANALYSIS; SUPPORT VECTOR MACHINE; VOXEL-BASED MORPHOMETRY;

ADULT; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; CEREBRUM; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; PRODROMAL SYMPTOMS; PSYCHOTIC DISORDERS; RETROSPECTIVE STUDIES; RISK; TIME FACTORS; YOUNG ADULT;

EID: 84878825019     PISSN: 05867614     EISSN: 17451701     Source Type: Journal    
DOI: 10.1093/schbul/sbs095     Document Type: Article

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