title: Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification.
abstract: BACKGROUND
  Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition.
  
  
  METHODS
  First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation.
  
  
  RESULTS
  The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments.
  
  
  CONCLUSIONS
  Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.
authors.affiliation: Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany. Nikolaos.Koutsouleris@med.uni-muenchen.de
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Koutsouleris
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Ronald
authors.initials: R
authors.lastname: Bottlender
authors.forename: Katja
authors.initials: K
authors.lastname: Patschurek-Kliche
authors.forename: Johanna
authors.initials: J
authors.lastname: Scheuerecker
authors.forename: Petra
authors.initials: P
authors.lastname: Decker
authors.forename: Christian
authors.initials: C
authors.lastname: Gaser
authors.forename: Hans-Jürgen
authors.initials: HJ
authors.lastname: Möller
authors.forename: Eva M
authors.initials: EM
authors.lastname: Meisenzahl
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D003072:Cognition Disorders / Q000175:diagnosis* / Q000209:etiology; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015999:Multivariate Analysis; D009483:Neuropsychological Tests; D062706:Prodromal Symptoms; D011618:Psychotic Disorders / Q000150:complications / Q000175:diagnosis*; D012306:Risk; D012680:Sensitivity and Specificity