title: Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis.
abstract: BACKGROUND
  The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition. To date, it is unknown whether these dynamic brain changes can be predicted at the single-subject level prior to disease transition using MRI-based machine-learning techniques.
  
  
  METHODS
  First, deformation-based morphometry and partial-least-squares (PLS) was used to investigate patterns of volumetric changes over time in 25 ARMS individuals versus 28 healthy controls (HC) (1) irrespective of the clinical outcome and (2) according to illness transition or non-transition. Then, the baseline MRI data were employed to predict the expression of these volumetric changes at the individual level using support-vector regression (SVR).
  
  
  RESULTS
  PLS revealed a pattern of pronounced morphometric changes in ARMS versus HC that affected predominantly the right prefrontal, as well as the perisylvian, parietal and periventricular structures (p<0.011), and that was more pronounced in the converters versus the non-converters (p<0.010). The SVR analysis facilitated a reliable prediction of these longitudinal brain changes in individual out-of training cases (HC vs ARMS: r=0.83, p<0.001; HC vs converters vs non-converters: r=0.83, p<0.001) by relying on baseline patterns that involved ventricular enlargements, as well as prefrontal, perisylvian, limbic, parietal and subcortical volume reductions.
  
  
  CONCLUSIONS
  Abnormal brain changes over time may underlie an elevated vulnerability for psychosis and may be most pronounced in subsequent converters to psychosis. Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosis-associated neuroanatomical changes over time.
authors.affiliation: Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Nikolaos.Koutsouleris@med.uni-muenchen.de
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Koutsouleris
authors.forename: Christian
authors.initials: C
authors.lastname: Gaser
authors.forename: Ronald
authors.initials: R
authors.lastname: Bottlender
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Petra
authors.initials: P
authors.lastname: Decker
authors.forename: Markus
authors.initials: M
authors.lastname: Jäger
authors.forename: Gisela
authors.initials: G
authors.lastname: Schmitt
authors.forename: Maximilian
authors.initials: M
authors.lastname: Reiser
authors.forename: Hans-Jürgen
authors.initials: HJ
authors.lastname: Möller
authors.forename: Eva M
authors.initials: EM
authors.lastname: Meisenzahl
mesh_terms: D000328:Adult; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D008032:Limbic System / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008297:Male; D008960:Models, Psychological; D009445:Neuroanatomy / Q000379:methods*; D017711:Nonlinear Dynamics; D010296:Parietal Lobe / Q000473:pathology; D017397:Prefrontal Cortex / Q000473:pathology; D011446:Prospective Studies; D011618:Psychotic Disorders / Q000175:diagnosis / Q000473:pathology*; D012044:Regression Analysis; D018570:Risk Assessment; D055815:Young Adult