title: Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis.
abstract: BACKGROUND
  Schizophrenia is a multifaceted mental disorder characterized by cognitive, perceptual, and affective symptom dimensions. This heterogeneity at the phenomenological level may be subserved by complex and heterogeneous patterns of structural abnormalities. Thus, delineating such patterns may improve the insight into the variability of disease and facilitate future magnetic resonance imaging-based diagnosis.
  
  
  METHODS
  We aimed to identify structurally complex signatures that directly differentiate patients with predominantly negative (pNEG), positive (pPOS), and disorganized (pDIS) symptoms using Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a new analytical framework for group analysis, which showed to have superior sensitivity and specificity over conventional voxel-based morphometric approaches, thus facilitating the identification of subtle neuroanatomical signatures delineating different subgroups.
  
  
  RESULTS
  pPOS were characterized by pronounced gray matter (GM) volume reductions in the ventromedial prefrontal cortex (vmPFC), which herein is defined to include the orbitofrontal cortex, and in occipitotemporal GM and parts of the lingual gyrus. pNEG was found to have vmPFC reduction but to a lesser degree than pPOS and with a relative sparing of the more medial vmPFC regions, compared to pDIS; it also had significantly less cerebellar GM. pDIS showed relatively highest GM volume preservation among three subtypes.
  
  
  CONCLUSIONS
  Although a common prefronto-perisylvian GM reduction pattern was present at the whole-group level, marked morphometric differences emerged between the three subgroups, including reduced cerebellar GM in pNEG and reduced vmPFC and occipitotemporal GM in pPOS. Besides deepening our insight into the neurobiological underpinnings of clinical heterogeneity, these results also identify important imaging biomarkers that may aid patient stratification.
authors.affiliation: Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA; These authors contributed equally to the article; tianhao.zhang@uphs.upenn.edu.
authors.forename: Tianhao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany; These authors contributed equally to the article;
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Koutsouleris
authors.affiliation: Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany; These authors shared the senior coauthorship.
authors.forename: Eva
authors.initials: E
authors.lastname: Meisenzahl
authors.affiliation: Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA; These authors shared the senior coauthorship.
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000328:Adult; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D002531:Cerebellum / Q000473:pathology*; D005260:Female; D066128:Gray Matter / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009929:Organ Size; D017397:Prefrontal Cortex / Q000473:pathology*; D012559:Schizophrenia / Q000473:pathology*; D012565:Schizophrenic Psychology; D055815:Young Adult
keywords: ODVBA; patient stratification; volume reduction; voxel-based morphometry