Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Cota Navin Gupta - , The Mind Research Network (Autor:in)
  • Vince D. Calhoun - , The Mind Research Network, University of New Mexico, Yale University (Autor:in)
  • Srinivas Rachakonda - , The Mind Research Network (Autor:in)
  • Jiayu Chen - , The Mind Research Network (Autor:in)
  • Veena Patel - , The Mind Research Network (Autor:in)
  • Jingyu Liu - , The Mind Research Network, University of New Mexico (Autor:in)
  • Judith Segall - , The Mind Research Network (Autor:in)
  • Barbara Franke - , Radboud University Nijmegen (Autor:in)
  • Marcel P. Zwiers - , Radboud University Nijmegen (Autor:in)
  • Alejandro Arias-Vasquez - , Radboud University Nijmegen (Autor:in)
  • Jan Buitelaar - , Radboud University Nijmegen (Autor:in)
  • Simon E. Fisher - , Radboud University Nijmegen, Max Planck Institute for Psycholinguistics (Autor:in)
  • Guillen Fernandez - , Radboud University Nijmegen (Autor:in)
  • Theo G.M. Van Erp - , University of California at Irvine (Autor:in)
  • Steven Potkin - , University of California at Irvine (Autor:in)
  • Judith Ford - , University of California at San Francisco (Autor:in)
  • Daniel Mathalon - , University of California at San Francisco (Autor:in)
  • Sarah McEwen - , University of California at Los Angeles (Autor:in)
  • Hyo Jong Lee - , Jeonbuk National University (Autor:in)
  • Bryon A. Mueller - , University of Minnesota System (Autor:in)
  • Douglas N. Greve - , Massachusetts General Hospital (Autor:in)
  • Ole Andreassen - , University of Oslo (Autor:in)
  • Ingrid Agartz - , University of Oslo, Karolinska Institutet, Diakonhjemmet Hospital (Autor:in)
  • Randy L. Gollub - , Massachusetts General Hospital, Harvard University (Autor:in)
  • Scott R. Sponheim - , University of Minnesota System, Minneapolis Veterans Administration Health Care System (Autor:in)
  • Stefan Ehrlich - , Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Massachusetts General Hospital, Universitätsklinikum Carl Gustav Carus Dresden (Autor:in)
  • Lei Wang - , Northwestern University (Autor:in)
  • Godfrey Pearlson - , Yale University, Institute of Living (Autor:in)
  • David C. Glahn - , Yale University, Institute of Living (Autor:in)
  • Emma Sprooten - , Yale University, Institute of Living (Autor:in)
  • Andrew R. Mayer - , The Mind Research Network (Autor:in)
  • Julia Stephen - , The Mind Research Network (Autor:in)
  • Rex E. Jung - , University of New Mexico (Autor:in)
  • Jose Canive - , University of New Mexico, Department of Veterans Affairs (Autor:in)
  • Juan Bustillo - , University of New Mexico (Autor:in)
  • Jessica A. Turner - , The Mind Research Network, Georgia State University (Autor:in)

Abstract

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both sourcebased morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Details

OriginalspracheEnglisch
Seiten (von - bis)1133-1142
Seitenumfang10
FachzeitschriftSchizophrenia bulletin
Jahrgang41
Ausgabenummer5
PublikationsstatusVeröffentlicht - Sept. 2015
Peer-Review-StatusJa

Externe IDs

PubMed 25548384
ORCID /0000-0003-2132-4445/work/160950844

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Independent component analysis, Schizophrenia, Source-based morphometry, Symptoms, Voxel-based morphometry