Graph metrics of structural brain networks in individuals with schizophrenia and healthy controls: Group differences, relationships with intelligence, and genetics

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Ronald A. Yeo - , University of New Mexico (Autor:in)
  • Sephira G. Ryman - , University of New Mexico, The Mind Research Network (Autor:in)
  • Martijn P. Van Den Heuvel - , Utrecht University (Autor:in)
  • Marcel A. De Reus - , Utrecht University (Autor:in)
  • Rex E. Jung - , University of New Mexico (Autor:in)
  • Jessica Pommy - , University of New Mexico (Autor:in)
  • Andrew R. Mayer - , University of New Mexico, The Mind Research Network (Autor:in)
  • Stefan Ehrlich - , Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Universitätsklinikum Carl Gustav Carus Dresden, Massachusetts General Hospital (Autor:in)
  • S. Charles Schulz - , University of Minnesota System (Autor:in)
  • Eric M. Morrow - , Brown University (Autor:in)
  • Dara Manoach - , Massachusetts General Hospital (Autor:in)
  • Beng Choon Ho - , University of Iowa (Autor:in)
  • Scott R. Sponheim - , University of Minnesota System, Department of Veterans Affairs (Autor:in)
  • Vince D. Calhoun - , The Mind Research Network, University of New Mexico (Autor:in)

Abstract

Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity - connections among high degree rich club nodes, feeder connections to these rich club nodes, and local connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.

Details

OriginalspracheEnglisch
Seiten (von - bis)240-249
Seitenumfang10
FachzeitschriftJournal of the International Neuropsychological Society
Jahrgang22
Ausgabenummer2
PublikationsstatusVeröffentlicht - 18 Feb. 2016
Peer-Review-StatusJa

Externe IDs

PubMed 26888620
ORCID /0000-0003-2132-4445/work/160950829

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Brain, Cognitive, Connectivity, Copy number variation, Graph theory, White matter