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Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics

  • Ronald A. Yeo (a1), Sephira G. Ryman (a1) (a2), Martijn P. van den Heuvel (a3), Marcel A. de Reus (a3), Rex E. Jung (a1) (a4), Jessica Pommy (a1), Andrew R. Mayer (a1) (a2), Stefan Ehrlich (a5) (a6) (a7), S. Charles Schulz (a8), Eric M. Morrow (a9), Dara Manoach (a10), Beng-Choon Ho (a11), Scott R. Sponheim (a8) (a12) and Vince D. Calhoun (a2) (a13)...

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. (JINS, 2016, 22, 240–249)

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Corresponding author

Correspondence and reprint requests to: Ronald A. Yeo, Department of Psychology, University of New Mexico, Albuquerque, NM. E-mail: ryeo@unm.edu

References

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