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

Published online by Cambridge University Press:  18 February 2016

Ronald A. Yeo ,
Sephira G. Ryman ,
Martijn P. van den Heuvel ,
Marcel A. de Reus ,
Rex E. Jung ,
Jessica Pommy ,
Andrew R. Mayer ,
Stefan Ehrlich ,
S. Charles Schulz  and
Eric M. Morrow
...Show all authors
Ronald A. Yeo*
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico
Sephira G. Ryman
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico The Mind Research Network, Albuquerque, New Mexico
Martijn P. van den Heuvel
Affiliation:
Department of Psychiatry, Brain Center Rudolph Magnus, University Medical Center Utrecht, Netherlands
Marcel A. de Reus
Affiliation:
Department of Psychiatry, Brain Center Rudolph Magnus, University Medical Center Utrecht, Netherlands
Rex E. Jung
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
Jessica Pommy
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico
Andrew R. Mayer
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico The Mind Research Network, Albuquerque, New Mexico
Stefan Ehrlich
Affiliation:
MGH/MIT/HMS Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts Department of Child and Adolescent Psychiatry, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
S. Charles Schulz
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
Eric M. Morrow
Affiliation:
Department of Molecular Biology, Cell Biology and Biochemistry, Laboratory for Molecular Medicine, Brown University, Providence, Rhode Island
Dara Manoach
Affiliation:
Psychiatric Neuroimaging and Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Charlestown, Massachusetts
Beng-Choon Ho
Affiliation:
Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa
Scott R. Sponheim
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota Minneapolis Veterans Administration Health Care System, Minneapolis, New Mexico
Vince D. Calhoun
Affiliation:
The Mind Research Network, Albuquerque, New Mexico Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
*
Correspondence and reprint requests to: Ronald A. Yeo, Department of Psychology, University of New Mexico, Albuquerque, NM. E-mail: [email protected]
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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)

Keywords

CognitiveWhite matterGraph theoryBrainCopy number variationConnectivity
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 2: Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease , February 2016 , pp. 240 - 249
Copyright
Copyright © The International Neuropsychological Society 2016 

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