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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)...


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)


Corresponding author

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


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Andreasen, N.C. (1984a). Scale for the assessment of negative symptoms. Iowa City, IA: University of Iowa.
Andreasen, N.C. (1984b). The scale for the assessment of positive symptoms. Iowa City, IA: The University of Iowa.
Andreasen, N.C., Flaum, M., & Arndt, S. (1992). The Comprehensive Assessment of Symptoms and History (CASH) - An instrument for assessing diagnosis and psychopathology. Archives of General Psychiatry, 49(8), 615623.
Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human functional networks. Proceedings of the National Academy of Sciences, 103(51), 1951819523.
Bathelt, J., O’Reilly, H., Clayden, J.D., Cross, J.H., & de Haan, M. (2013). Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources of high-density EEG recordings. Neuroimage, 82, 595604.
Blair, C. (2006). How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. The Behavioral and Brain Sciences, 29(2), 109125; discussion 125–160.
Bohlken, M.M., Mandl, R.C.W., Brouwer, R.M., van den Heuvel, M.P., Hedman, A.M., Kahn, R.S., & Hulshoff Pol, H.E. (2014). Heritability of structural brain network topology: A DTI study of 156 twins. Human Brain Mapping, 35(10), 52955305.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186198.
Caspi, A., Houts, R.M., Belsky, D.W., Goldman-Mellor, S.J., Harrington, H., Israel, S., & Moffitt, T.E. (2013). The p Factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2(2), 119137.
Chang, L.C., Jones, D.K., & Pierpaoli, C. (2005). RESTORE: Robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine, 53, 10881095.
Chen, J., Liu, J., & Calhoun, V.D. (2010). Correction of copy number data using principal component analysis. 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 827–828.
Chen, J., Liu, J., & Calhoun, V.D. (2011). A pipeline for copy number variation detection based on principal components analysis. Proceedings IEEE Bioinformatics and Biomedicine, 2011, 69756978.
Colizza, V., Flammini, A., Serrano, M.A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2(2), 110115.
De Reus, M.A., Saenger, V.M., Kahn, R.S., & van den Heuvel, M.P. (2014). An edge-centric perspective on the human connectome: Link communities in the brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1653), 20130527.
De Reus, M., & van den Heuvel, M.P. (2014). Simulated rich club lesioning in brain networks: A scaffold for communication and integration? Frontiers in Human Neuroscience, 8, 647.
Deary, I.J. (2012). Intelligence. Annual Review of Psychology, 63, 453482.
Dennis, E.L., Jahanshad, N., Toga, A.W., McMahon, K.L., de Zubicaray, G.I., Martin, N.G., & Thompson, P.M. (2013). Test-retest reliability of graph theory measures of structural brain connectivity. Medical Imaging and Computer Assisted Intervention, 15, 305312.
Dickinson, D., & Harvey, P.D. (2009). Systemic hypotheses for generalized cognitive deficits in schizophrenia: A new take on an old problem. Schizophrenia Bulletin, 35(2), 403414.
Fair, D.A., Cohen, A.L., Power, J.D., Dosenbach, N.U.F., Church, J.A., Miezin, F.M., & Petersen, S.E. (2009). Functional brain networks develop from a “local to distributed” organization. PLoS Computational Biology, 5(5), e1000381.
First, M., Spitzer, R.L., Gibbon, M., & Williams, J.B. (1997). Structured clinical interview for DSM-IV-TR axis I disorders. Washington, DC: American Psychiatric Press, Inc.
Fischer, F.U., Wolf, D., Scheurich, A., & Fellgiebel, A. (2014). Association of structural global brain network properties with intelligence in normal aging. PloS One, 9(1), e86258.
Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segoone, F., Salat, D.H., & Dale, A.M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 1122.
Gollub, R.L., Shoemaker, J.M., King, M.D., White, T., Ehrlich, S., Sponheim, S.R., & Andreasen, N.C. (2013). The MCIC collection: A shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics, 11(3), 367388.
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biol, 6(7), e159.
Haier, R.J., Colom, R., Schroeder, D.H., Condon, C.A., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37(2), 136.
Iturria-Medina, Y., Sotero, R.C., Canales-Rodriguez, E.J., Alemán-Gómez, Y., & Melie-Garcia, L. (2008). Studying the human brain anatomical network via diffusion-weighted MRI and graph theory. Neuroimage, 40(3), 10641076.
Johnson, W., Bouchard, T.J., Krueger, R.F., McGue, M., & Gottesman, I.I. (2004). Just one g: Conistent results from three test batteries. Intelligence, 32(1), 85107.
Kahn, R.S., & Keefe, R.S.E. (2013). Schizophrenia is a cognitive illness: Time for a change. JAMA Psychiatry, 70(10), 11071112.
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.
Lencz, T., Knowles, E., Davies, G., Guha, S., Liewald, D.C., Starr, J.M., & Malhotra, A.K. (2014). Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: A report from the Cognitive Genomics consorTium (COGENT). Molecular Psychiatry, 19(2), 168174.
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395.
Martin, A.K., Robinson, G., Reutens, D., & Mowry, B. (2014). Cognitive and structural neuroimaging characteristics of schizophrenia patients with large, rare copy number deletions. Psychiatry Research: Neuroimaging, 224, 311318.
McAuley, J.J., da Fontoura Costa, L., & Caetano, T.S. (2007). Rich-club phenomenon across complex network hierarchies. Applied Physics Letters, 91(8), 084103.
Messé, A., Marrelec, G., Bellec, P., Perlbarg, V., Doyon, J., Pélégrini-Issac, M., & Benali, H. (2012). Comparing structural and functional graph theory features in the human brain using multimodal MRI. Irbm, 33(4), 244253.
Mori, S., & van Zijl, P.C. (2002). Fiber tracking: Principles and strategies - A technical review. NMR Biomedicine, 15, 468480.
Nisbett, R.E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D.F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130159.
Raznahan, A., Greenstein, D., Lee, N.R., Clasen, L.S., & Giedd, J.N. (2012). Prenatal growth in humans and postnatal brain maturation into late adolescence. Proceedings of the National Academy of Sciences, 109, 1136611371.
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 10591069. doi:10.1016/j.neuroimage.2009.10.003
Ryman, S.G., Vakhtin, A.A., Yeo, R.A., van den Heuvel, M.P., de Reus, M., Flores, R.A., Caprihan, A., & Jung, R.E. (n.d.). The cost of intelligence: Graph analysis of white matter connectivity in a large normal cohort. Under review.
Ryman, S.G., van den Heuvel, M.P., Yeo, R.A., Caprihan, A., Carrasco, J., Vakhtin, A.A., Jung, R.E. (2014). Sex differences in the relationship between white matter connectivity and creativity. Neuroimage, 101, 380389.
Schaefer, J., Giangrande, E., Weinberger, D.R., & Dickinson, D. (2013). The global cognitive impairment in schizophrenia: Consistent over decades and around the world. Schizophrenia Research, 150(1), 4250.
Selig, J.P., & Preacher, K. (2008). Monte Carlo method for assessing mediation: An interactive tool for creating confidence intervals for indirect effects [Computer software]. Retrieved from
Senden, M., Deco, G., de Reus, M.A., Goebel, R., & van den Heuvel, M.P. (2014). Rich club organization supports a diverse set of functional network configurations. Neuroimage, 96, 174182.
Sponheim, S.R., Jung, R.E., Seidman, L.J., Mesholam-Gately, R.I., Manoach, D.S., O’Leary, D.S., & Schulz, S.C. (2010). Cognitive deficits in recent-onset and chronic schizophrenia. Journal of Psychiatric Research, 44(7), 421428.
Van den Heuvel, M.P., & Fornito, A. (2014). Brain networks in schizophrenia. Neuropsychology Review, 24(1), 3248.
Van den Heuvel, M.P., Kahn, R.S., Goñi, J., & Sporns, O. (2012). High-cost, high capacity backbone for global brain communication. Proceedings of the National Academy of Sciences of the United States of America, 109, 1137211377.
Van den Heuvel, M.P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 1577515786. doi:10.1523/JNEUROSCI.3539-11.2011
Van den Heuvel, M.P., Sporns, O., Collin, G., Scheewe, T., Mandl, R.C.W., Cahn, W., & Kahn, R.S. (2013). Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry, 70(8), 783792. doi:10.1001/jamapsychiatry.2013.1328
Van den Heuvel, M.P., Stam, C.J., Kahn, R.S., & Hulshoff Pol, H.E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 76197624.
Von Ehrenstein, O.S., Mikolajczyk, R.T., & Zhang, J. (2009). Timing and trajectories of fetal growth related to cognitive development in childhood. American Journal of Epidemiology, 170(11), 13881395.
Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440442.
Yeo, R.A., Gangestad, S.W., Walton, E., Ehrlich, S., Pommy, J., Turner, J.A., & Calhoun, V.D. (2014). Genetic influences on cognitive endophenotypes in schizophrenia. Schizophrenia Research, 156(1), 7175.
Yeo, R.A., & Gangestad, S.W. (2015). Developmental instability, mutation load, and neurodevelopmental disorders. In K.J. Mitchell (Ed.), Genetics of neurodevelopmental disorders (pp. 81110). Hoboken, NJ: Wiley-Blackwell.
Yeo, R.A., Gangestad, S.W., Liu, J., Ehrlich, S., Thoma, R.J., Pommy, J.M., & Calhoun, V.D. (2013). The impact of copy number deletions on general cognitive ability and ventricle size in patients with schizophrenia and healthy control subjects. Biological Psychiatry, 73(6), 540545.
Yeo, R.A., Martinez, D., Pommy, J., Ehrlich, S., Schulz, S.C., Ho, B.-C., & Calhoun, V.D. (2013). The impact of parent socio-economic status on executive functioning and cortical morphology in individuals with schizophrenia and healthy controls. Psychological Medicine, 44, 12571265.
Yu, Q., Plis, S.M., Erhardt, E.B., Allen, E.A., Sui, J., Kiehl, K.A., & Calhoun, V.D. (2011). Modular organization of functional network connectivity in healthy controls and patients with schizophrenia during the resting state. Frontiers in Systems Neuroscience, 5, 103.
Yu, Q., Plis, S.M., Erhardt, E.B., Allen, E.A., Sui, J., Kiehl, K.A., & Calhoun, V.D. (2013). Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia. Schizophrenia Research, 143(1), 165171.
Zalesky, A., Fornito, A., Seal, M.L., Cocchi, L., Westin, C.-F., Bullmore, E.T., & Pantelis, C. (2011). Disrupted axonal fiber connectivity in schizophrenia. Biological Psychiatry, 69(1), 8089.
Zhang, F., Gu, W., Hurles, M.E., & Lupski, J.R. (2009). Copy number variation in human health, disease, and evolution. Annual Review of Genomics and Human Genetics, 10, 451481.



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