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Edge-Centered DTI Connectivity Analysis: Application to Schizophrenia

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Abstract

Diffusion tensor imaging (DTI) provides connectivity information that helps illuminate the processes underlying normal development as well as brain disorders such as autism and schizophrenia. Researchers have widely adopted graph representations to model DTI connectivity among brain structures; however, most measures of connectivity have been centered on nodes, rather than edges, in these graphs. We present an edge-based algorithm for assessing anatomic connectivity; this approach provides information about connections among brain structures, rather than information about structures themselves. This perspective allows us to formulate multivariate graph-based models of altered connectivity that distinguish among experimental groups. We demonstrate the utility of this approach by analyzing data from an ongoing study of schizophrenia.

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Acknowledgments

This work is supported by the National Institutes of Health (R01MH085646, P50MH103222, and R01DA027680 to LEH) and by the University of Maryland’s Center for Health Informatics and Bioimaging, and the State of Maryland MPower initiative (to EHH).

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Authors and Affiliations

  1. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USA

    Edward H. Herskovits & Rong Chen

  2. Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA

    L. Elliot Hong, Peter Kochunov & Hemalatha Sampath

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  1. Edward H. Herskovits
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  2. L. Elliot Hong
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  4. Hemalatha Sampath
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Correspondence to Edward H. Herskovits.

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Herskovits, E.H., Hong, L.E., Kochunov, P. et al. Edge-Centered DTI Connectivity Analysis: Application to Schizophrenia. Neuroinform 13, 501–509 (2015). https://doi.org/10.1007/s12021-015-9273-6

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