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Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis

  • Mayuresh S. Korgaonkar
    Affiliations
    The Brain Dynamics Centre, Sydney Medical School-Westmead and Westmead Millennium Institute for Medical Research, Sydney

    Discipline of Psychiatry, University of Sydney Medical School: Western, Westmead Hospital, Sydney
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  • Alex Fornito
    Affiliations
    Monash Clinical and Imaging Neuroscience, School of Psychology and Psychiatry & Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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  • Leanne M. Williams
    Affiliations
    The Brain Dynamics Centre, Sydney Medical School-Westmead and Westmead Millennium Institute for Medical Research, Sydney

    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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  • Stuart M. Grieve
    Correspondence
    Address correspondence to Stuart M. Grieve, Ph.D., University of Sydney, Sydney Medical School, The Brain Dynamics Center, 36 Larkin St, Waverton, NSW 2060, Australia
    Affiliations
    The Brain Dynamics Centre, Sydney Medical School-Westmead and Westmead Millennium Institute for Medical Research, Sydney

    Sydney Translational Imaging Laboratory, Sydney Medical School, University of Sydney, Australia

    Department of Radiology, Royal Prince Alfred Hospital, Australia

    Charles Perkins Centre, University of Sydney, Camperdown, Australia
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      Background

      Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome.

      Methods

      We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization.

      Results

      Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures.

      Conclusions

      This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.

      Key Words

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      Linked Article

      • Connectomics Reveals Faulty Wiring Patterns for Depressed Brain
        Biological PsychiatryVol. 76Issue 7
        • Preview
          Uncovering the neural basis of psychiatric and neurological disorders is the foundation for the development of diagnosis and treatment programs. While disorder-related changes in focal brain areas and specific brain connections have been scrutinized, a recently developed research framework—human brain connectomics (1)—offers the opportunity to study the brain as a complex, integrative network. In a nutshell, a brain network can be constructed on the basis of connections (edges) among brain regions (nodes) derived from a variety of imaging data.
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