Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing-remitting multiple sclerosis

Authors

  • Yang Li,

    1. Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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    • Yang Li and Valerie Jewells contributed equally to this work.

  • Valerie Jewells,

    1. Division of Neuroradiology, Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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    • Yang Li and Valerie Jewells contributed equally to this work.

  • Minjeong Kim,

    1. Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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  • Yasheng Chen,

    1. Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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  • Andrew Moon,

    1. Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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  • Diane Armao,

    1. Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
    2. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, North Carolina
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  • Luigi Troiani,

    1. Department of Neurology, University of North Carolina at Chapel Hill, North Carolina
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  • Silva Markovic-Plese,

    1. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, North Carolina
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  • Weili Lin,

    1. Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
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  • Dinggang Shen

    Corresponding author
    1. Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina
    • Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 130 Mason Farm Road, Chapel Hill, NC 27599-7513, USA. E-mail: dgshen@med.unc.edu

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Abstract

Although it is inarguable that conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of multiple sclerosis (MS), cMRI does not show close correlation with clinical findings or pathologic features, and is unable to predict prognosis or stratify disease severity. To this end, diffusion tensor imaging (DTI) with tractography and neuroconnectivity analysis may assist disease assessment in MS. We, therefore, attempted this pilot study for initial assessment of early relapsing-remitting MS (RRMS). Neuroconnectivity analysis was used for evaluation of 24 early RRMS patients within 2 years of presentation, and compared to the network measures of a group of 30 age-and-gender-matched normal control subjects. To account for the situation that the connections between two adjacent regions may be disrupted by an MS lesion, a new metric, network communicability, was adopted to measure both direct and indirect connections. For each anatomical area, the brain network communicability and average path length were computed and compared to characterize the network changes in efficiencies. Statistically significant (P < 0.05) loss of communicability was revealed in our RRMS cohort, particularly in the frontal and hippocampal/parahippocampal regions as well as the motor strip and occipital lobes. Correlation with the 25-foot Walk test with communicability measures in the left superior frontal (r = −0.71) as well as the left superior temporal gyrus (r = −0.43) and left postcentral gyrus (r = −0.41) were identified. Additionally identified were increased communicability between the deep gray matter structures (left thalamus and putamen) with the major interhemispheric and intrahemispheric white matter tracts, the corpus callosum, and cingulum, respectively. These foci of increased communicability are thought to represent compensatory changes. The proposed DTI-based neuroconnectivity analysis demonstrated quantifiable, structurally relevant alterations of fiber tract connections in early RRMS and paves the way for longitudinal studies in larger patient groups. Hum Brain Mapp 34:3376–3391, 2013. © 2012 Wiley Periodicals, Inc.

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