Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in m...

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Bibliographic Details
Published inFrontiers in neurology Vol. 9; p. 828
Main Authors Tahedl, Marlene, Levine, Seth M, Greenlee, Mark W, Weissert, Robert, Schwarzbach, Jens V
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 11.10.2018
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Summary:Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.
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Edited by: Maria Assunta Rocca, San Raffaele Hospital (IRCCS), Italy
Reviewed by: Hanneke E. Hulst, VU University Medical Center, Netherlands; Scott Kolbe, The University of Melbourne, Australia
This article was submitted to Applied Neuroimaging, a section of the journal Frontiers in Neurology
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2018.00828