Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7188
Authors: Radetz, Angela
Koirala, Nabin
Krämer, Julia
Johnen, Andreas
Fleischer, Vinzenz
Gonzalez-Escamilla, Gabriel
Cerina, Manuela
Muthuraman, Muthuraman
Meuth, Sven G.
Groppa, Sergiu
Title: Gray matter integrity predicts white matter network reorganization in multiple sclerosis
Online publication date: 21-Jun-2022
Language: english
Abstract: Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease leading to gray matter atrophy and brain network reconfiguration as a response to increasing tissue damage. We evaluated whether white matter network reconfiguration appears subsequently to gray matter damage, or whether the gray matter degenerates following alterations in white matter networks. MRI data from 83 patients with clinically isolated syndrome and early relapsing–remitting MS were acquired at two time points with a follow-up after 1 year. White matter network integrity was assessed based on probabilistic tractography performed on diffusion-weighted data using graph theoretical analyses. We evaluated gray matter integrity by computing cortical thickness and deep gray matter volume in 94 regions at both time points. The thickness of middle temporal cortex and the volume of deep gray matter regions including thalamus, caudate, putamen, and brain stem showed significant atrophy between baseline and follow-up. White matter network dynamics, as defined by modularity and distance measure changes over time, were predicted by deep gray matter volume of the atrophying anatomical structures. Initial white matter network properties, on the other hand, did not predict atrophy. Furthermore, gray matter integrity at baseline significantly predicted physical disability at 1-year follow-up. In a sub-analysis, deep gray matter volume was significantly related to cognitive performance at baseline. Hence, we postulate that atrophy of deep gray matter structures drives the adaptation of white matter networks. Moreover, deep gray matter volumes are highly predictive for disability progression and cognitive performance.
DDC: 610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
DOI: http://doi.org/10.25358/openscience-7188
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: Human brain mapping
41
4
Pages or article number: 917
927
Publisher: Wiley-Liss
Publisher place: New York, NY
Issue date: 2020
ISSN: 1097-0193
Publisher DOI: 10.1002/hbm.24849
Appears in collections:JGU-Publikationen

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