Gray matter integrity predicts white matter network reorganization in multiple sclerosis. Issue 4 (5th November 2019)

• Record Type: Journal Article
• Authors: Radetz, Angela; Koirala, Nabin; Krämer, Julia; Johnen, Andreas; Fleischer, Vinzenz; Gonzalez‐Escamilla, Gabriel; Cerina, Manuela; Muthuraman, Muthuraman; Meuth, Sven G.; Groppa, Sergiu
• Is Part Of: Human brain mapping. Volume 41:Issue 4(2020)
• Page Start: 917
• Page End: 927
• Publication Date: 2019-11-05
• Subjects: atrophy -- graph theory -- multiple sclerosis -- network analysis -- neuropsychology -- structural connectivity -- tractography; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24849
• Languages: English
• ISSNs: 1065-9471

Abstract

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.