Autoren:	Fleischer, Vinzenz Ciolac, Dumitru Gonzalez-Escamilla, Gabriel Grothe, Matthias Strauss, Sebastian Molina Galindo, Lara S. Radetz, Angela Salmen, Anke Lukas, Carsten Klotz, Luisa Meuth, Sven G. Bayas, Antonios Paul, Friedemann Hartung, Hans-Peter Heesen, Christoph Stangel, Martin Wildemann, Brigitte Then Bergh, Florian Tackenberg, Björn Kümpfel, Tania Zettl, Uwe K. Knop, Matthias Tumani, Hayrettin Wiendl, Heinz Gold, Ralf Bittner, Stefan Zipp, Frauke Groppa, Sergiu Muthuraman, Muthuraman
Titel:	Subcortical volumes as early predictors of fatigue in multiple sclerosis
Online-Publikationsdatum:	5-Sep-2022
Erscheinungsdatum:	2022
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	Objective Fatigue is a frequent and severe symptom in multiple sclerosis (MS), but its pathophysiological origin remains incompletely understood. We aimed to examine the predictive value of subcortical gray matter volumes for fatigue severity at disease onset and after 4 years by applying structural equation modeling (SEM). Methods This multicenter cohort study included 601 treatment-naive patients with MS after the first demyelinating event. All patients underwent a standardized 3T magnetic resonance imaging (MRI) protocol. A subgroup of 230 patients with available clinical follow-up data after 4 years was also analyzed. Associations of subcortical volumes (included into SEM) with MS-related fatigue were studied regarding their predictive value. In addition, subcortical regions that have a central role in the brain network (hubs) were determined through structural covariance network (SCN) analysis. Results Predictive causal modeling identified volumes of the caudate (s [standardized path coefficient] = 0.763, p = 0.003 [left]; s = 0.755, p = 0.006 [right]), putamen (s = 0.614, p = 0.002 [left]; s = 0.606, p = 0.003 [right]) and pallidum (s = 0.606, p = 0.012 [left]; s = 0.606, p = 0.012 [right]) as prognostic factors for fatigue severity in the cross-sectional cohort. Moreover, the volume of the pons was additionally predictive for fatigue severity in the longitudinal cohort (s = 0.605, p = 0.013). In the SCN analysis, network hubs in patients with fatigue worsening were detected in the putamen (p = 0.008 [left]; p = 0.007 [right]) and pons (p = 0.0001). Interpretation We unveiled predictive associations of specific subcortical gray matter volumes with fatigue in an early and initially untreated MS cohort. The colocalization of these subcortical structures with network hubs suggests an early role of these brain regions in terms of fatigue evolution. ANN NEUROL 2022;91:192–202
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7671
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY-NC-ND
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by-nc-nd/4.0/
Zeitschrift:	Annals of neurology 91 2
Seitenzahl oder Artikelnummer:	192 202
Verlag:	Wiley-Blackwell
Verlagsort:	Hoboken, NJ
Erscheinungsdatum:	2022
ISSN:	1531-8249
DOI der Originalveröffentlichung:	10.1002/ana.26290
Enthalten in den Sammlungen:	JGU-Publikationen