Subcortical volumes as early predictors of fatigue in multiple sclerosis
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Subcortical volumes as early predictors of fatigue in multiple sclerosis. / 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; Bergh, Florian Then; Tackenberg, Björn; Kümpfel, Tania; Zettl, Uwe K; Knop, Matthias; Tumani, Hayrettin; Wiendl, Heinz; Gold, Ralf; Bittner, Stefan; Zipp, Frauke; Groppa, Sergiu; Muthuraman, Muthuraman; German Competence Network Multiple Sclerosis (KKNMS).
In: ANN NEUROL, Vol. 91, No. 2, 02.2022, p. 192-202.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Subcortical volumes as early predictors of fatigue in multiple sclerosis
AU - Fleischer, Vinzenz
AU - Ciolac, Dumitru
AU - Gonzalez-Escamilla, Gabriel
AU - Grothe, Matthias
AU - Strauss, Sebastian
AU - Molina Galindo, Lara S
AU - Radetz, Angela
AU - Salmen, Anke
AU - Lukas, Carsten
AU - Klotz, Luisa
AU - Meuth, Sven G
AU - Bayas, Antonios
AU - Paul, Friedemann
AU - Hartung, Hans-Peter
AU - Heesen, Christoph
AU - Stangel, Martin
AU - Wildemann, Brigitte
AU - Bergh, Florian Then
AU - Tackenberg, Björn
AU - Kümpfel, Tania
AU - Zettl, Uwe K
AU - Knop, Matthias
AU - Tumani, Hayrettin
AU - Wiendl, Heinz
AU - Gold, Ralf
AU - Bittner, Stefan
AU - Zipp, Frauke
AU - Groppa, Sergiu
AU - Muthuraman, Muthuraman
AU - German Competence Network Multiple Sclerosis (KKNMS)
N1 - This article is protected by copyright. All rights reserved.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
U2 - 10.1002/ana.26290
DO - 10.1002/ana.26290
M3 - SCORING: Journal article
C2 - 34967456
VL - 91
SP - 192
EP - 202
JO - ANN NEUROL
JF - ANN NEUROL
SN - 0364-5134
IS - 2
ER -
