Subcortical volumes as early predictors of fatigue in multiple sclerosis.

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 four years by applying structural equation modeling (SEM).

METHODS

This multi-center cohort study included 601 treatment-naive MS patients after the first demyelinating event. All patients underwent a standardized 3T MRI protocol. A subgroup of 230 patients with available clinical follow-up data after four 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. This article is protected by copyright. All rights reserved.