Background: Fatigue ranks among the most common and disabling symptoms in multiple sclerosis (MS). Recent theoretical works have surmised that this trait might be related to alterations across interoceptive mechanisms. However, this hypothesis has not been empirically evaluated.
Objectives: To determine whether fatigue in MS patients is associated with specific behavioral, structural, and functional disruptions of the interoceptive domain.
Methods: Fatigue levels were established via the Modified Fatigue Impact Scale. Interoception was evaluated through a robust measure indexed by the heartbeat detection task. Structural and functional connectivity properties of key interoceptive hubs were tested by magnetic resonance imaging (MRI) and resting-state functional MRI. Machine learning analyses were employed to perform pairwise classifications.
Results: Only patients with fatigue presented with decreased interoceptive accuracy alongside decreased gray matter volume and increased functional connectivity in core interoceptive regions, the insula, and the anterior cingulate cortex. Each of these alterations was positively associated with fatigue. Finally, machine-learning analysis with a combination of the above interoceptive indices (behavioral, structural, and functional) successfully discriminated (area under the curve > 90%) fatigued patients from both non-fatigued and healthy controls.
Conclusion: This study offers unprecedented evidence suggesting that disruptions of neurocognitive markers subserving interoception may constitute a signature of fatigue in MS.
Keywords: Multiple sclerosis; fatigue; functional connectivity; heartbeat detection task; interoception; machine learning; voxel-based morphometry.