Background: Fatigue, a frequent and disabling symptom for people with multiple sclerosis (PwMS), inconsistently correlates with white matter (WM) pathology. Network-based analysis, accounting for the manner in which lesions disrupt networks of structurally connected gray matter (GM) regions, may provide additional insight.
Objective: To identify patterns of WM tract disruption which explain self-reported fatigue severity in PwMS.
Methods: 137 PwMS and 50 age- and sex-matched healthy controls (HC) underwent fatigue assessment and brain MRI. Lesion maps were applied to determine the severity of WM tract disruption between pairs of GM regions. Then, the Network-Based-Statistics tool was applied to identify structural networks whose disruption explained fatigue severity. To determine whether these networks explain unique variance above conventional MRI measures and depression, regressions were applied controlling for age, sex, brain volume, T2-lesion volume, and depression.
Results: Patient-perceived fatigue in PwMS was positively associated with overall lesion burden (β = 0.563, p-value < 0.001). In contrast, localized disruptions in WM tracts between regions including the amygdala, insula, hippocampus, putamen, temporal pole, caudal-middle-frontal gyrus, rostral-middle-frontal gyrus, inferior-parietal gyrus, and banks of the superior temporal sulcus were significantly negatively correlated with fatigue in PwMS (β = -0.586, p-value < 0.001). Average disruption within this specific, localized network explained significant additional variance in fatigue above what was otherwise explained by depression and conventional MRI measures of neuropathology (ΔR2 = 0.078, p-value < 0.001).
Conclusion: Although overall lesion burden correlates positively with fatigue in PwMS, localized WM damage between the amygdala, temporal pole, and other connected structures is associated with lower severity of patient-perceived fatigue.
Keywords: Amygdala; Fatigue; Multiple sclerosis; Structural connectivity.
Copyright © 2018. Published by Elsevier B.V.