Insights from multidimensional analyses of post-stroke fatigue

BackgroundPost-stroke fatigue (PSF) is an overlooked and debilitating condition. As a multidimensional construct, fatigue encompasses physical, cognitive, and emotional components, complicating efforts to understand PSF pathophysiological mechanisms and identify key predictors. ObjectivesWe aimed to investigate the impact of lesion characteristics on the different facets of PSF while accounting for socio-demographic, psychological, and neurological factors. Methods231 first-ever ischemic stroke patients from a prospective hospital-based cohort were assessed using the Multidimensional Fatigue Inventory (MFI) and the Hospital Anxiety and Depression Scale (HAD) alongside routine clinical evaluations. Lesion analysis was done through two approaches: a voxel-based method using support vector regression-based multivariate lesion-symptom mapping (SVR-LSM), and a network-based method using principal component analysis (PCA) of lesioned gray and white matter regions. ResultsThe overall prevalence of PSF was 20.8%. PSF was more frequent among women and younger patients and strongly associated with HAD scores. SVR-LSM identified an association between lesions in the right corona radiata and external capsule and total MFI scores but none with HAD scores. The network-based approach showed associations between mental fatigue and reduced activity subdimensions and brain components involving cerebro-cerebellar tracts. ConclusionsOur findings suggest that PSF arises from an interplay of socio-demographic, emotional, and cerebral risk factors, accounting for its heterogeneous presentation. Regarding the associations with the lesioned regions, the involvement of motor pathways raises the possibility that neuronal overactivity, compensating for disrupted networks, may contribute to long-term fatigue. Further whole-brain analyses are warranted to confirm and extend these observations.