Identifying clinico-radiological determinants of post-stroke fatigue 3 months post-stroke in a French hospital-based cohort of non-severe stroke patients without psychiatric comorbidities.

Post-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. We aimed to investigate the impact of lesion characteristics on different facets of subacute PSF while accounting for socio-demographic, psychological, and neurological factors. We assessed 231 patients with first-ever mild ischemic stroke without recent anxiety or depressive disorders using the Multidimensional Fatigue Inventory (MFI) at 3 months and the Hospital Anxiety and Depression Scale (HAD), alongside routine clinical evaluations. Lesion analysis was performed using 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. PSF had an overall prevalence of 20.8%, was more frequent in women and younger patients, and was associated with HAD scores. SVR-LSM identified associations between lesions in the right corona radiata and external capsule with total MFI scores, but not with HAD scores. After adjusting for relevant confounders, the network-based approach revealed associations between mental fatigue and reduced activity subdimensions and brain components involving cerebro-cerebellar tracts. Our findings indicate that, in a relatively homogeneous population, PSF arises from an interplay of socio-demographic, emotional, and cerebral risk factors. The involvement of motor pathways raises the possibility that neuronal overactivity, compensating for disrupted networks, may contribute to long-term fatigue. Further studies in more diverse populations along with whole-brain analyses would validate the generalizability of our results.