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 had an overall prevalence of 20.8% at 3 months in non-severe stroke patients without recent anxiety or depressive disorders.

• 231 patients with first-ever mild ischemic stroke were assessed using the Multidimensional Fatigue Inventory (MFI) at 3 months post-stroke.
• Patients were excluded if they had recent anxiety or depressive disorders, assessed using the Hospital Anxiety and Depression Scale (HAD).
• The cohort was described as a 'relatively homogeneous population' of non-severe stroke patients.

Post-stroke fatigue was more frequent in women and younger patients.

• Sex and age were identified as socio-demographic determinants of PSF in this cohort.
• This finding was noted despite the sample being drawn from a hospital-based cohort without psychiatric comorbidities.
• The association with younger age runs counter to typical age-related fatigue expectations, suggesting stroke-specific mechanisms.

Post-stroke fatigue was associated with HAD (Hospital Anxiety and Depression Scale) scores.

• HAD scores were collected alongside routine clinical evaluations at 3 months post-stroke.
• The association with HAD scores was identified despite the exclusion of patients with recent anxiety or depressive disorders.
• PSF was found to arise from 'an interplay of socio-demographic, emotional, and cerebral risk factors.'

SVR-LSM identified associations between lesions in the right corona radiata and external capsule with total MFI scores.

• Support vector regression-based multivariate lesion-symptom mapping (SVR-LSM) was used as a voxel-based lesion analysis method.
• The associations were found with total MFI scores but not with HAD scores, suggesting a fatigue-specific rather than mood-related neurological substrate.
• These regions are part of motor pathways, raising the possibility that 'neuronal overactivity, compensating for disrupted networks, may contribute to long-term fatigue.'

A network-based approach using PCA of lesioned brain regions revealed associations between mental fatigue and reduced activity subdimensions with brain components involving cerebro-cerebellar tracts.

• Principal component analysis (PCA) of lesioned gray and white matter regions was used as a complementary network-based lesion analysis method.
• These associations were identified after adjusting for relevant confounders including socio-demographic, psychological, and neurological factors.
• The cerebro-cerebellar tract involvement was linked specifically to the mental fatigue and reduced activity subdimensions of the MFI, not to total fatigue scores.

Two complementary lesion analysis approaches — voxel-based SVR-LSM and network-based PCA — were applied to assess the impact of lesion characteristics on different facets of subacute PSF.

• SVR-LSM provided a voxel-level analysis of lesion-symptom relationships across the brain.
• PCA of lesioned gray and white matter regions provided a network-level perspective on lesion effects.
• The MFI was used to capture multiple fatigue subdimensions including mental fatigue, physical fatigue, reduced activity, reduced motivation, and general fatigue.
• The authors note that 'whole-brain analyses would validate the generalizability of our results,' suggesting limitations in lesion coverage.