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Updated: Jul 22, 2025
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
Published on: September 1, 2023
Neuroimaging correlates of post-stroke fatigue: A systematic review and meta-analysis
Amy A Jolly1, Adriana Zainurin1, Gillian Mead2
1Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
Background:
Fatigue is a common and disabling symptom following stroke, but its underlying mechanisms are unknown. Associations with a number of imaging features have been proposed.
Aims:
We aimed to assess whether neuroimaging parameters could better inform our understanding of possible causes of post-stroke fatigue (PSF) through systematic review and meta-analysis.
Methods:
Using a predefined protocol registered with PROSPERO (ID: CRD42022303168), we searched EMBASE, MEDLINE, PubMed, and PsycInfo for studies assessing PSF and computerized tomography (CT), magnetic resonance (MR), positron emission tomography (PET) imaging, or diffusion tensor imaging (DTI). We extracted neuroimaging parameters and narratively analyzed study results to assess any association with PSF. Where there were 3+ similar studies, we carried out a meta-analysis using inverse-variance random-effects model to estimate the total association of each neuroimaging parameter on PSF. The risk of bias was assessed using the Newcastle and Ottawa Scale.
Results:
We identified 46 studies (N = 6543); in many studies, associations with fatigue were secondary or subanalyses (28.3%). Imaging parameters were assessed across eight variables: lesion lateralization, lesion location, lesion volume, brain atrophy, infarct number, cerebral microbleeds, white matter hyperintensities (WMHs), and network measures. Most variables showed no conclusive evidence for any association with fatigue. Meta-analysis, where possible, showed no association of the following with PSF; left lesion lateralization (OR: 0.88, 95% CI (0.64, 1. 22) (p = 0.45)), infratentorial lesion location (OR: 1.83, 95% CI (0.63, 5.32) (p = 0.27)), and WMH (OR: 1.21, 95% CI (0.84, 1.75) (p = 0.29)). Many studies assessed lesion location with mixed findings; only one used voxel-symptom lesion-mapping (VSLM). Some small studies suggested an association between altered functional brain networks, namely frontal, fronto-striato-thalamic, and sensory processing networks, with PSF.
Conclusion:
There was little evidence for the association between any neuroimaging parameters and PSF. Future studies should utilize advanced imaging techniques to fully understand the role of lesion location in PSF, while the role of altered brain networks in mediating PSF merits further research.
