Stroke remains a leading cause of death and disability worldwide. While well-established risk factors play a major role, genetic predisposition is a crucial determinant of stroke susceptibility, with heritability estimates up to 39% for ischemic stro...
BACKGROUND: Stroke is increasingly understood as a network disorder with symptoms often arising from disruption of white matter connectivity. Previous connectome-based lesion-symptom mapping studies revealed that poststroke motor deficits are not onl...
Synaptic plasticity is a neuron's intrinsic ability to make new connections throughout life. The morphology and function of synapses are highly susceptible to any pathological condition. Ischemic stroke is a cerebrovascular event that affects various...
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...
PURPOSE: To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases.
Journal of neuroengineering and rehabilitation
Jun 7, 2025
BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments are not typically addressed during assessment or rehabilitation. Currently, most robotic paradigms for training of the upper limb have focused solely ...
OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive impairment (PSCI) significantly affects long-term prognosis in acute anterior circulation large-vessel occlusion stroke (LVO-AIS). This study aims to de...
According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the ra...
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...
OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and classification of brain stroke conditions, with the potential to enhance accuracy and support clinical decision-making.
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