ArticleFrontiers in aging neuroscience2022
Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- EEG biomarkers for assessment, prognosis, and monitoring of natural upper limb recovery after stroke: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Time-Varying Effective Network of Rehabilitation in Parkinson's Disease With Disease Laterality: A TMS-EEG Study.CNS neuroscience & therapeutics · 2026Article
- [Gait event-driven dynamic changes in brain-muscle networks and neuromuscular coordination mechanisms].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.CNS neuroscience & therapeutics · 2025Article
- Exploration of working memory retrieval stage for mild cognitive impairment: time-varying causality analysis of electroencephalogram based on dynamic brain networks.Journal of neuroengineering and rehabilitation · 2025Article
- Time-varying EEG networks of major depressive disorder during facial emotion tasks.Cognitive neurodynamics · 2024Article
- Dynamic Connectivity Analysis Using Adaptive Window Size.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
13 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.
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