ArticleFrontiers in aging neuroscience2023
Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Biomarkers of stroke recovery using EEG-based resting-state functional connectivity: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Risk prediction models for post-stroke cognitive impairment: a systematic review and meta-analysis.Frontiers in neurology · 2026Pooled it
- Research progress of quantitative electroencephalography in post-ischemic stroke mental disorders.Frontiers in neurology · 2025Pooled it
- [Advances in research on neuroelectrophysiological characteristics of post-stroke cognitive impairment based on quantitative electroencephalography and acupuncture interventions].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Role of Electroencephalographic Biomarkers as Predictors of Post-Stroke Cognitive Outcomes in Patients with Cerebral Infarction: Literature Review.Neuropsychiatric disease and treatment · 2026Review
- Neuromodulation and rehabilitation of post-stroke cognitive impairment: challenges and prospects.Frontiers in psychiatry · 2026Review
- Electroencephalography in stroke Care: From acute monitoring to Post-Stroke epilepsy.Clinical neurophysiology practice · 2026Review
- Ferroptosis in Diabetic Cardiomyopathy and Atherosclerosis: Mechanisms and Clinical Prospects.International journal of molecular sciences · 2025Review
- Development and validation of a machine learning-based risk prediction model for post-stroke cognitive impairment.Scientific reports · 2025Article
- Multimodal dynamic hierarchical clustering model for post-stroke cognitive impairment prediction.Visual computing for industry, biomedicine, and art · 2025Article
- Acute Stroke Severity Assessment: The Impact of Lesion Size and Functional Connectivity.Brain sciences · 2025Article
- Identification and validation of an interpretable EEG-based machine learning model for the diagnosis of post-stroke cognitive impairment.Frontiers in aging neuroscience · 2025Article
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- Adaptive spatiotemporal encoding network for cognitive assessment using resting state EEG.NPJ digital medicine · 2024Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: More than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach. Methods: We enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups. Results: Eighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers ( Conclusion: Estimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.
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