[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120777-en":3,"doc-seo-120777-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120777,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes - Original Research","Acute ischemic stroke commonly leads to poststroke cognitive impairment, creating a barrier to neurological recovery. This study evaluates whether electroencephalography-derived brain network properties can predict post-stroke cognitive trajectories using machine learning. Consecutive stroke patients underwent EEG during the acute phase and cognitive assessment 3 months later with Montreal Cognitive Assessment (MoCA). Theta-band network characteristics and multiple graph metrics correlated with MoCA, supporting EEG-based prediction of cognitive outcomes across lesion laterality groups.","TYPE Original Research PUBLISHED 28 September 2023 DOI 10.3389/fnagi.2023.1238274  \nOPEN ACCESS  \nEDITED BY  \nJinping Xu,  \nChinese Academy of Sciences (CAS), China  \nREVIEWED BY  \nBo Tan,  \nUniversity of Electronic Science and Technology of China, China Yi Yang,  \nBeijing Tiantan Hospital Affiliated to Capital Medical University, China  \n*CORRESPONDENCE  \nJae-Sung Lim  \n [jaesunglim@amc.seoul.kr](jaesunglim@amc.seoul.kr)[ ](jaesunglim@amc.seoul.kr)Seung Wan Kang  \n [seungwkang@imedisync.com](seungwkang@imedisync.com)  \n†These authors have contributed equally to this work  \nRECEIVED 11 June 2023  \nACCEPTED 11 September 2023  \nPUBLISHED 28 September 2023  \nCITATION  \nLee M, Hong Y, An S, Park U, Shin J, Lee J, Oh MS, Lee B-C, Yu K-H, Lim J-S and  \nKang SW (2023) Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.  \nFront. Aging Neurosci. 15:1238274 .  \ndoi: 10.3389/fnagi.2023.1238274  \nCOPYRIGHT  \n© 2023 Lee, Hong, An, Park, Shin, Lee, Oh, Lee, Yu, Lim and Kang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes  \nMinwoo Lee 1†, Yuseong Hong 2†, Sungsik An3, Ukeob Park 2, Jaekang Shin 2, Jeongjae Lee 1, Mi Sun Oh 1, Byung-Chul Lee 1, Kyung-Ho Yu 1, Jae-Sung Lim4* and Seung Wan Kang 2*  \n1 Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea, 2 iMedisync, Inc., Seoul, Republic of Korea, 3 Department of Neurology, Hwahong Hospital, Suwon, Republic of Korea, 4 Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea  \nObjectives: More than half of patients with acute ischemic stroke develop poststroke 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.  \nMethods: 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.  \nResults: 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 (Rsquared) were 0.76 and 0.65 for the left and right stroke groups, respectively.  \nConclusion: 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.  \nK","cbCaiuueqZSshmNT","https://ap.wps.com/l/cbCaiuueqZSshmNT","pdf",1515395,1,10,"English","en",105,"# Introduction\n## Background and clinical challenge\n## Functional connectivity and network-based rationale\n# Objectives\n# Methods\n## Participants and study design\n## EEG preprocessing and network analysis\n## Cognitive assessment and modeling approach\n# Results\n## Prediction accuracy and key network features\n## Left vs right lesion group performance\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To determine whether electroencephalography-derived brain network properties can predict post-stroke cognitive function using a machine learning approach.\"},{\"question\":\"How were patients and cognitive outcomes assessed?\",\"answer\":\"Patients with acute ischemic stroke underwent EEG during the acute phase and completed cognitive assessment 3 months later using the Montreal Cognitive Assessment (MoCA).\"},{\"question\":\"Which EEG network features were most associated with cognitive scores?\",\"answer\":\"In the left hemispheric lesion group, theta-band network attributes correlated with MoCA, and higher global efficiency and clustering coefficient along with lower characteristic path length were associated with higher MoCA scores.\"}]","Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes - 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