[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119405-en":3,"doc-seo-119405-105":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":20,"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},119405,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Classification of Neurodegenerative Diseases Using Brain Effective Connectivity and Machine Learning Techniques - A Systematic Review","Effective connectivity (EC) captures directional influences and causal relationships across brain regions, while machine learning supports complex pattern learning from high-dimensional data. This systematic review examines studies that use EC derived from fMRI, EEG, and MEG together with machine learning to classify neurodegenerative diseases and distinguish patients from healthy controls. Searches were conducted in PubMed and Embase up to June 13, 2024 following PRISMA. Sixteen studies were included, covering Alzheimer’s disease, mild cognitive impairment, and Parkinson’s disease, reporting feature extraction, classifier use, validation methods, and accuracy. Results indicate promising potential and benefits from EC combined with multimodal connectivity features.","TYPE Systematic Review PUBLISHED 22 May 2025  \nDOI 10.3389/fneur.2025.1581105  \nOPEN ACCESS  \nEDITED BY  \nQi Zhang,  \nYale University, United States  \nREVIEWED BY  \nKiwamu Kudo,  \nRicoh Company, Ltd., Japan Debanjali Bhattacharya,  \nAmrita School of Artificial Intelligence, India  \n*CORRESPONDENCE  \nFang-Fang Huang  \n [fangfang.huang@haust.edu.cn](fangfang.huang@haust.edu.cn)  \nRECEIVED 21 February 2025  \nACCEPTED 05 May 2025  \nPUBLISHED 22 May 2025  \nCITATION  \nWang Y-F, Huang Y, Chang X-Y, Guo S-Y, Chen Y-Q, Wang M-Z, Liu K-L and Huang F-F (2025) Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.  \nFront. Neurol. 16:1581105 .  \ndoi: 10.3389/fneur.2025.1581105  \nCOPYRIGHT  \n© 2025 Wang, Huang, Chang, Guo, Chen, Wang, Liu and Huang. This is an open-access article distributed under the terms of the  \nCreative 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.  \nClassification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review  \nYing-Fang Wang, Yuan Huang, Xiao-Yi Chang, Si-Ying Guo, Yu-Qi Chen, Ming-Zhu Wang, Kai-Le Liu and  \nFang-Fang Huang *  \nDepartment of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, China  \nBackground: Effective connectivity (EC) refers to the directional influences or causal relationships between brain regions. In the field of artificial intelligence, machine learning has demonstrated remarkable proficiency in image recognition and the complex dataset analysis. In recent years, machine learning models leveraging EC have been increasingly used to classify neurodegenerative diseases and differentiate them from healthy controls. This review aims to comprehensively examine research employing EC—derived from techniques such as functional magnetic resonance imaging, electroencephalography, and magnetoencephalography—in conjunction with machine learning methods to classify neurodegenerative diseases.  \nMethods: We conducted a literature search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, collecting articles published prior to June 13, 2024, from the PubMed and Embase databases.  \nResults: We selected 16 relevant studies based on predefined inclusion criteria: six focused on Alzheimer’s disease (AD), six on mild cognitive impairment (MCI), one on Parkinson’s disease (PD), two on both AD and MCI, and one on both AD and PD. We summarized the methods for EC feature extraction and selection, the application of classifiers, validation techniques, and the accuracy of the classification models.  \nConclusion: The integration of EC with machine learning techniques has demonstrated promising potential in the classification of neurodegenerative diseases. Studies have shown that combining EC with multimodal features such as functional connectivity offers novel approaches to enhancing the performance of classification models.  \nKEYWORDS  \nAlzheimer’s disease, brain effective connectivity, classification model, deep learning, electroencephalogram, functional magnetic resonance imaging, machine learning, neurodegenerative diseases  \nFrontiers in Neurology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nNeurodegenerative diseases are characterized by cognitive decline, severe motor disability, and dementia. These diseases include Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease, and amyotrophic lateral sclerosis ( 1) . Furthermore, mild cognitive impairment (MCI), an antecedent to AD, is cat","cbCaiq6eKRr7Nkft","https://ap.wps.com/l/cbCaiq6eKRr7Nkft","pdf",1015943,1,13,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What does effective connectivity (EC) mean in this review?\",\"answer\":\"EC describes directional influences or causal relationships between brain regions, used here as a key neuroimaging-derived feature for disease classification.\"},{\"question\":\"How were the studies selected for the systematic review?\",\"answer\":\"The review followed PRISMA guidance and searched PubMed and Embase for articles published before June 13, 2024, applying predefined inclusion criteria.\"},{\"question\":\"Which neurodegenerative conditions were covered by the included studies?\",\"answer\":\"The 16 included studies focused on Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, with some studies addressing both AD and MCI or both AD and PD.\"}]","Classification of Neurodegenerative Diseases Using Brain Effective Connectivity and Machine Learning Techniques - 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