[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122683-en":3,"doc-seo-122683-105":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},122683,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Brain disease research based on functional magnetic resonance imaging data and machine learning - a review - review","Brain diseases including neurodegenerative and neuropsychiatric disorders impose a substantial public-health burden and remain difficult to diagnose due to unclear pathogenesis and reliance on subjective clinical scoring. Functional MRI offers a powerful way to measure brain activity and supports new insights for diagnosis. Machine-learning methods have recently achieved better performance than conventional approaches. This review surveys representative fMRI-based machine-learning studies from the last three years, covering major disorders and summarizing dataset sizes, feature engineering, selection strategies, models, validation methods, and reported accuracies, then outlines future research directions for interdisciplinary AI-aided diagnosis.","TYPE Review  \nPUBLISHED 17 August 2023  \nDOI 10. 3389/fnins.2023.1227491  \nOPEN ACCESS  \nEDITED BY  \nLu Zhao,  \nUniversity of Southern California, United States  \nREVIEWED BY  \nMario Versaci,  \nMediterranea University of Reggio Calabria, Italy Esmaeil Mohammadi,  \nUniversity of Oklahoma Health Sciences Center, United States  \n*CORRESPONDENCE  \nNa Li  \n [lina2864@csu.edu.cn](lina2864@csu.edu.cn)  \nRECEIVED 23 May 2023  \nACCEPTED 13 July 2023  \nPUBLISHED 17 August 2023  \nCITATION  \nTeng J, Mi C, Shi J and Li N (2023) Brain disease research based on functional magnetic resonance imaging data and machine learning:  \na review. Front. Neurosci. 17:1227491 .  \ndoi: 10.3389/fnins.2023.1227491  \nCOPYRIGHT  \n© 2023 Teng, Mi, Shi and Li. This is an  \nopen-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.  \nBrain disease research based on functional magnetic resonance imaging data and machine learning: a review  \nJing Teng1 , Chunlin Mi1 , Jian Shi2 and Na Li3*  \n1 School of Control and Computer Engineering, North China Electric Power University, Beijing, China,  \n2 Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital of Central South University, Changsha, China, 3 Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, China  \nBrain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the a􀀀ected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer’s disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson’s disease. We summarize these 55 articles from multiple perspectives, including the e􀀀ect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.  \nKEYWORDS  \nbrain diseases, functional magnetic resonance imaging, machine learning, diagnosis, feature selection  \n1. Introduction  \nThe brain is the most complicated and delicate biological organ in human cognition, which contains nearly 100 billion neurons with over 1,000 trillion synaptic connections between neurons (Koch and Laurent, 1999; Azevedo et al., 2009; Zhang, 2019) . It processes various information humans obtain daily, regulates various bodily functions, and manages advanced activities such as emotion, movement, learning, and memory (Raji et al., 2009; Shoeibi et al., 2023) . Due to the extremely 􀀂ne biological structure of the brain, minor damage to its internal functions is highly likely to lead to diseases such as Alzheimer’s disease (AD) (Tanveer et al., 2020), mild cognitive impairment (MCI) (Fathi et al., 2022), schizophrenia (SCZ) (Fathi et al., 2022), Parkinson’s disease (PD) (Li and Li, 2022), autism spectrum disorders (ASD)","cbCaieuaVwfTAQha","https://ap.wps.com/l/cbCaieuaVwfTAQha","pdf",1223091,1,18,"English","en",105,"# Introduction\n## Brain diseases and diagnostic challenges\n## Functional MRI and neuroimaging tools\n## Machine learning for fMRI-based diagnosis","[{\"question\":\"Why is diagnosing brain diseases considered challenging?\",\"answer\":\"Brain disease diagnosis lacks a universal gold standard and often depends on clinical symptom scores and clinicians’ experience, which can be subjective and inefficient, leading to misdiagnosis or omissions.\"},{\"question\":\"What role does functional magnetic resonance imaging (fMRI) play in this research?\",\"answer\":\"fMRI measures brain activity by tracking variations in blood flow and blood oxygen concentration, enabling researchers to distinguish activation patterns between patients and healthy controls.\"},{\"question\":\"What aspects of machine-learning studies are summarized in the review?\",\"answer\":\"The review synthesizes 55 recent articles by dataset size, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracy results.\"}]","Brain disease research based on functional magnetic resonance imaging data and machine learning - a review - review | PDF",1785812175,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"brain-disease-research-based-on-functional-magnetic-resonance-imaging-data-and-machine-learning-a-review-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/brain-disease-research-based-on-functional-magnetic-resonance-imaging-data-and-machine-learning-a-review-review/122683/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is diagnosing brain diseases considered challenging?","Question",{"text":75,"@type":76},"Brain disease diagnosis lacks a universal gold standard and often depends on clinical symptom scores and clinicians’ experience, which can be subjective and inefficient, leading to misdiagnosis or omissions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does functional magnetic resonance imaging (fMRI) play in this research?",{"text":80,"@type":76},"fMRI measures brain activity by tracking variations in blood flow and blood oxygen concentration, enabling researchers to distinguish activation patterns between patients and healthy controls.",{"name":82,"@type":73,"acceptedAnswer":83},"What aspects of machine-learning studies are summarized in the review?",{"text":84,"@type":76},"The review synthesizes 55 recent articles by dataset size, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracy results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]