[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86439-en":3,"doc-seo-86439-105":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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86439,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","MVMGNN Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI","Alzheimer’s disease (AD) is a common neurodegenerative disorder where early diagnosis is essential to slow progression and support timely intervention. Mild cognitive impairment (MCI) is a key intermediate stage with elevated conversion risk to AD. Structural MRI (sMRI) enables detailed anatomical analysis, yet single-graph construction limits joint modeling of spatial relationships and morphological similarities. Multi-view methods face redundancy and noisy connections without effective selection and fusion. This paper proposes MVMGNN, an sMRI-based multi-view masked graph neural network.","MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using  \nStructural MRI  \nNi Yao1 ·Zhenxu Wang1·Danyang Sun1·Chuang Han1·Yanting Li1·Jiaofen Nan1·Fubao Zhu1,* ·Chen Zhao2* ·Weihua Zhou3,4  \n1 School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China  \n2 Department of Computer Science, Kennesaw State University Marietta, GA, USA  \n3 Department of Applied Computing, Michigan Technological University, Houghton, MI, USA  \n4 Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA  \nZhenxu Wang and Chen Zhao contributed equally.  \n* Correspondence: Fubao Zhu  \nEmail address: [fbzhu@zzuli.edu.cn](fbzhu@zzuli.edu.cn)  \nMailing address: School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China  \nChen Zhao  \nEmail address: [czhao4@kennesaw.edu](czhao4@kennesaw.edu)  \nMailing address: 680 Arntson Dr, Marietta, GA 30060  \nAbstract  \nAlzheimer’s disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention. Mild cognitive impairment (MCI), which represents an intermediate clinical stage between cognitively normal aging and AD, is also an important target for early identification because individuals with MCI are at an increased risk of progressing to AD. Structural magnetic resonance imaging (sMRI) provides detailed characterization of anatomical structures and plays an important role in AD-related brain analysis. However, existing sMRI-based brain network methods typically rely on a single graph construction strategy, limiting their ability to jointly capture spatial relationships and morphological similarities between brain regions. While multi-view approaches can incorporate complementary information, they are often affected by redundant features and noisy connections without effective selection and fusion mechanisms. To address these issues, this paper proposes an sMRI-based multi-view masked graph neural network model (MVMGNN) for AD diagnosis. Brain regions are regarded as nodes with and radiomics features, and two complementary graph views are constructed based on spatial proximity and feature similarity. A joint node–edge masking mechanism is proposed to simultaneously select radiomics feature dimensions and structural connections, reducing redundancy during graph learning. Furthermore, a patient-level cross-view gated fusion mechanism is proposed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN outperforms several competing approaches in AD classification. Interpretability analysis further demonstrates that MVMGNN is able to identify key brain regions associated with AD, providing useful insights into discriminative patterns in sMRI-based brain networks.Our implementation is publicly available at [https://github.com/chenzhao2023/MVMGNN_AD](https://github.com/chenzhao2023/MVMGNN_AD)[ ](https://github.com/chenzhao2023/MVMGNN_AD)[Keywords:](Keywords: Alzheimer)[ Alzheimer](Keywords: Alzheimer)’s disease; structural MRI; multi-view learning; graph neural network; crossview gated fusion  \n1. Introduction  \nAlzheimer’s disease (AD) is a chronic neurodegenerative disorder commonly observed in the elderly population and one of the leading causes of dementia. With the acceleration of global population aging, AD has become one of the most prevalent, fatal, and socioeconomically burdensome diseases of the 21st century[1] . Therefore, achieving early and accurate diagnosis of AD is of great practical significance for delaying disease progression and developing individualized intervention strategies[2] .  \nIn recent years, computer-aided diagnosis methods based on neuroimaging data have made significant progress in the prediction of AD ","cbCaisCMwEtcWMXU","https://ap.wps.com/l/cbCaisCMwEtcWMXU","pdf",1790951,3,1,24,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the goal of MVMGNN in diagnosing Alzheimer’s disease?\",\"answer\":\"MVMGNN aims to improve early AD diagnosis from structural MRI by leveraging multi-view graph learning to better capture brain-region relationships and reduce misleading information.\"},{\"question\":\"How does MVMGNN construct the two graph views?\",\"answer\":\"It treats brain regions as nodes with radiomics features and builds two complementary views based on spatial proximity and feature similarity.\"},{\"question\":\"What mechanisms does MVMGNN use to reduce redundancy and integrate multi-view information?\",\"answer\":\"It introduces joint node–edge masking to select radiomics feature dimensions and structural connections, and a patient-level cross-view gated fusion mechanism to integrate representations from both views.\"}]",1784211744,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mvmgnn-multi-view-masked-graph-neural-network-for-alzheimers-disease-diagnosis-using-structural-mri","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/mvmgnn-multi-view-masked-graph-neural-network-for-alzheimers-disease-diagnosis-using-structural-mri/86439/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of MVMGNN in diagnosing Alzheimer’s disease?","Question",{"text":75,"@type":76},"MVMGNN aims to improve early AD diagnosis from structural MRI by leveraging multi-view graph learning to better capture brain-region relationships and reduce misleading information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does MVMGNN construct the two graph views?",{"text":80,"@type":76},"It treats brain regions as nodes with radiomics features and builds two complementary views based on spatial proximity and feature similarity.",{"name":82,"@type":73,"acceptedAnswer":83},"What mechanisms does MVMGNN use to reduce redundancy and integrate multi-view information?",{"text":84,"@type":76},"It introduces joint node–edge masking to select radiomics feature dimensions and structural connections, and a patient-level cross-view gated fusion mechanism to integrate representations from both 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