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This study develops artificial intelligence models that learn neurocognitive trajectories from multimodal neuroimaging, using information from 653 ADNI participants. Models predict future clinical diagnoses and cognitive decline quantitatively and qualitatively. Results report strong diagnostic prediction and domain-wise deterioration detection, and show that modality-specific pre-training with late fusion improves accuracy.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/deep-multimodal-learning-for-domain-level-cognitive-decline-prediction-in-alzheimers-disease/407607/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/deep-multimodal-learning-for-domain-level-cognitive-decline-prediction-in-alzheimers-disease/407607.png","ImageObject",300,407,{"name":92,"@type":93},"Sarah ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in Alzheimer’s disease?","Question",{"text":112,"@type":113},"It targets prediction of cognitive decline and future clinical diagnoses despite high variability in disease progression.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which data modalities and inputs are used for the models?",{"text":117,"@type":113},"The models use structural T1-weighted MRI, FDG-PET, AV45-PET, neuropsychological assessments, and demographic variables.",{"name":119,"@type":110,"acceptedAnswer":120},"What modeling framework is proposed to improve performance?",{"text":121,"@type":113},"The study proposes modality-specific pre-training to learn feature embeddings, then integrates them via a late-fusion layer for downstream prediction.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},407607,1790625054,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962085320529,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","TYPE Original Research PUBLISHED 17 December 2025 DOI 10. 3389/frai.2025.1731062  \nOPEN ACCESS  \nEDITED BY  \nMoolchand Sharma,  \nMaharaja Agrasen Institute of Technology, India  \nREVIEWED BY  \nVikash Yadav,  \nGovernment Polytechnic Bighapur, Unnao, India  \nMonica Bhutani,  \nBharati Vidyapeeth’s College of Engineering, India  \n*CORRESPONDENCE  \nFernando García-Gutiérrez  \n [fegarc05@ucm.es](fegarc05@ucm.es)  \nRECEIVED 23 October 2025  \nREVISED 15 November 2025  \nACCEPTED 24 November 2025  \nPUBLISHED 17 December 2025  \nCITATION  \nGarcía-Gutiérrez F, Matias-Guiu JA and Ayala JL (2025) Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer’s disease.  \nFront. Artif. Intell. 8:1731062 .  \ndoi: 10.3389/frai.2025.1731062  \nCOPYRIGHT  \n© 2025 García-Gutiérrez, Matias-Guiu and Ayala. 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.  \nDeep multimodal learning for domain-level cognitive decline prediction in Alzheimer’s disease  \nFernando García-Gutiérrez1*, Jordi A. Matias-Guiu2 and José L. Ayala1 for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)  \n1 Department of Computer Architecture and Automation, Universidad Complutense de Madrid, Madrid, Spain, 2 Department of Neurology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain  \nIntroduction: Alzheimer’s disease (AD) is characterized by significant variability in clinical progression; however, few studies have focused on developing models to predict cognitive decline. Anticipating these trajectories is essential for patient management, care planning, and developing new treatments. This study explores the potential of artificial intelligence (AI) techniques to model neurocognitive trajectories from multimodal neuroimaging data and further investigates different data representation frameworks.  \nMethods: Using information from 653 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we developed models to predict future clinical diagnoses and cognitive decline, both quantitatively (rate of decline) and qualitatively (presence or absence of decline) . Input features included structural T1-weighted magnetic resonance imaging (MRI), [ 18 F]-fluorodeoxyglucose positron emission tomography (FDG-PET), [ 18 F]-florbetapir PET (AV45-PET), neuropsychological assessments, and demographic variables. Several information representation strategies were explored, including tabular data models, convolutional neural networks (CNNs), and graph neural networks (GNNs) . Furthermore, to maximize the use of all available information, we proposed a modeling framework that performed modality-specific pre-training to learn feature embeddings, which were then integrated through a late-fusion layer to produce a unified representation for downstream prediction.  \nResults: The modeling strategies demonstrated good predictive performance for future clinical diagnoses, consistent with previous studies (F1 = 0. 779) . Quantitative models explained approximately 29.4%–36.0% of the variance in cognitive decline. In the qualitative analysis, the models achieved AUC values above 0.83 when predicting cognitive deterioration in the memory, language, and executive function domains. Architecturally, CNN- and GNN-based models yielded the best performance, and the proposed pre-training strategy consistently improved predictive accuracy.  \nConclusions: This study demonstrates that AI techniques can capture patterns of cognitive decline by exploiting multimodal neuroimaging data. These findings contribute to the development of more precis","cbCaifejlVG76ngb","https://ap.wps.com/l/cbCaifejlVG76ngb","pdf",7542640,18,"English","# Introduction\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What problem does the study address in Alzheimer’s disease?\",\"answer\":\"It targets prediction of cognitive decline and future clinical diagnoses despite high variability in disease progression.\"},{\"question\":\"Which data modalities and inputs are used for the models?\",\"answer\":\"The models use structural T1-weighted MRI, FDG-PET, AV45-PET, neuropsychological assessments, and demographic variables.\"},{\"question\":\"What modeling framework is proposed to improve performance?\",\"answer\":\"The study proposes modality-specific pre-training to learn feature embeddings, then integrates them via a late-fusion layer for downstream prediction.\"}]","Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer’s disease | PDF",1790575090,45]