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Objective: Develop and analyze novel biomarkers to predict DAT development and progression. Methods: An ensemble learning classifier with feature selection created an image/genotype DAT score using MRI only, genetics only, or combined multimodal data, with stratification across DAT stages and a 0.5 threshold for future conversion prediction. Results on ADNI show genetic scores improve prediction for normal controls (Accuracy 0.857), MRI better characterizes stable MCI (Accuracy 0.614), and multimodal fusion improves remaining groups. Conclusion: MRI and genetics contribute complementary signals; appropriate multimodal integration enhances predictive performance.",{"@graph":69,"@context":126},[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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-multimodal-neuroimaging-genomics-dementia-score-for-predicting-future-conversion-to-alzheimers-disease/289992/",{"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/machine-learning-based-multimodal-neuroimaging-genomics-dementia-score-for-predicting-future-conversion-to-alzheimers-disease/289992.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What data modalities were used to build the dementia score?","Question",{"text":112,"@type":113},"The study uses MRI-only, genetics-only, and combined multimodal data to compute an image/genotype DAT score.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the researchers predict future conversion to Alzheimer’s-type dementia?",{"text":117,"@type":113},"A predictive classifier outputs DAT scores, and a predefined 0.5 threshold is used to decide whether a subject will develop DAT in the future.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main performance findings for genetic vs. MRI scores?",{"text":121,"@type":113},"For currently normal control subjects, genetic-based scores better predicted future DAT progression (Accuracy 0.857) than MRI (Accuracy 0.143). MRI better characterized subjects with stable mild cognitive impairment (Accuracy 0.614) than genetics (Accuracy 0.356).",{"name":123,"@type":110,"acceptedAnswer":124},"Why can multimodal MRI and genetic data improve prediction?",{"text":125,"@type":113},"MRI reflects anatomical brain changes associated with DAT progression, while genetic data can detect risk prior to symptomatic onset; combining both modalities improves classification across stratified groups.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},289992,1789639443,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":133,"read_time":148,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer’s Disease  \nGhazal Mirabnahrazam a,1, Da Ma b,a, Sieun Lee a,g, Karteek Popuri a, Hyunwoo Lee c, Jiguo Cao d, Lei Wang e, James E Galvin f, Mirza Faisal Beg a, ∗, and the Alzheimer’s Disease Neuroimaging Initiative 2  \na School of Engineering, Simon Fraser University, Burnaby, BC, Canada  \nb School of Medicine, Wake Forest University, Winston-Salem, NC, USA  \nc Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada d Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada e Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USAf Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA g Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom  \nAbstract  \nBackground: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer’s type (DAT) .  \nObjective: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT.  \nMethods: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject’s likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject would develop DAT in the future.  \nResults: Our results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy=0 .857) compared to MRI (Accuracy=0 . 143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy=0 .614) compared to genetics (Accuracy=0 .356) . Combining MRI and genetic data showed improved classification performance in the remaining stratified groups.  \nConclusion: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data in the right way can improve prediction performance.  \n∗Corresponding author: Mirza Faisal Beg, PhD, P. Eng., Michael Smith Foundation for Health Research Scholar, School of Engineering Science, Simon Fraser University, ASB 8857, 8888 University Drive, Phone: (778) 782-5696, Website: [http://www2.ensc.sfu.ca/~mfbeg/](http://www2.ensc.sfu.ca/~mfbeg/)  \nEmail [addresses: ghazal_mirabnahrazam@sfu.ca](addresses: ghazal_mirabnahrazam@sfu.ca) (Ghazal Mirabnahrazam), [da_ma@sfu.ca](da_ma@sfu.ca) (Da Ma), [sieun.lee@nottingham.ac.uk](sieun.lee@nottingham.ac.uk)  \n(Sieun Lee), [kpopuri@sfu.ca](kpopuri@sfu.ca) (Karteek Popuri), [hyunwoo.lee@ubc.ca](hyunwoo.lee@ubc.ca) (Hyunwoo Lee), jiguo_cao@sfu.ca (Jiguo Cao),  \n[Lei.Wang@osumc.edu](Lei.Wang@osumc.edu) (Lei Wang), [jeg200@miami.edu](jeg200@miami.edu) (James E Galvin), [faisal-lab@sfu.ca](faisal-lab@sfu.ca) (Mirza Faisal Beg)  \n1 First Author  \n2 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ([http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided](http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided)[ ](h","cbCail1uOKMNYIu1","https://ap.wps.com/l/cbCail1uOKMNYIu1","pdf",1700719,35,"English","# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What data modalities were used to build the dementia score?\",\"answer\":\"The study uses MRI-only, genetics-only, and combined multimodal data to compute an image/genotype DAT score.\"},{\"question\":\"How did the researchers predict future conversion to Alzheimer’s-type dementia?\",\"answer\":\"A predictive classifier outputs DAT scores, and a predefined 0.5 threshold is used to decide whether a subject will develop DAT in the future.\"},{\"question\":\"What were the main performance findings for genetic vs. MRI scores?\",\"answer\":\"For currently normal control subjects, genetic-based scores better predicted future DAT progression (Accuracy 0.857) than MRI (Accuracy 0.143). MRI better characterized subjects with stable mild cognitive impairment (Accuracy 0.614) than genetics (Accuracy 0.356).\"},{\"question\":\"Why can multimodal MRI and genetic data improve prediction?\",\"answer\":\"MRI reflects anatomical brain changes associated with DAT progression, while genetic data can detect risk prior to symptomatic onset; combining both modalities improves classification across stratified groups.\"}]","Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer’s Disease | PDF",88]