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Deviations of anatomical structures from normal age trajectories have been linked to outcomes. The study predicts individual AD progression risk by measuring how baseline age- and sex-expected volumetric values differ from observed values. Method: T1w MRI scans from multiple cohorts are processed with AssemblyNET, then healthy aging trajectories are modeled using Bayesian multilevel splines. Deviation features from 70 ROIs support time-to-event conversion modeling with a linear survival SVM and permutation importance. Results: The method identifies varying AD conversion risk and attains a concordance index of 0.82 (0.04), highlighting specific ROI features and baseline diagnosis.",{"@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":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/patient-level-predicting-ad-onset-using-lifespan-volumetric-trajectories/468518/",{"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/patient-level-predicting-ad-onset-using-lifespan-volumetric-trajectories/468518.png","ImageObject",300,407,{"name":92,"@type":93},"\tCallum ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address?","Question",{"text":112,"@type":113},"It aims to predict individual risk of progressing to Alzheimer’s disease using deviations in baseline brain volumetric measurements derived from neuroimaging.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the MRI images processed and how are trajectories modeled?",{"text":117,"@type":113},"T1w MRI scans from multiple databases are processed with AssemblyNET. Healthy aging trajectories for 70 brain structures are modeled across ages 10 to 90 using cubic B-splines in a Bayesian multilevel framework.",{"name":119,"@type":110,"acceptedAnswer":120},"What modeling approach is used to predict conversion to AD?",{"text":121,"@type":113},"Baseline deviations (along with sex and baseline diagnosis) are used for time-to-event modeling of conversion to AD using a linear Survival Support Vector Machine with 10-fold cross-validation, with permutation importance to rank ROIs.",{"name":123,"@type":110,"acceptedAnswer":124},"How well does the method perform and which features matter most?",{"text":125,"@type":113},"It achieves a concordance index of 0.82 (0.04). The most important features include baseline diagnosis (CN or MCI) and volumes of Amygdala, Inferior lateral ventricles, Superior temporal gyrus, and Superior frontal gyrus.","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},468518,1790774343,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":133,"read_time":52,"preview_page_count":4},137451211410,"https://ap-avatar.wpscdn.com/avatar/2000bb0a9246f588df?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786362646172706240","DOI: 10. 1002/alz70856_107271  \nBIOMARKERS  \nPODIUM PRESENTATION  \nNEUROIMAGING  \nPatient-level predicting AD onset using lifespan volumetric trajectories  \nVladimir S Fonov1  Pedro Rosa-Neto 1, 2  D Louis Collins 1, 3  \n1 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada  \n2Translational Neuroimaging Laboratory, Montreal, QC, Canada  \n3 Montreal Neurological Institute, Montreal, QC, Canada  \nCorrespondence  \nVladimir S Fonov, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada. [Email:](Email: vladimir.fonov@mcgill.ca)[ vladimir.fonov@mcgill.ca](Email: vladimir.fonov@mcgill.ca)  \nAbstract  \nBackground: Predicting dementia risk from neuroimaging and cognitive data is vital for earlyAlzheimer’s disease(AD)management. Coupe[2019]and others have shown that the volume of certain anatomical structures deviate from the normal trajectories. We aim to predict individual AD progression risk by analyzing deviations from expected age and sex-based volumetric measurements at the baseline.  \nMethod: We utilized T1w MRI scans from several databases: (N Scans, N subjects, age range) : ADNI1,2,3 (8697, 2118, 50-97y), AIBL (1242, 667, 55-97y), HCP (1113, 1113, 28-36y), ICBM (341, 341, 18-80y), MCSA (1801,1801, 49-89y), NIHPD (1089, 442,4-22y), NKI (2306, 1326, 6-85y), OASIS1,2,3 (3212, 1802, 18-97y), UK Biobank (47396, 42912, 44-83y), PreventAD (2400, 387, 54-88y) and TRIAD (914,20-91y) and processed them with AssemblyNET [Coupe 2019].  \nWe used scans from UKBB, ICBM, NKI, HCP, and NIHPD studies, along with half of the cognitively normal (CN) subjects from ADNI1,2,3, to model healthy aging trajectories with cubic b-splines for each brain structure from ages 10 to 90 using a Bayesian multilevel model.  \nWe used baseline scans from remaining CN subjects (N = 984, 73 progressed to AD) and MCI subjects (N = 1396, 395 progressed) to calculate deviation from age and sex expected volumes as estimated for 70 ROIs extracted by AssemblyNET. These values, alongwith sex and baseline diagnosis, were used to perform time-to-event modelling of conversion toAD. We employed a linear Survival Support Vector Machine within a 10-fold cross-validation loop. Additionally, permutation importance was used to identify important ROIs.  \nResult: Our results demonstrate that the proposed method effectively identifies individuals with varying levels of risk for AD conversion, achieving a concordance indexof 0.82(0.04). The five most important features identified by the method are baseline diagnosis (CN or MCI), and volumes of Amygdala, Inferior lateral ventricles, Superior temporal gyrus and Superior frontal gyrus.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Alzheimer’s Association. Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.  \nAlzheimer’s Dement. 2025;21(Suppl. 2):e107271.  \n[https://doi.org/10.1002/alz70856_107271](https://doi.org/10.1002/alz70856_107271)  \nwi[leyonlinelibrary.com/journal/alz](leyonlinelibrary.com/journal/alz)  \n1of4  \nConclusion: We have developed a library of healthy aging trajectories for 70 anatomical structures, demonstrating its effectiveness in identifying subjects at high risk of progressing to Alzheimer’s Dementia.  \nBIOMARKERS  \nBIOMARKERS","cbCaikc62QtWp2wL","https://ap.wps.com/l/cbCaikc62QtWp2wL","pdf",1514841,"English","# Abstract\n## Background\n## Method\n## Result\n## Conclusion","[{\"question\":\"What problem does the study address?\",\"answer\":\"It aims to predict individual risk of progressing to Alzheimer’s disease using deviations in baseline brain volumetric measurements derived from neuroimaging.\"},{\"question\":\"How are the MRI images processed and how are trajectories modeled?\",\"answer\":\"T1w MRI scans from multiple databases are processed with AssemblyNET. Healthy aging trajectories for 70 brain structures are modeled across ages 10 to 90 using cubic B-splines in a Bayesian multilevel framework.\"},{\"question\":\"What modeling approach is used to predict conversion to AD?\",\"answer\":\"Baseline deviations (along with sex and baseline diagnosis) are used for time-to-event modeling of conversion to AD using a linear Survival Support Vector Machine with 10-fold cross-validation, with permutation importance to rank ROIs.\"},{\"question\":\"How well does the method perform and which features matter most?\",\"answer\":\"It achieves a concordance index of 0.82 (0.04). The most important features include baseline diagnosis (CN or MCI) and volumes of Amygdala, Inferior lateral ventricles, Superior temporal gyrus, and Superior frontal gyrus.\"}]","Patient-level predicting AD onset using lifespan volumetric trajectories | PDF"]