[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121914-en":3,"doc-seo-121914-105":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},121914,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Development of a machine learning algorithm to predict the residual cognitive reserve index","Late-life neurodegeneration drives cognitive decline unevenly, and explaining resilience requires measuring cognitive reserve in ways that are valid and accessible. This study evaluated whether a machine learning approach using standard clinical variables could predict a residual-based cognitive reserve criterion and prospectively moderate brain–cognition associations. Using MRI-based residual reserve index (RRI) in a training sample (N=1665) and validating in an independent ADNI 1/3/GO sample (N=1640), Extended and Full models showed modest-to-strong alignment with the criterion and moderated longitudinal associations.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nDevelopment of a machine learning algorithm to predict the residual cognitive reserve index  \nPermalink  \n[https://escholarship.org/uc/item/9hh257xw](https://escholarship.org/uc/item/9hh257xw)  \nJournal  \nBrain Communications, 6(4)  \nISSN  \n2632-1297  \nAuthors  \nGavett, Brandon E  \nFarias, Sarah Tomaszewski Fletcher, Evan  \net al.  \nPublication Date  \n2024-07-02  \nDOI  \n10.1093/braincomms/fcae240  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[https://doi.org/10.1093/braincomms/fcae240](https://doi.org/10.1093/braincomms/fcae240) BRAIN COMMUNICATIONS 2024: fcae240 | 1  \nBRAIN COMMUNICATIONS  \nDevelopment of a machine learning algorithm to predict the residual cognitive reserve index  \nBrandon E. Gavett, 1 Sarah Tomaszewski Farias, 1 Evan Fletcher, 1 Keith Widaman,2 Rachel A. Whitmer, 1,3and Dan Mungas 1 the Alzheimer’s Disease Neuroimaging Initiative†  \n† Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ([adni.loni.usc.edu](adni.loni.usc.edu)). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: [http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List](http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List).  \nElucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g. capable of moderating brain-cognition associations), widely accessible (e.g. does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (i) predict a residual-based cognitive reserve criterion standard and (ii) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California (UC) Davis and the Alzheimer’s Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N = 1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index (RRI) using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full [Extended model plus Clinical Dementia Rating (CDR) and Everyday Cognition (ECog) scale] . External validation was performed in an independent sample of ADNI 1/3/GO participants (N = 1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models’ cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r = 0.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r = 0.49 and 0.54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading) . The primary difference between the Minimal model—which did not perform well as a measure of cognitive reserve—and the Extended and Full models—which demonstrated good accuracy and validity—is the lack of cognitive performance and informant-r","cbCaiiLaaj3aGgUm","https://ap.wps.com/l/cbCaiiLaaj3aGgUm","pdf",2876743,1,16,"English","en",105,"# Introduction\n## Methods and modeling approach\n## Training, validation, and evaluation\n## Results and interpretation","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the lack of a cognitive reserve measure that is both valid and widely accessible for moderating brain–cognition relationships.\"},{\"question\":\"How were the machine learning models built and compared?\",\"answer\":\"Three XGBoost models were trained to predict the MRI-based residual reserve index: Minimal, Extended, and Full, differing by which clinical and cognitive variables they used.\"},{\"question\":\"Which model performed best and what explains the difference?\",\"answer\":\"The Extended and Full models correlated more strongly with the criterion standard and moderated longitudinal associations, while the Minimal model did not. The key difference was the absence of cognitive performance and informant-report data in the Minimal model.\"}]","Development of a machine learning algorithm to predict the residual cognitive reserve index | PDF",1785807713,40,{"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},"development-of-a-machine-learning-algorithm-to-predict-the-residual-cognitive-reserve-index","",{"@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/development-of-a-machine-learning-algorithm-to-predict-the-residual-cognitive-reserve-index/121914/",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},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the lack of a cognitive reserve measure that is both valid and widely accessible for moderating brain–cognition relationships.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models built and compared?",{"text":80,"@type":76},"Three XGBoost models were trained to predict the MRI-based residual reserve index: Minimal, Extended, and Full, differing by which clinical and cognitive variables they used.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what explains the difference?",{"text":84,"@type":76},"The Extended and Full models correlated more strongly with the criterion standard and moderated longitudinal associations, while the Minimal model did not. The key difference was the absence of cognitive performance and informant-report data in the Minimal model.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]