Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment

Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment Daniel Schulman, Ali Jannati, Tanya Talkar, David J. Libon, Rod Swenson, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8532476/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Traditional neuropsychological assessment for diagnosis of mild cognitive impairment (MCI) or dementia requires a lengthy in-clinic evaluation by a specialist. This creates a substantial patient burden and prolonged diagnostic and treatment timelines. Digital cognitive assessments (DCA) offer a scalable solution to meet these challenges, but their validation is challenged by the scarcity of large, high-quality datasets with established ground truth. We applied a semi-supervised model-based clustering method to combine a large dataset (N=1189) of the Digital Assessment of Cognition (DAC), a brief, remote-capable DCA, with a smaller dataset pairing DAC assessments with ground-truth neuropsychological diagnoses (N=248). The resulting model identified cognitively unimpaired, MCI, and dementia groups with high accuracy on an external test dataset. Congruent validity was established through strong expected associations with traditional analog assessments. These results validate prior exploratory work and demonstrate the potential for more nuanced, holistic, and scalable cognitive assessments in non-specialist settings. Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations Competing interest reported. Dr. Schulman is an employee of Linus Health, and receives stock options from Linus Health. Dr. Jannati is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity. Dr. Talkar is an employee of Linus Health, and receives stock options from Linus Health. Dr. Libon consults to Linus Health, and receives royalties from Oxford University Press and Linus Health. Mr. Higgins is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity. Dr. Pascual-Leone serves as a paid member of the scientific advisory boards for Neuroelectrics, TetraNeuron, MedRhythms, Bitbrain, and AscenZion. He is co-founder of TI solutions and co-founder and chief medical officer of Linus Health. Dr. Pascual-Leone is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging, and applications of noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. Dr. Tobyne is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity. Supplementary Files ApheleiaDACClusteringSupplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Dr. Schulman is an employee of Linus Health, and receives stock options from Linus Health.\nDr. Jannati is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity.\nDr. Talkar is an employee of Linus Health, and receives stock options from Linus Health.\nDr. Libon consults to Linus Health, and receives royalties from Oxford University Press and Linus Health.\nMr. Higgins is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity.\nDr. Pascual-Leone serves as a paid member of the scientific advisory boards for Neuroelectrics, TetraNeuron, MedRhythms, Bitbrain, and AscenZion. He is co-founder of TI solutions and co-founder and chief medical officer of Linus Health.\nDr. Pascual-Leone is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging, and applications of noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia.\nDr. Tobyne is an employee of Linus Health, receives stock options from Linus Health, and is listed as an inventor on pending patents related to digital biomarkers of cognitive and functional status and amyloid positivity.","formattedTitle":"Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8532476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8532476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traditional neuropsychological assessment for diagnosis of mild cognitive impairment (MCI) or dementia requires a lengthy in-clinic evaluation by a specialist. This creates a substantial patient burden and prolonged diagnostic and treatment timelines. Digital cognitive assessments (DCA) offer a scalable solution to meet these challenges, but their validation is challenged by the scarcity of large, high-quality datasets with established ground truth. We applied a semi-supervised model-based clustering method to combine a large dataset (N=1189) of the Digital Assessment of Cognition (DAC), a brief, remote-capable DCA, with a smaller dataset pairing DAC assessments with ground-truth neuropsychological diagnoses (N=248). The resulting model identified cognitively unimpaired, MCI, and dementia groups with high accuracy on an external test dataset. Congruent validity was established through strong expected associations with traditional analog assessments. 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