Machine Learning to Infer Neurocognitive Testing Scores Among Adolescents and Young Adults with Congenital Heart Disease

Machine Learning to Infer Neurocognitive Testing Scores Among Adolescents and Young Adults with Congenital Heart Disease | 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 Machine Learning to Infer Neurocognitive Testing Scores Among Adolescents and Young Adults with Congenital Heart Disease Mohammad Arafat Hussain, Sheng He, Heather R. Adams, Evdokia Anagnostou, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787532/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Editorial Note 26 June, 2025. The author notified Research Square that the original funding statement for this preprint was incorrect due to accidental inclusion of an older funding statement. The funding statement for this preprint has been updated to correct this error and now reads: " This work is supported by the National Institutes of Health – 1066 [5U01HL131003-09 (subaward number: OS00000958)]. " Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract Congenital heart disease (CHD) affects approximately 1% of newborns and is associated with an increased risk for neurodevelopmental impairments. Identification and characterization of the factors affecting neurocognitive outcomes in adolescents and young adults (AYAs) with CHD remains an important area of research. We have integrated demographic, parental, socioeconomic, genetic, and brain magnetic resonance imaging (MRI) features into multivariate machine learning to infer neuropsychological testing scores with an enhanced forward inclusion backward elimination (FIBE) technique. Across 89 participants (aged 7–30 years), we included 15 neurocognitive assessments in 7 domains, achieving Pearson’s correlations 𝑟 = 0.25−0.65 between actual and inferred scores. General intelligence, working memory, and processing speed were most inferable, with various MRI features, the loss-of-functon(LoF) genetic variants in genes associated with neurodevelopmental disorders or that function as chromatin modifiers, sex, and father’s education level as key joint covariates. In contrast, oral language and perceptual organization were the least inferable. These findings highlight the combinatorial effects of genetic and environmental factors on the individual variability in neurocognitive functions among AYAs with CHD. Given the small sample size and data heterogeneity, further investigation in large cohorts is needed. Health sciences/Biomarkers/Predictive markers Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Machine Learning to Infer Neurocognitive Testing Scores Among Adolescents and Young Adults with Congenital Heart Disease 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6787532","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466942511,"identity":"a197141c-e307-4b1c-9ece-dca12d22c21d","order_by":0,"name":"Mohammad Arafat Hussain","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0545-5779","institution":"Boston Children's Hospital, Harvard Medical School","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Arafat","lastName":"Hussain","suffix":""},{"id":466942512,"identity":"4109717f-6ed8-43e4-ae4e-024726b08f6b","order_by":1,"name":"Sheng He","email":"","orcid":"","institution":"Boston Children's Hospital, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"He","suffix":""},{"id":466942513,"identity":"9c004288-16a1-4911-96d7-eca5cbadb51e","order_by":2,"name":"Heather R. 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