Distinct Neurodevelopmental Patterns and Intermediate Integration-Based Predictive Modeling in Autism

Distinct Neurodevelopmental Patterns and Intermediate Integration-Based Predictive Modeling in Autism | 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 Distinct Neurodevelopmental Patterns and Intermediate Integration-Based Predictive Modeling in Autism Yanlin Wang, Shiqiang Ma, Ruimin Ma, Linxia Xiao, Jiawei Li, Shi Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4265379/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 Autism Spectrum Disorder (ASD) is known to exhibit a more rapid brain expansion during early postnatal period of life, followed by a deceleration in age-related growth compared to typically developing children (TD). However, the development and evolution of the cerebral cortex that drive these changes remains unclear. In this study, we characterize the distinct developmental trajectory of cortical features in individuals with ASD across the lifespan, investigate the regional differences between ASD and TD, and propose a deep-learning workflow that combines graph convolutional networks with low-rank multi-model tensor fusion (LMFGCN) for ASD identification. Our findings reveal that the brain neurodevelopmental process in ASD is not a uniform process but rather is characterized by distinct developmental trajectories that vary across age and regions. The separated trajectories between cortical thickness (CT) and surface area (SA) especially at the early stage (about ages 0-8 years) indicate that early brain volume overgrowth in ASD is predominantly attributed to increased SA, rather than CT. The regional differences in CT and SA show spatially disrupted patterns that are congruent with changes in cortical volume. Moreover, we introduce LMFGCN, a deep-learning framework with intermediate fusion approach, outperforms early and late fusion methods and other state-of-the-art models in ASD classification. Overall, our results provide insights into spatiotemporal gradients of brain maturation, identify distinct brain alterations across the lifespan, and demonstrate the potential utility of LMFGCN in ASD identification. Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders Biological sciences/Neuroscience Autism Spectrum Disorder Brain Imaging Techniques Neurodevelopment Trajectory Diagnosis and Classification Full Text Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryASD.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. 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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-4265379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":316136727,"identity":"8a0555cf-5d2c-4bd6-b7fd-d8649bba2fe0","order_by":0,"name":"Yanlin 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