Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technology

Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technology | 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 Research Article Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technology Ziyi Yang, Rongzhao He, Yucen Sheng, Dalin Zhu, Ying Wang, Yu Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5636908/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Neonates with marked brain developmental delays are at increased risk of neurodevelopmental disorders. Brain chronological age is a valuable biomarker for assessing abnormal maturation in developing brains; however, accurately estimating brain age at birth remains challenging. In this study, we introduce a cross-modal relationship inference network (CMRINet) that integrates structural and diffusion magnetic resonance imaging data to improve the accuracy of neonatal brain age estimation. The CMRINet employs a Transformer encoder and relational inference module to capture both the long- and short-range dependencies of multimodal features among cortical parcels. Our model outperformed others in predicting neonatal brain age, achieving a mean squared error of 0.51 and a mean absolute error of 0.55 on the test set. By applying the model trained on full-term neonates to preterm infants at term-equivalent age, we found that the predicted age was significantly lower than the chronological age, suggesting delayed development in preterm brains. Furthermore, the deviation of predicted age was significantly associated with long-term motor development of preterm infants. These findings highlight the effectiveness of the CMRINet for neonatal brain age estimation, with potential clinical utility in early detection of neurodevelopmental risks during the perinatal period. Neonate Multimodal MRI Deep Learning Brain Age Prediction Neurodevelopmental Risks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Dec, 2024 Editor assigned by journal 19 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 13 Dec, 2024 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. 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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-5636908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392372379,"identity":"a4c96ca5-a121-4b9d-94c6-31adf29c9304","order_by":0,"name":"Ziyi Yang","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ziyi","middleName":"","lastName":"Yang","suffix":""},{"id":392372382,"identity":"8082846f-b7bf-4281-8243-d0547c6b2d3d","order_by":1,"name":"Rongzhao He","email":"","orcid":"","institution":"Lanzhou 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Brain chronological age is a valuable biomarker for assessing abnormal maturation in developing brains; however, accurately estimating brain age at birth remains challenging. In this study, we introduce a cross-modal relationship inference network (CMRINet) that integrates structural and diffusion magnetic resonance imaging data to improve the accuracy of neonatal brain age estimation. The CMRINet employs a Transformer encoder and relational inference module to capture both the long- and short-range dependencies of multimodal features among cortical parcels. Our model outperformed others in predicting neonatal brain age, achieving a mean squared error of 0.51 and a mean absolute error of 0.55 on the test set. By applying the model trained on full-term neonates to preterm infants at term-equivalent age, we found that the predicted age was significantly lower than the chronological age, suggesting delayed development in preterm brains. Furthermore, the deviation of predicted age was significantly associated with long-term motor development of preterm infants. 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