Semantic Concept in Brain fMRI Spatio-Temporal Voxel Patterns

Semantic Concept in Brain fMRI Spatio-Temporal Voxel Patterns | 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 Semantic Concept in Brain fMRI Spatio-Temporal Voxel Patterns Nanning Zheng, Ma Yongqiang, Haodong Jing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6674875/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 Cognitive neuroscience bridges insights into human brain mechanisms with artificial intelligence, where brain-inspired architectures have driven unprecedented success in artificial neural networks. However, endowing AI models with the dynamic functional patterns of biological brains remains a longstanding challenge. To advance computational models that better emulate the brain’s information processing, this study systematically investigates semantic representations in the human visual cortex and constructs a biologically plausible framework for categorizing embedded semantic concepts in visual stimuli. We designed a controlled cognitive experiment to analyze visual semantic processing, collecting fMRI data from 15 participants. A spatiotemporal graph network was employed to capture dynamic features of semantic brain regions, enabling the construction of functional networks for concept classification and prediction. Leveraging self-supervised learning, our decoding framework reconstructs visual stimuli and compares them with predicted categorical outputs to derive semantically coherent representations. Experimental results demonstrate the model’s superiority in decoding fMRI data, outperforming existing methods in both accuracy and semantic consistency. This unified framework integrates visual and semantic processing, offering biologically interpretable insights into brain-inspired semantic cognition. Biological sciences/Computational biology and bioinformatics/Computational models Physical sciences/Mathematics and computing/Computer science Semantic Cognition Brain-inspired Neural Networks fMRI Spatio-Temporal Graph Networks Visual Information Understanding Full Text Additional Declarations There is NO Competing Interest. 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. 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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-6674875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469460855,"identity":"9a0ddd75-1efb-4138-b6cb-f16b1c247c0c","order_by":0,"name":"Nanning Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYHACNhAhx8BwAEQzE6/FmIHhMIlaEhsgqonQYi7d/uzBxx216RsOnj8mwVBhndjAfvYAXi2Wcw6kG848czx3ZsNhNgmGM+mJDTx5CXi1GNxIOCbN23Yst58BqIWx7XBigwSPAQEtiW3Sf9uOpbOBtfwjSksymzRjW00CP1hLA1Fa0tgke9sOGAL9YmyRcCzduI0nh5CW9GcSP9vq5A1uHHx440ONtWw/+xn8WqAAGI0SBxgYEhig0UQEqGNg4G8gUu0oGAWjYBSMOAAApuFGFwWvBLYAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Nanning","middleName":"","lastName":"Zheng","suffix":""},{"id":469460856,"identity":"ed8f4cbb-70c7-4284-b549-84794c7cf083","order_by":1,"name":"Ma Yongqiang","email":"","orcid":"https://orcid.org/0000-0002-6063-5601","institution":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ma","middleName":"","lastName":"Yongqiang","suffix":""},{"id":469460857,"identity":"a4eb3288-a368-467d-b21f-22f7da1a089f","order_by":2,"name":"Haodong Jing","email":"","orcid":"","institution":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Haodong","middleName":"","lastName":"Jing","suffix":""}],"badges":[],"createdAt":"2025-05-15 17:50:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6674875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6674875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745503,"identity":"34aaf735-7e94-4e4d-83ef-710d9630c8a3","added_by":"auto","created_at":"2026-02-16 08:51:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6837908,"visible":true,"origin":"","legend":"Article File","description":"","filename":"SemanticConceptinBrainfMRISpatioTemporalVoxelPatterns20250515.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6674875/v1_covered_fa717ec5-dcba-4154-bfb5-61b20ae18788.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Semantic Concept in Brain fMRI Spatio-Temporal Voxel Patterns","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":"Semantic Cognition, Brain-inspired Neural Networks, fMRI, Spatio-Temporal Graph Networks, Visual Information Understanding","lastPublishedDoi":"10.21203/rs.3.rs-6674875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6674875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cognitive neuroscience bridges insights into human brain mechanisms with artificial intelligence, where brain-inspired architectures have driven unprecedented success in artificial neural networks. 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