Modeling Hierarchical Functional Brain Networks via L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder for Multi-task fMRI Data

Modeling Hierarchical Functional Brain Networks via L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder for Multi-task fMRI Data | 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 Modeling Hierarchical Functional Brain Networks via L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder for Multi-task fMRI Data Puwang Cui, Huan Liu, Li Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5275268/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 Modeling functional brain networks is essential for revealing the functional mechanisms of the human brain. Deep Neural Network (DNN) models have widely been employed for extracting multi-scale spatiotemporal features from functional Magnetic Resonance Imaging (fMRI) data. Nonetheless, existing DNN approaches often struggle with capturing generic temporal features across diverse tasks due to their reliance on fully connected layers. To overcome this limitation, this paper introduces a novel framework based on a L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder (L2-FCRAAE), designed to model hierarchical functional brain networks (FBNs). The L2-FCRAAE framework is engineered with fully convolutional recurrent layers that notably exclude any fully connected layers. This design choice ensures adaptability to variable-length fMRI data, facilitating the acquisition of temporal dependencies within sequential data. Consequently, it effectively models temporal dynamics and helps in recognizing brain states from fMRI data. Moreover, the incorporation of normalized temporal and channel attention blocks into the encoder can prevent model overfitting during training, thereby enhancing the model's representation capacity. Experimental outcomes showcase that the proposed L2-FCRAAE exhibits superior capability and generalizability in capturing spatial and temporal patterns of FBNs. It robustly identifies task-related components and resting-state brain networks (RSNs) in a hierarchical manner. Overall, this study presents a novel approach for understanding the hierarchical organization of functional brain architecture. If this paper is accepted and the code is published. Hierarchical Functional Brain Network Task fMRI Fully Convolutional Recurrent Autoencoder Attention Mechanism Full Text Additional Declarations No competing interests reported. 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-5275268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367124991,"identity":"2716ea71-1319-4460-bf83-c36e90897ba9","order_by":0,"name":"Puwang Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACfvbmgw8+GNjwsLE3H2CQAAkdIKBFsudYsuGMijQ5Pp5jCcRpMbiRYybNc+awsZxEjgFEiJAWhgMJBhK8bWmJbQw53yQs/jDI8d1IYPxcgEcHYwNQj2SbDVDL2W0Skm0MxpI3EpilZ+DRwgzSYwiyhbEXqKWBIXHDjQQ2Zh48WthAehLbDie2MfM8k5D4w1BPUAsPWA/Q+2xsPGwSEmwMCQaEtEiA9QADGajD2EKyTcJw5pmHzdL4tNjff//99x9gVMrPf/zwtsQfG3m+48kHP+PTggKYJcCRydhArAag2g/Eqx0Fo2AUjIIRBABxfkv9roil+wAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Puwang","middleName":"","lastName":"Cui","suffix":""},{"id":367124992,"identity":"0c9bd0fc-b7ac-4433-adfe-b0e5e6203aa6","order_by":1,"name":"Huan Liu","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Liu","suffix":""},{"id":367124993,"identity":"e30a939a-3b5f-467d-a8dd-2e2b4bb9879c","order_by":2,"name":"Li Li","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-16 11:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5275268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5275268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75467230,"identity":"40135226-79eb-479e-9e94-69714f279549","added_by":"auto","created_at":"2025-02-05 02:01:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047605,"visible":true,"origin":"","legend":"","description":"","filename":"ModelingHierarchicalFunctionalBrainNetworksviaL2NormalizedFullyConvolutionalRecurrentAttentionAutoencoderforMultitaskfMRIData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5275268/v1_covered_2677d05a-b0ae-4592-9aa9-6bd6d3ed33ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Hierarchical Functional Brain Networks via L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder for Multi-task fMRI Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Hierarchical Functional Brain Network, Task fMRI, Fully Convolutional Recurrent Autoencoder, Attention Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-5275268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5275268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Modeling functional brain networks is essential for revealing the functional mechanisms of the human brain. 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