Predicting Neural Activity from Connectome Embedding Spaces

Predicting Neural Activity from Connectome Embedding Spaces | 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 Biological Sciences - Article Predicting Neural Activity from Connectome Embedding Spaces Zihan Zhang, Huanqiu Zhang, Stefan Mihalas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8845871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract While cortical connectomes contain enormous amounts of information, population activity is typically low-dimensional. Can low-dimensional connectome features reliably predict neural activity? Using the MICrONS dataset, which combines millimeter-scale, nanometer-resolution connectivity with simultaneously recorded in vivo activity, we find statistically significant alignment between morphological and functional similarity, quantified by subspace angles and centered kernel alignment. Topological analyses further show that representation spaces for both connectome and activity exhibit low-dimensional hyperbolic geometry with exponential scaling. Motivated by this shared geometry, we used multidimensional scaling to embed anatomical affinities and trained a simple linear model to reconstruct neuronal activity. The embedded connectome accounted for 68% of the variance in activity similarity, outperforming similarly simple models using the full high-dimensional connectome (56%). These results reveal robust structure-function coupling: geometry-aware dimensionality reduction discards most microscopic connectome detail yet improves prediction of neural activity. This suggests that synaptic wiring implicitly encodes an abstract low-dimensional organization underlying cortical population dynamics. Biological sciences/Neuroscience/Computational neuroscience/Network models Biological sciences/Neuroscience/Neural circuits Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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-8845871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":596517945,"identity":"492c0f9b-4d66-4990-a43d-8367b700dde3","order_by":0,"name":"Zihan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDACZh4gYWAjB+bwEK+lIM2YBC1gZR8OJTYQrUW+nffg5wKDA+kbbiQwPnjbRoQWxma+ZOkZBndygVqYDecSo4WZmcdAmsfgWe6G2wls0rzEaGFj5jH+zWNwON3gdgL7b6K08DDzmAFtOZwA1MLGTJQWCaAWax6DNMOZ9x82S845R4QW+f4zxrd5/tjI8505fPDDmzIitCABxgbS1I+CUTAKRsEowA0AcQswK+TP/FUAAAAASUVORK5CYII=","orcid":"","institution":"University of Washington \u0026 Allen Institute","correspondingAuthor":true,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Zhang","suffix":""},{"id":596517946,"identity":"8fd46bc2-85d0-4fbe-88ef-a51f360b5380","order_by":1,"name":"Huanqiu Zhang","email":"","orcid":"","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Huanqiu","middleName":"","lastName":"Zhang","suffix":""},{"id":596517947,"identity":"d941e3fe-7925-4bfe-83b1-2befa66121c1","order_by":2,"name":"Stefan Mihalas","email":"","orcid":"https://orcid.org/0000-0002-2629-7100","institution":"Allen Institute for Brain Science","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Mihalas","suffix":""}],"badges":[],"createdAt":"2026-02-11 00:40:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8845871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8845871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104402568,"identity":"d98271a1-ac7c-4109-93e7-0a8b31087216","added_by":"auto","created_at":"2026-03-11 12:15:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9358161,"visible":true,"origin":"","legend":"Article File","description":"","filename":"maintextnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8845871/v1_covered_7c2d5e4e-51f3-4088-94e7-cb88442f9876.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Predicting Neural Activity from Connectome Embedding Spaces","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8845871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8845871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"While cortical connectomes contain enormous amounts of information, population activity is typically low-dimensional. Can low-dimensional connectome features reliably predict neural activity? Using the MICrONS dataset, which combines millimeter-scale, nanometer-resolution connectivity with simultaneously recorded in vivo activity, we find statistically significant alignment between morphological and functional similarity, quantified by subspace angles and centered kernel alignment. Topological analyses further show that representation spaces for both connectome and activity exhibit low-dimensional hyperbolic geometry with exponential scaling. Motivated by this shared geometry, we used multidimensional scaling to embed anatomical affinities and trained a simple linear model to reconstruct neuronal activity. The embedded connectome accounted for 68% of the variance in activity similarity, outperforming similarly simple models using the full high-dimensional connectome (56%). These results reveal robust structure-function coupling: geometry-aware dimensionality reduction discards most microscopic connectome detail yet improves prediction of neural activity. This suggests that synaptic wiring implicitly encodes an abstract low-dimensional organization underlying cortical population dynamics.","manuscriptTitle":"Predicting Neural Activity from Connectome Embedding Spaces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 08:26:58","doi":"10.21203/rs.3.rs-8845871/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-neuroscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"neuro","sideBox":"Learn more about [Nature Neuroscience](http://www.nature.com/neuro/)","snPcode":"","submissionUrl":"","title":"Nature Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d1b090d-3390-4561-83c1-b3443e57f8c8","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63474553,"name":"Biological sciences/Neuroscience/Computational neuroscience/Network models"},{"id":63474554,"name":"Biological sciences/Neuroscience/Neural circuits"}],"tags":[],"updatedAt":"2026-03-06T08:26:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 08:26:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8845871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8845871","identity":"rs-8845871","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}