No significant changes in synaptic density and gray matter volume following motor learning

No significant changes in synaptic density and gray matter volume following motor learning | 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 No significant changes in synaptic density and gray matter volume following motor learning Melina Hehl, Takuya Toyonaga, Richard E. Carson, Patrick Dupont, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8957106/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: In animal neurophysiology, motor learning induces long-term potentiation- and depression-like plasticity, i.e., the strengthening or weakening of connections between neurons. In humans, gray matter volume (GMV) changes, measured non-invasively by MRI, in response to motor learning are interpreted as a surrogate measure of plasticity. Here we measure synaptic density with positron emission tomography (PET) to investigate the learning-induced synaptic plasticity more directly, and MRI-based GMV to investigate structural plasticity. Methods: Twenty-two volunteers participated in a simple or complex (group factor) four-week motor training on a bimanual tracking task (BTT). Learning progress was modelled individually. [18F]SynVesT-1 PET and T1-weighted MRI were acquired at baseline (PRE) and immediately after the end of the motor training (POST), and at MID for MRI only (time factor). We restricted our analyses to six a priori chosen VOIs of the visuomotor network, extracted individually from FreeSurfer (v6.0.0) cortical surface parcellation. Average standard uptake value ratios (SUVR), GMV and average cortical thickness (aCT) in each VOI were statistically compared over time, group and time-by-group, and associated with learning progress. Results: Participants’ BTT performance improved significantly, and more for the complex than the simple training group. The VOI-based PET and GM analyses did not yield any significant results. Conclusion: We did not identify significant changes in [18F]SynVesT-1 synaptic density as a surrogate marker for plasticity after motor learning in the a priori chosen VOIs. Health sciences/Neurology Biological sciences/Neuroscience Neuroplasticity synaptic density SV2A gray matter motor learning Full Text Additional Declarations No competing interests reported. Supplementary Files Hehletal2026Supplement.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 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. 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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-8957106","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602140165,"identity":"b13481b2-7dea-4be8-a000-6e67b60495e9","order_by":0,"name":"Melina Hehl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3PMUvDQBTA8XcEOrW6viDoJxBSCsEh+FlyBOomiiAIghcKz6XomoIfIm7dbDholkPX66RZ3IvQUX3QuggXOzrcfzi4gx/vHYDP9y8L1IzPFDDIGwVH60eh2oj4IWLUV4DbEdgQCrcih7d5rs+u4XR3L6erCeF+ZLP5m5gmThKbSuliDhfhQ0WLR8JBZIcnkTBDN7FS6W4HZGklLRpCWRoT85LaTV4bJp8gn5icM7kpzcuKyVfLFKF0j3gKShK8WBrV4w6TWctfeLHeHcrCylFYPGN/UlOM0mRuUmv90V0l8r7IquX4MjnY0cE7LqfHTrIJf93Tv4DP5/P5WvsGI7pfdCQnPAsAAAAASUVORK5CYII=","orcid":"","institution":"KU Leuven","correspondingAuthor":true,"prefix":"","firstName":"Melina","middleName":"","lastName":"Hehl","suffix":""},{"id":602140166,"identity":"5f97f53f-b578-4b6f-a68f-a21e791e3d7d","order_by":1,"name":"Takuya Toyonaga","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Toyonaga","suffix":""},{"id":602140167,"identity":"c42ba1bc-49d1-426d-9721-2b032a046f3c","order_by":2,"name":"Richard E. 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In humans, gray matter volume (GMV) changes, measured non-invasively by MRI, in response to motor learning are interpreted as a surrogate measure of plasticity. Here we measure synaptic density with positron emission tomography (PET) to investigate the learning-induced synaptic plasticity more directly, and MRI-based GMV to investigate structural plasticity.\n\nMethods: Twenty-two volunteers participated in a simple or complex (group factor) four-week motor training on a bimanual tracking task (BTT). Learning progress was modelled individually. [18F]SynVesT-1 PET and T1-weighted MRI were acquired at baseline (PRE) and immediately after the end of the motor training (POST), and at MID for MRI only (time factor). We restricted our analyses to six a priori chosen VOIs of the visuomotor network, extracted individually from FreeSurfer (v6.0.0) cortical surface parcellation. 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