Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior

Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior | 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 Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior Xiuyi Wang, Baihan Lyu, Katya Krieger-Redwood, Nicholas Souter, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7082183/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract The brain is a dynamic system where complex behaviours emerge from interactions across regions. Linking brain function to cognition requires tools that are sensitive to these dynamics. We introduce a technique, Feature Similarity (FS), which represents a conceptual advancement in measuring brain region interactions by integrating multiple features, moving beyond traditional methods that focus on a single measure. Our results show that FS can capture functional brain organisation: regions within the same functional network have greater FS compared to those in different networks, and FS also identifies the principal gradient that spans from unimodal to transmodal cortices. FS demonstrated greater sensitivity to task modulation than functional connectivity (FC) and 46 out of 49 statistical measures of pairwise interactions (SPIs). Specifically, FS reveals interaction patterns missed by FC and most SPIs, such as a double dissociation in Dorsal Attention Network: greater interaction with Visual network during working memory tasks and greater interaction with default mode network during long-term memory tasks. These findings position FS as a powerful tool for capturing task-specific brain network dynamics and advancing our understanding of cognitive flexibility. Using FS to reanalyse existing fMRI data could resolve inconsistencies, lead to novel insights, and prompt development of new frameworks. Biological sciences/Neuroscience/Cognitive neuroscience/Attention Biological sciences/Psychology/Human behaviour fMRI functional interaction feature similarity Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Wang2025FeatureSimilaritySM.pdf Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior RS1004.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Communications Biology → 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-7082183","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486482175,"identity":"678bc16e-6bc7-442a-b269-320e5318dac7","order_by":0,"name":"Xiuyi 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