Machine Learning and Complex Network Analysis of Drug Effects on Neuronal Microelectrode Biosensor Data

Machine Learning and Complex Network Analysis of Drug Effects on Neuronal Microelectrode Biosensor 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 Article Machine Learning and Complex Network Analysis of Drug Effects on Neuronal Microelectrode Biosensor Data Manuel Ciba, Marc Petzold, Caroline L. Alves, Francisco A. Rodrigues, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5926669/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA A receptor antagonist inducing hypersynchrony, demonstrated the workflow’s ability to detect pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. Comparing machine learning-based results with linear mixed model statistical tests validated the biological relevance of the rankings obtained while emphasizing caution when interpreting inconsistencies. This robust framework enables analysis of subtle or complex drug effects on in vitro neuronal networks, advancing biosensor applications in neuropharmacology and drug discovery. Biological sciences/Computational biology and bioinformatics/Data mining Physical sciences/Mathematics and computing/Statistics Biological sciences/Systems biology/Complexity Biological sciences/Stem cells/Mammary stem cells Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 25 Feb, 2025 Reviews received at journal 12 Feb, 2025 Reviewers agreed at journal 03 Feb, 2025 Reviewers invited by journal 03 Feb, 2025 Editor assigned by journal 03 Feb, 2025 Editor invited by journal 03 Feb, 2025 Submission checks completed at journal 31 Jan, 2025 First submitted to journal 29 Jan, 2025 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-5926669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":428380902,"identity":"9d87846e-6b80-4f78-ac42-8762aadd59b3","order_by":0,"name":"Manuel Ciba","email":"","orcid":"","institution":"Aschaffenburg University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Ciba","suffix":""},{"id":428380903,"identity":"89676baa-7651-4628-ad76-b4deb28a5153","order_by":1,"name":"Marc Petzold","email":"","orcid":"","institution":"Aschaffenburg University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Petzold","suffix":""},{"id":428380904,"identity":"d6aa7f2a-7e84-43af-877c-efe86d5515f3","order_by":2,"name":"Caroline L. 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