A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography

A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography | 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 A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography Alina Troglio, Andrea Fiebig, Anna Maxion, Ekaterina Kutafina, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6874570/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Analyzing temporal spike patterns in nociceptors recorded via microneurography is challenging due to the use of a single recording electrode, waveform variability, and high similarity of spike shapes across neurons limiting interpretation of sensory coding such as pain and itch. We present a data-driven, supervised spike sorting approach to improve the analysis of nociceptive discharges, identified through activity-dependent conduction velocity changes. Our method integrates three feature sets and applies machine learning models including one-class SVM, SVM, and XGBoost. Validation used experimentally derived ground truth data acquired by controlled electrical stimulation, allowing precise spike time-locking. Compared to Spike2 software, our approach achieved higher F1-scores and reduced false positives, indicating improved spike sorting. Although XGBoost achieved the highest median F1-scores, optimal performance was dependent on individual combinations of feature sets and models for each recording. In some recordings with many nerve fibers and a low signal-to-noise ratio, reliable sorting was not feasible. This highlights the necessity to determine sortability and optimal configures using a ground truth protocol for each recording. These findings represent an important step toward reliable analysis of nociceptive activity. The openly accessible framework supports analyzing pruritogen-induced and spontaneous activity in neuropathic pain patients, advancing tools for peripheral neural decoding. Biological sciences/Biological techniques/Electrophysiology/Extracellular recording Biological sciences/Biological techniques/Electrophysiology/Single channel recording Biological sciences/Computational biology and bioinformatics/Machine learning Health sciences/Neurology/Neurological disorders/Neuropathic pain microneurography spike trains machine learning pain nociceptor C-fibers Full Text Additional Declarations Competing interest reported. BN received consulting fees from Vertex. The other authors declare no competing interests. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor invited by journal 16 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 11 Jun, 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. 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-6874570","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538525334,"identity":"f97ce107-32f4-4cf5-ac31-bc5383b7592c","order_by":0,"name":"Alina Troglio","email":"","orcid":"","institution":"RWTH Aachen University","correspondingAuthor":false,"prefix":"","firstName":"Alina","middleName":"","lastName":"Troglio","suffix":""},{"id":538525335,"identity":"56b09803-6b82-4463-9941-9eba20f0a079","order_by":1,"name":"Andrea Fiebig","email":"","orcid":"","institution":"RWTH Aachen 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BN received consulting fees from Vertex. The other authors declare no competing interests.","formattedTitle":"A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microneurography, spike trains, machine learning, pain, nociceptor, C-fibers","lastPublishedDoi":"10.21203/rs.3.rs-6874570/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6874570/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalyzing temporal spike patterns in nociceptors recorded via microneurography is challenging due to the use of a single recording electrode, waveform variability, and high similarity of spike shapes across neurons limiting interpretation of sensory coding such as pain and itch. We present a data-driven, supervised spike sorting approach to improve the analysis of nociceptive discharges, identified through activity-dependent conduction velocity changes.\u003c/p\u003e\u003cp\u003eOur method integrates three feature sets and applies machine learning models including one-class SVM, SVM, and XGBoost. Validation used experimentally derived ground truth data acquired by controlled electrical stimulation, allowing precise spike time-locking. Compared to Spike2 software, our approach achieved higher F1-scores and reduced false positives, indicating improved spike sorting. Although XGBoost achieved the highest median F1-scores, optimal performance was dependent on individual combinations of feature sets and models for each recording. In some recordings with many nerve fibers and a low signal-to-noise ratio, reliable sorting was not feasible. This highlights the necessity to determine sortability and optimal configures using a ground truth protocol for each recording.\u003c/p\u003e\u003cp\u003eThese findings represent an important step toward reliable analysis of nociceptive activity. The openly accessible framework supports analyzing pruritogen-induced and spontaneous activity in neuropathic pain patients, advancing tools for peripheral neural decoding.\u003c/p\u003e","manuscriptTitle":"A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 10:47:40","doi":"10.21203/rs.3.rs-6874570/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T08:10:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T15:03:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T20:54:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T12:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37619971028442259489931020857843089973","date":"2025-10-29T18:11:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134502109602027068325592125779582836792","date":"2025-10-27T09:15:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157826110440827527079230321859296223972","date":"2025-10-26T05:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232489432950040368681992745507669557393","date":"2025-10-24T18:51:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153144709162539938499425160572663630769","date":"2025-10-22T19:06:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88160703437239617330491133174967720926","date":"2025-10-22T18:15:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T18:08:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-16T08:24:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T11:59:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T03:11:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-11T19:32:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a96dd721-bfee-4714-8548-5a11ef7c1013","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57278171,"name":"Biological sciences/Biological techniques/Electrophysiology/Extracellular recording"},{"id":57278172,"name":"Biological sciences/Biological techniques/Electrophysiology/Single channel recording"},{"id":57278173,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"},{"id":57278174,"name":"Health sciences/Neurology/Neurological disorders/Neuropathic pain"}],"tags":[],"updatedAt":"2026-03-16T16:16:05+00:00","versionOfRecord":{"articleIdentity":"rs-6874570","link":"https://doi.org/10.1038/s41598-026-41561-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-12 16:00:22","publishedOnDateReadable":"March 12th, 2026"},"versionCreatedAt":"2025-11-03 10:47:40","video":"","vorDoi":"10.1038/s41598-026-41561-9","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41561-9","workflowStages":[]},"version":"v1","identity":"rs-6874570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6874570","identity":"rs-6874570","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}