{"paper_id":"12f25aca-e331-483c-a364-dc29018b203a","body_text":"Fractional Neural Field Modeling of Consciousness Transitions under Anesthesia: Multi-Scale Integration from Molecular Mechanisms to EEG Signatures | 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 Fractional Neural Field Modeling of Consciousness Transitions under Anesthesia: Multi-Scale Integration from Molecular Mechanisms to EEG Signatures Rex Igwe, Divine Allen, Goodness Nwanna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7706678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Consciousness arises from coordinated neural network activity but is exquisitely sensitive to molecular perturbations, such as those induced by anesthetic agents. Predicting how molecular-level pharmacology propagates through networks to produce EEG-level signatures remains a central challenge in neuroscience. Here, we introduce a fractional-order neural field model that rigorously integrates receptor dynamics ( \\(\\:{GABA}_{A}\\) , NMDA, K2P channels), network connectivity, and fractional temporal derivatives to capture memory-dependent neural processes. The fractional order \\(\\:\\alpha\\:\\:\\in\\:\\:\\left(\\text{0,1}\\right)\\) quantitatively modulates transient growth, spectral EEG slopes, ERP amplitudes, and gray-zone dynamics near bifurcation thresholds. We establish well-posedness, spectral stability, and explicit bifurcation criteria, including fractional generalizations of classical pitchfork and Hopf transitions. Using synthetic experimental EEG datasets designed to replicate canonical empirical features ( \\(\\:\\frac{1}{{f}^{\\beta\\:}}\\:\\:\\) scaling, alpha oscillations, ERP pulses), we validate the model through power spectral density comparisons, ERP waveform analyses, and stability domain mapping. Numerical simulations reproduce classical neural field phenomena while predicting novel testable behaviors: prolonged transients under ketamine, subexponential drifts in gray-zone anesthesia, and \\(\\:\\alpha\\:-\\) dependent bifurcation shifts. By bridging molecular pharmacology, network dynamics, and EEG biomarkers in a reproducible simulation framework, this work provides a unifying, predictive model for consciousness transitions, with direct implications for anesthetic management and the mechanistic understanding of neural memory effects. Biological sciences/Biophysics Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience Fractional-order neural fields Consciousness transitions Anesthesia modulation EEG biomarkers Bifurcation analysis Memory-dependent neural dynamics Full Text Additional Declarations No competing interests reported. Supplementary Files R.A.G.zip Cite Share Download PDF Status: Posted 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. 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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-7706678\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":530458737,\"identity\":\"330ff67e-97a6-4992-b385-157a78ee1c8b\",\"order_by\":0,\"name\":\"Rex 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Predicting how molecular-level pharmacology propagates through networks to produce EEG-level signatures remains a central challenge in neuroscience. Here, we introduce a fractional-order neural field model that rigorously integrates receptor dynamics (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{GABA}_{A}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, NMDA, K2P channels), network connectivity, and fractional temporal derivatives to capture memory-dependent neural processes. The fractional order \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:\\\\:\\\\in\\\\:\\\\:\\\\left(\\\\text{0,1}\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e quantitatively modulates transient growth, spectral EEG slopes, ERP amplitudes, and gray-zone dynamics near bifurcation thresholds. We establish well-posedness, spectral stability, and explicit bifurcation criteria, including fractional generalizations of classical pitchfork and Hopf transitions.\\u003c/p\\u003e\\u003cp\\u003eUsing synthetic experimental EEG datasets designed to replicate canonical empirical features (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\frac{1}{{f}^{\\\\beta\\\\:}}\\\\:\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003escaling, alpha oscillations, ERP pulses), we validate the model through power spectral density comparisons, ERP waveform analyses, and stability domain mapping. Numerical simulations reproduce classical neural field phenomena while predicting novel testable behaviors: prolonged transients under ketamine, subexponential drifts in gray-zone anesthesia, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:-\\\\)\\u003c/span\\u003e\\u003c/span\\u003edependent bifurcation shifts. By bridging molecular pharmacology, network dynamics, and EEG biomarkers in a reproducible simulation framework, this work provides a unifying, predictive model for consciousness transitions, with direct implications for anesthetic management and the mechanistic understanding of neural memory effects.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Fractional Neural Field Modeling of Consciousness Transitions under Anesthesia: Multi-Scale Integration from Molecular Mechanisms to EEG Signatures\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-17 04:30:04\",\"doi\":\"10.21203/rs.3.rs-7706678/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"e9ebf10c-e94f-46f3-b3aa-e22b5ee5d390\",\"owner\":[],\"postedDate\":\"October 17th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":56390117,\"name\":\"Biological sciences/Biophysics\"},{\"id\":56390118,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":56390119,\"name\":\"Biological sciences/Neuroscience\"}],\"tags\":[],\"updatedAt\":\"2025-11-11T18:38:30+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-17 04:30:04\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7706678\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7706678\",\"identity\":\"rs-7706678\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}