Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter

Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter | 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 Research Article Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter Jixuan Wang, Bin Deng, Jiang Wang, Xiang Lei, Chen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3331364/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The brain rhythm is strongly associated with the brain function. Alzheimer’s disease (AD) is majorly reflected by the brain rhythm switching from the alpha band (9–12 Hz) to the theta band (4–8 Hz), accompanying with the loss of brain function. However, extracting the implicating intrinsic characteristic variations of the brain network by utilizing the Electroencephalogram (EEG) information is extremely difficult. Kaman observer, as an effective Bayesian technique, can provide a visualization service for probing the intrinsic characteristics underlying the pathological theta oscillations. This work first establishes an excitation-inhibitory neural network model and explores the role of the proportion of the inhibitory neurons and inhibitory synapses in the pathological theta oscillation. The results indicate that the apoptosis of inhibitory neurons and accompanied loss of inhibitory synaptic weight are the main neural bases of the frequency decrease of neural oscillation. Then, we further explore the intrinsic spiking characteristic by considering spike frequency adaptation (SFA) to the inhibitory neurons. The results show that the SFA reduces the firing rate of neurons, which facilitates the theta rhythm. The enhancement of SFA current by increasing time constant of its gating variable can further decrease the theta frequency from 7 Hz to 4 Hz. Finally, for this high-dimensional nonlinear excitation-inhibitory neural network model, cubature Kalman filter (CKF) is employed to estimate the above potential variations from the noisy EEG information. The observation results show that both the proportion of inhibitory neurons and the inhibitory SFA current present descending trends as the degree of AD increases. Collectively, the generation of AD state is speculated to rely on multi-origin inhibitory intrinsic characteristics: a significant attenuation on the proportion of inhibitory neurons, synaptic weight and SFA current. The observation result by CKF from EEG verifies the simulation results from the model. We investigate the parameter effects from both the forward model simulation and the inverse estimation process of network parameters using EEG data. This work enhances the understanding of the role of inhibitory intrinsic characteristics on pathological theta oscillation and provides an effective method to decode the dynamics underlying the neural activities. Excitation-inhibitory neural network model Theta rhythm Inhibitory origins Electroencephalogram Cubature Kalman filters Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Reject, do not transfer 26 Nov, 2023 Reviewers agreed at journal 19 Sep, 2023 Reviewers invited by journal 18 Sep, 2023 Editor assigned by journal 11 Sep, 2023 First submitted to journal 06 Sep, 2023 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-3331364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":234538682,"identity":"f03d66c5-c3e6-45cd-995a-49cf489edbf1","order_by":0,"name":"Jixuan Wang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jixuan","middleName":"","lastName":"Wang","suffix":""},{"id":234538683,"identity":"37885e0b-62d8-4802-aed1-129de2e74add","order_by":1,"name":"Bin Deng","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Deng","suffix":""},{"id":234538684,"identity":"ed3657eb-0171-44f0-b2ed-dd1e7d140c19","order_by":2,"name":"Jiang Wang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Wang","suffix":""},{"id":234538685,"identity":"c8a99610-c57e-4702-8166-b854783d1636","order_by":3,"name":"Xiang Lei","email":"","orcid":"","institution":"Tianjin Huanhu Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Lei","suffix":""},{"id":234538686,"identity":"3cc9a4c6-60eb-42c4-98b5-590bac27e8d2","order_by":4,"name":"Chen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACPmYGBiBiqG+TP3yAQQIkdICAFjaoFsY+CbbEBuK0MEC1zJPgMWxgIEoLO4/h54KKO8xs0j3fH1i2Mcjx3Uhg/FyA12E8xtIzzjxjY5M5u7FBso3BWPJGArP0DPxazJh52w7zsDHkgrUkbriRABQkqOXfYQk2hpyHIC31RGppOGzAJpHDCNKSYEBYC1uxNM+xwwlsPMcMZ0ickzCceeZhszQ+Lfz8hzd+5qk5nCDf3vzgs0SZjTzf8eSDn/FpQQHMEuD4Z2wgVgNQ7Qfi1Y6CUTAKRsEIAgBBoUDK/6oWaAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1635-3479","institution":"Tianjin University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-09-06 13:23:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3331364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3331364/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43627896,"identity":"7d70770b-a9cd-412f-85fa-da49b1e53df9","added_by":"auto","created_at":"2023-09-25 12:45:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":957180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3331364/v1_covered_3ffc97c5-9e08-4906-b3b5-783709fb5271.pdf"}],"financialInterests":"","formattedTitle":"Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Excitation-inhibitory neural network model, Theta rhythm, Inhibitory origins, Electroencephalogram, Cubature Kalman filters","lastPublishedDoi":"10.21203/rs.3.rs-3331364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3331364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe brain rhythm is strongly associated with the brain function. Alzheimer\u0026rsquo;s disease (AD) is majorly reflected by the brain rhythm switching from the alpha band (9\u0026ndash;12 Hz) to the theta band (4\u0026ndash;8 Hz), accompanying with the loss of brain function. However, extracting the implicating intrinsic characteristic variations of the brain network by utilizing the Electroencephalogram (EEG) information is extremely difficult. Kaman observer, as an effective Bayesian technique, can provide a visualization service for probing the intrinsic characteristics underlying the pathological theta oscillations. This work first establishes an excitation-inhibitory neural network model and explores the role of the proportion of the inhibitory neurons and inhibitory synapses in the pathological theta oscillation. The results indicate that the apoptosis of inhibitory neurons and accompanied loss of inhibitory synaptic weight are the main neural bases of the frequency decrease of neural oscillation. Then, we further explore the intrinsic spiking characteristic by considering spike frequency adaptation (SFA) to the inhibitory neurons. The results show that the SFA reduces the firing rate of neurons, which facilitates the theta rhythm. The enhancement of SFA current by increasing time constant of its gating variable can further decrease the theta frequency from 7 Hz to 4 Hz. Finally, for this high-dimensional nonlinear excitation-inhibitory neural network model, cubature Kalman filter (CKF) is employed to estimate the above potential variations from the noisy EEG information. The observation results show that both the proportion of inhibitory neurons and the inhibitory SFA current present descending trends as the degree of AD increases. Collectively, the generation of AD state is speculated to rely on multi-origin inhibitory intrinsic characteristics: a significant attenuation on the proportion of inhibitory neurons, synaptic weight and SFA current. The observation result by CKF from EEG verifies the simulation results from the model. We investigate the parameter effects from both the forward model simulation and the inverse estimation process of network parameters using EEG data. This work enhances the understanding of the role of inhibitory intrinsic characteristics on pathological theta oscillation and provides an effective method to decode the dynamics underlying the neural activities.\u003c/p\u003e","manuscriptTitle":"Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-25 12:37:18","doi":"10.21203/rs.3.rs-3331364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Reject, do not transfer","date":"2023-11-27T04:12:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-09-19T04:49:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-19T00:56:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-11T22:24:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nonlinear Dynamics","date":"2023-09-06T09:23:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c07c0845-6d12-4ea3-99c9-42a08e3d7ed5","owner":[],"postedDate":"September 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-09-25T12:37:19+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-25 12:37:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3331364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3331364","identity":"rs-3331364","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}