Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis

Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis | 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 Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis Caroline L. Alves, Loriz Francisco Sallum, Francisco Aparecido Rodrigues, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4095364/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 Objectives: Parkinson’s disease is a multifactorial neurodegenerative disorder whose progression remains complex despiteextensive research. Our study presents an innovative approach to understanding PD progression through detailed analysis ofelectroencephalography signals. By segmenting patients based on disease duration, we uncover unique neural connectivitypatterns corresponding to different duration of PD development. Methods: Employing advanced machine learning techniques,our methodology achieves exceptional accuracy rates in binary classification tasks compared to prior literature. Integrationof Shapley Additive Explanations values enhances model interpretability, revealing critical brain regions and connectivitypatterns implicated in PD pathophysiology. Results: Coherence emerges as a crucial metric for capturing synchronizedsignal behaviors, aiding in discriminating PD patients from controls. Further, our analysis suggests a continuum of neuralconnectivity patterns across disease duration, with early-stage PD resembling healthy brain function and advanced durationexhibiting distinct features indicative of disease progression. Conclusions: These findings deepen our understanding of PDpathogenesis, laying the groundwork for personalized diagnostic and therapeutic approaches tailored to different diseaseduration. Our study contributes significant insights into the complex interplay between neural dynamics, disease progression,and age-related changes in PD, offering potential for future research and clinical applications Biological sciences/Neuroscience Physical sciences/Mathematics and computing/Applied mathematics Full Text Additional Declarations No competing interests reported. Supplementary Files matricesfinalscientificreports.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. 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-4095364","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283607066,"identity":"7f4f5f68-a635-4849-b9f9-54528ba48165","order_by":0,"name":"Caroline L. 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Our study presents an innovative approach to understanding PD progression through detailed analysis ofelectroencephalography signals. By segmenting patients based on disease duration, we uncover unique neural connectivitypatterns corresponding to different duration of PD development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Employing advanced machine learning techniques,our methodology achieves exceptional accuracy rates in binary classification tasks compared to prior literature. Integrationof Shapley Additive Explanations values enhances model interpretability, revealing critical brain regions and connectivitypatterns implicated in PD pathophysiology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Coherence emerges as a crucial metric for capturing synchronizedsignal behaviors, aiding in discriminating PD patients from controls. Further, our analysis suggests a continuum of neuralconnectivity patterns across disease duration, with early-stage PD resembling healthy brain function and advanced durationexhibiting distinct features indicative of disease progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: These findings deepen our understanding of PDpathogenesis, laying the groundwork for personalized diagnostic and therapeutic approaches tailored to different diseaseduration. Our study contributes significant insights into the complex interplay between neural dynamics, disease progression,and age-related changes in PD, offering potential for future research and clinical applications\u003c/p\u003e","manuscriptTitle":"Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 16:16:17","doi":"10.21203/rs.3.rs-4095364/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"039dcc2d-a954-4722-a0a6-2485ea1ca912","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29853878,"name":"Biological sciences/Neuroscience"},{"id":29853879,"name":"Physical sciences/Mathematics and computing/Applied mathematics"}],"tags":[],"updatedAt":"2024-06-12T19:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-26 16:16:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4095364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4095364","identity":"rs-4095364","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}