Early identification of Parkinson’s disease from EEG-based functional connectivity matrices

Early identification of Parkinson’s disease from EEG-based functional connectivity matrices | 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 Early identification of Parkinson’s disease from EEG-based functional connectivity matrices Tanvir Hasib, Parameswari Shunmugam, Kannan Ramakrishnan, Vijayakumar Vengadasalam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7596737/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 Parkinson’s disease (PD) disrupts large-scale brain networks, but most current diagnostics—reliant on late-appearing motor symptoms—miss the window for early intervention. While EEG captures neural dynamics, traditional connectivity metrics (e.g., linear correlation, coherence) overlook nonlinear dependencies critical to PD pathology. The goal is to detect early brain network changes linked to Parkinson’s disease by representing mutual information (MI) connectivity data as spatial images. Deep learning is then used subsequently for classifying PD and healthy controls, while using explainable AI to identify the possible electrodes underlying brain neural connectivity. Resting-state EEG recording data from PD patients and matched controls was transformed into whole-brain MI connectivity matrices, treated as 2D adjacency matrix images. The convolutional neural network (CNN) classified the matrices. Gradient-weighted Class Activation Mapping (Grad-CAM++) highlighted the key connections that drove the network’s decisions. These maps also visualized how the topology shifted. The CNN achieved strong classification performance, showing that MI matrices capture signatures of Parkinson’s disease. Grad-CAM localized decisions to pathological connections in frontal-central-temporal circuits core motor-execution networks degenerating earliest in PD. Further analysis validated compensatory strengthening of short-range intrahemispheric connections alongside degraded long-range integration, aligning with PD’s "network efficiency collapse" hypothesis. By exposing presymptomatic network reorganization via MI matrices and spatial deep learning, we offer an EEG-based signature for early detection. Biological sciences/Computational biology and bioinformatics Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf 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-7596737","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":530975109,"identity":"8646fada-08d7-492b-9f06-a2ce9ab5e7c4","order_by":0,"name":"Tanvir Hasib","email":"","orcid":"","institution":"Multimedia University","correspondingAuthor":false,"prefix":"","firstName":"Tanvir","middleName":"","lastName":"Hasib","suffix":""},{"id":530975110,"identity":"a7cd5b7e-02d9-4199-b764-77280357a0ef","order_by":1,"name":"Parameswari Shunmugam","email":"","orcid":"","institution":"Sultan Salahuddin Abdul Aziz Shah 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While EEG captures neural dynamics, traditional connectivity metrics (e.g., linear correlation, coherence) overlook nonlinear dependencies critical to PD pathology. The goal is to detect early brain network changes linked to Parkinson’s disease by representing mutual information (MI) connectivity data as spatial images. Deep learning is then used subsequently for classifying PD and healthy controls, while using explainable AI to identify the possible electrodes underlying brain neural connectivity. Resting-state EEG recording data from PD patients and matched controls was transformed into whole-brain MI connectivity matrices, treated as 2D adjacency matrix images. The convolutional neural network (CNN) classified the matrices. Gradient-weighted Class Activation Mapping (Grad-CAM++) highlighted the key connections that drove the network’s decisions. These maps also visualized how the topology shifted. 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