A Novel Dynamic Graph Architecture for Staging Parkinson’s Disease Progression Using Cerebrospinal Fluids Longitudinal Profiles

A Novel Dynamic Graph Architecture for Staging Parkinson’s Disease Progression Using Cerebrospinal Fluids Longitudinal Profiles | 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 Novel Dynamic Graph Architecture for Staging Parkinson’s Disease Progression Using Cerebrospinal Fluids Longitudinal Profiles Lubna Mahmoud Abuzohair, Hind Zantout, Md Azher Uddin, Marta Vallejo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9034342/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 Dynamic graph learning methods typically capture local structural information and short-range temporal dependencies at each time step. In this work, we introduce a dynamic graph learning architecture that generates time-step embeddings capturing both local structural context and progression-trajectory patterns for each node across an entire longitudinal sequence. The framework clusters fused embeddings that integrate (i) the global temporal trajectory of each node and (ii) its local spatial context at every graph snapshot to discover meaningful temporal patterns in longitudinal datasets. The proposed model was evaluated in the context of Parkinson’s disease (PD) progression using six years of longitudinal cerebrospinal fluid profiles from 24 patients. Visit-based graphs were constructed by representing patients as nodes enriched with peptide-abundance features, and by connecting patients with similar features profiles. A graph convolutional network captures visit-specific spatial relationships, while a sequential model learns global temporal representations. A fusion module integrates both sources of information to produce enriched node embeddings that reflect inter- and intra-patient molecular dynamics. Clustering of learned embeddings reveals four distinct stages of PD’s progression, supported by strong validity indices (Davies–Bouldin: 0.169; Calinski–Harabasz: 1264.24).Significant differences in motor severity (UPDRS 2 and UPDRS 3; p < 0.05) were observed in the groups, while the non-motor scores showed a more diffuse pattern (p = 0.11). Compared with existing features representation approaches, these findings demonstrate the potential of the proposed dynamic graph learning for data-driven disease staging and offer a generalizable framework to uncover latent temporal patterns in longitudinal datasets. Biological sciences/Computational biology and bioinformatics/Machine learning Physical sciences/Mathematics and computing/Computational science Learning Representation Dynamic Graphs Parkinson’s Disease deep learning unsupervised learning Full Text Additional Declarations There is NO Competing Interest. 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-9034342","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601809066,"identity":"e6e7b640-2549-4ff2-8cbe-75b648408543","order_by":0,"name":"Lubna Mahmoud Abuzohair","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPgbmAwcS/9gkQPkJeNRCARsDW+KDhw1pJGnhMTZ82HCYFC3sB8wkEneczzM4wPzwA2NbGhFaeBLSJBLP3C42OMBmLMHYlkOMwxKOSSSw3U7cdoDBjIGxrYIILfwP24BazgG1sH8jUotEMrNBYtsBoBYekC3EOEziGeODhDPJifsP8xRLJJwjwvv8/PkfDv6osEuc2d6+8cOHsmTCWhCAmYGoWBkFo2AUjIJRQAwAAAFjOrA+ArRhAAAAAElFTkSuQmCC","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":true,"prefix":"","firstName":"Lubna","middleName":"Mahmoud","lastName":"Abuzohair","suffix":""},{"id":601809067,"identity":"34808079-5e6e-4096-9163-bb1d578297fc","order_by":1,"name":"Hind Zantout","email":"","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":false,"prefix":"","firstName":"Hind","middleName":"","lastName":"Zantout","suffix":""},{"id":601809068,"identity":"1eb9f08f-d6d4-4b00-9302-ada93d161cb1","order_by":2,"name":"Md Azher Uddin","email":"","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Azher","lastName":"Uddin","suffix":""},{"id":601809069,"identity":"fce53f28-4089-497f-ab97-6900ba2b398e","order_by":3,"name":"Marta Vallejo","email":"","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Vallejo","suffix":""},{"id":601809070,"identity":"d004ae67-148b-48f0-9a2f-cdae42e0229d","order_by":4,"name":"John Woodward","email":"","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Woodward","suffix":""}],"badges":[],"createdAt":"2026-03-04 23:56:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9034342/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9034342/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104779451,"identity":"8c1263f3-dc2a-460a-8d5e-6859f2f947f6","added_by":"auto","created_at":"2026-03-17 07:40:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":581366,"visible":true,"origin":"","legend":"Article File","description":"","filename":"JML3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9034342/v1_covered_23d6139f-4a51-4f96-ab08-6b5421c7720c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eA Novel Dynamic Graph Architecture for Staging Parkinson’s Disease Progression Using Cerebrospinal Fluids Longitudinal Profiles\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"Learning Representation, Dynamic Graphs, Parkinson’s Disease, deep learning, unsupervised learning","lastPublishedDoi":"10.21203/rs.3.rs-9034342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9034342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDynamic graph learning methods typically capture local structural information and short-range temporal dependencies at each time step. In this work, we introduce a dynamic graph learning architecture that generates time-step embeddings capturing both local structural context and progression-trajectory patterns for each node across an entire longitudinal sequence. The framework clusters fused embeddings that integrate (i) the global temporal trajectory of each node and (ii) its local spatial context at every graph snapshot to discover meaningful temporal patterns in longitudinal datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed model was evaluated in the context of Parkinson’s disease (PD) progression using six years of longitudinal cerebrospinal fluid profiles from 24 patients. Visit-based graphs were constructed by representing patients as nodes enriched with peptide-abundance features, and by connecting patients with simi\u0002lar features profiles. A graph convolutional network captures visit-specific spatial relationships, while a sequential model learns global temporal representations. 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