Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects

Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects | 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 Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects Pham Minh Tuan, Tatiana Horowitz, Mouloud Adel, Julien Wojak, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6560851/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 Purpose: Connectivity analyses of fluorodeoxyglucose positron emission tomography (FDG-PET) static images provides a valuable means of investigating brain network organization by capturing metabolic activity at rest. Graph theory is emergently applied to model these networks; however, the choice of graph construction method can significantly impact analytical outcomes. Methods: In this study, we systematically evaluate and compare five methods for building individual graphs from FDG-PET images focusing on healthy control subjects. We assess five methods, categorized into mean-based graphs and probability density function (PDF)-based graphs, using two criteria: structural similarity between individual and group-level graphs, and their hub topology structure analysis. Results: Our findings indicate that the Effect Size-based (ES) method best preserves group-level graph structure, achieving 98.9% similarity for the averaged graph while also maintaining around 84% similarity for individual graphs. Among PDF-based approaches, the Wasserstein (WA) method, with its adaptability in PDF-based settings, provides the highest similarity across both averaged (82.5%) and individual (79.1%) graphs, with its adaptive in PDF-settings, making it the most effective for multi-scale network analysis. Meanwhile, Dynamic Time Warping (DTW) captures the highest individual variability, as reflected by its largest variation among individual graphs (11.5%). Conclusion: This analysis highlights the unique strengths and limitations of each method, emphasizing the critical importance of careful method selection tailored to specific research objectives. Additionally, our study suggests a framework for selecting the appropriate methods, with implications for further both research and clinical applications. Healthy Control FDG-PET ADNI Connectivity Individual Graph Construction Dynamic Time Warping Effect Size Kullback-Leibler divergence Wasserstein Full Text Additional Declarations The authors declare no competing interests. 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-6560851","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449974302,"identity":"1f79346b-1398-4ac4-9d2a-b9925378176d","order_by":0,"name":"Pham Minh Tuan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pham","middleName":"Minh","lastName":"Tuan","suffix":""},{"id":449974303,"identity":"b6e55b8a-e9f7-411f-b238-4b730f2b69d5","order_by":1,"name":"Tatiana Horowitz","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tatiana","middleName":"","lastName":"Horowitz","suffix":""},{"id":449974304,"identity":"0621558d-dc4a-4840-8ca3-625cbcf28351","order_by":2,"name":"Mouloud Adel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mouloud","middleName":"","lastName":"Adel","suffix":""},{"id":449974305,"identity":"b07f761b-16e4-4d60-8c6f-8b662a60d147","order_by":3,"name":"Julien Wojak","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Wojak","suffix":""},{"id":449974306,"identity":"262ff977-8f68-48fa-9de0-83e2181039f9","order_by":4,"name":"Nguyen Linh Trung","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nguyen","middleName":"Linh","lastName":"Trung","suffix":""},{"id":449974307,"identity":"97e46bdf-cba0-41df-9466-563e34080ddf","order_by":5,"name":"Eric Guedj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3NoQvCQBTH8XcIWoarJwP9F54YFfxXHIIWw6LNiWASo+h/ocVkePLAdHZhRYt5SUQW3KmgZZtR8L7luOM+/ABMpp9M+M+TmADw9dj/juxab6K+WiML35c0Yjs8DOHG5eJWXRzPY7BnCmG7SSalqTuSYsq1Eu3XzhwZZNBDoHMyQSXGICbsLjWxsDPwY5ILKZk0X2SwJHXWBCqPlRSCVkzgyi0klY9JAzCLSCVG0vW71QXtanVNqkHHozRiTwqnMIzqleKBT4EVSSgH7dUxjTxyx3rv41cWiIv0np/9z2Qymf6zO39vWC/zi1aaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1912-6132","institution":"Aix-Marseille University","correspondingAuthor":true,"prefix":"","firstName":"Eric","middleName":"","lastName":"Guedj","suffix":""}],"badges":[],"createdAt":"2025-04-30 04:04:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6560851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6560851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81794379,"identity":"3d10395b-b219-4efa-b3e5-be141ae6d907","added_by":"auto","created_at":"2025-05-02 02:55:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1306024,"visible":true,"origin":"","legend":"","description":"","filename":"EJNMMIHCGraphmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6560851/v1_covered_2d094e8f-9dcf-4537-a966-c7fbc5bb932d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects\u003c/strong\u003e\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":"Healthy Control, FDG-PET, ADNI, Connectivity, Individual Graph Construction, Dynamic Time Warping, Effect Size, Kullback-Leibler divergence, Wasserstein","lastPublishedDoi":"10.21203/rs.3.rs-6560851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6560851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eConnectivity analyses of fluorodeoxyglucose positron emission tomography (FDG-PET) static images provides a valuable means of investigating brain network organization by capturing metabolic activity at rest. Graph theory is emergently applied to model these networks; however, the choice of graph construction method can significantly impact analytical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In this study, we systematically evaluate and compare five methods for building individual graphs from FDG-PET images focusing on healthy control subjects. We assess five methods, categorized into mean-based graphs and probability density function (PDF)-based graphs, using two criteria: structural similarity between individual and group-level graphs, and their hub topology structure analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our findings indicate that the Effect Size-based (ES) method best preserves group-level graph structure, achieving 98.9% similarity for the averaged graph while also maintaining around 84% similarity for individual graphs. Among PDF-based approaches, the Wasserstein (WA) method, with its adaptability in PDF-based settings, provides the highest similarity across both averaged (82.5%) and individual (79.1%) graphs, with its adaptive in PDF-settings, making it the most effective for multi-scale network analysis. Meanwhile, Dynamic Time Warping (DTW) captures the highest individual variability, as reflected by its largest variation among individual graphs (11.5%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This analysis highlights the unique strengths and limitations of each method, emphasizing the critical importance of careful method selection tailored to specific research objectives. Additionally, our study suggests a framework for selecting the appropriate methods, with implications for further both research and clinical applications.\u003c/p\u003e","manuscriptTitle":"Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 02:47:12","doi":"10.21203/rs.3.rs-6560851/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":"deb12f04-e6be-4cc5-968c-fde41bd67e4e","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-02T02:47:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-02 02:47:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6560851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6560851","identity":"rs-6560851","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}