FDG-PET Detects Amygdala-Hippocampal Connection Volume and Heterogeneity in Alzheimer's Disease Progression

FDG-PET Detects Amygdala-Hippocampal Connection Volume and Heterogeneity in Alzheimer's Disease Progression | 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 FDG-PET Detects Amygdala-Hippocampal Connection Volume and Heterogeneity in Alzheimer's Disease Progression Ramin Rasi, Albert Guvenis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252826/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 Objective This study delves into the potential of radiomics to identify the Alzheimer's disease (AD) stages and monitor its progression using FDG PET images. The particular objective is to explore the potential of tracking progression using a reduced and meaningful set of imaging biomarkers at the border of the Hippocampus and Amygdala. Method Our study utilized 18F-FDG PET scans from 513 participants spanning three stages of Alzheimer’s disease (AD) from the ADNI database. The hippocampus, amygdala, and entorhinal cortex consistently emerged as key regions of interest. To further investigate their interconnectivity, we defined the hippocampus-amygdala connecting region using a distance transform approach. Next, we systematically evaluated eight feature selection techniques in combination with six classification models to determine the most effective predictive framework. Finally, we conducted a Pearson correlation analysis to pinpoint the most significant features for AD classification. Results The connectivity area between the hippocampus and amygdala demonstrated superior efficacy for diagnosing AD. Two features shape_MeshVolume_Right, gldm_SmallDependenceLowGrayLevelEmphasis_left, glrlm_ShortRunLowGrayLevelEmphasis_left for this single region, could predict AD versus Control Normal (CN) individuals with ROC AUC = 0.91, and two features predict MCI versus AD with ROC AUC = 0.80, and a feature shape_LeastAxisLength_left, glszm_LargeAreaEmphasis_left for CN versus MCI with ROC AUC = 0.69. The features' mean values were able to demonstrate the incremental deterioration between groups of consecutive AD stages with statistical significance (p < 0.05). Conclusions In this study, we identify significant radiomic features within the hippocampus-amygdala connecting region and their utility in tracking AD progression. By limiting these specific biomarkers, we offer a simple but clinically relevant approach to AD diagnosis and monitoring and demonstrate the role of radiomics in early detection and intervention. Alzheimer's Disease (AD) Mild Cognitive Impairment (MCI) Machine Learning Radiomics Full Text Additional Declarations No competing interests reported. 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-6252826","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433415026,"identity":"78b1312a-42e9-4dba-a87b-ab83ebb7f255","order_by":0,"name":"Ramin Rasi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBAC+wYGhgMgBht7AwMzUVoMDsC08BwgQQsESCQQq+X46cTDhXvs7Pkk3xh+LqiwYeBv707Aq8W+J3fD4RnPkhPbpHOMpWecSWOQOHN2A14tdgxALTwHmBPYpHMMpHnbDjMYSOTi12LM/xakpd6eTfKM8W+itBjOANtymLFNgseMOFsMboBtOZ7YxpNWZs1zJo2HoF8Mzudu/sxzoNpevv3w5ts8FTZy/O29+LUgAQ4DEMlDrHIQYH9AiupRMApGwSgYQQAAYvpHyUQf0EIAAAAASUVORK5CYII=","orcid":"","institution":"Boğaziçi University","correspondingAuthor":true,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Rasi","suffix":""},{"id":433415027,"identity":"182c38b0-e71d-4b5b-a2f5-9c231eed7155","order_by":1,"name":"Albert Guvenis","email":"","orcid":"","institution":"Boğaziçi University","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Guvenis","suffix":""}],"badges":[],"createdAt":"2025-03-18 11:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6252826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252826/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80647188,"identity":"8d0da179-0f2b-4f10-9a7a-661121026638","added_by":"auto","created_at":"2025-04-15 14:17:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158628,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript03182025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252826/v1_covered_c6272653-7253-471b-93da-96f147896a30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FDG-PET Detects Amygdala-Hippocampal Connection Volume and Heterogeneity in Alzheimer's Disease Progression","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Machine Learning, Radiomics","lastPublishedDoi":"10.21203/rs.3.rs-6252826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study delves into the potential of radiomics to identify the Alzheimer's disease (AD) stages and monitor its progression using FDG PET images. The particular objective is to explore the potential of tracking progression using a reduced and meaningful set of imaging biomarkers at the border of the Hippocampus and Amygdala.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eOur study utilized 18F-FDG PET scans from 513 participants spanning three stages of Alzheimer\u0026rsquo;s disease (AD) from the ADNI database. The hippocampus, amygdala, and entorhinal cortex consistently emerged as key regions of interest. To further investigate their interconnectivity, we defined the hippocampus-amygdala connecting region using a distance transform approach. Next, we systematically evaluated eight feature selection techniques in combination with six classification models to determine the most effective predictive framework. Finally, we conducted a Pearson correlation analysis to pinpoint the most significant features for AD classification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe connectivity area between the hippocampus and amygdala demonstrated superior efficacy for diagnosing AD. Two features shape_MeshVolume_Right, gldm_SmallDependenceLowGrayLevelEmphasis_left, glrlm_ShortRunLowGrayLevelEmphasis_left for this single region, could predict AD versus Control Normal (CN) individuals with ROC AUC\u0026thinsp;=\u0026thinsp;0.91, and two features predict MCI versus AD with ROC AUC\u0026thinsp;=\u0026thinsp;0.80, and a feature shape_LeastAxisLength_left, glszm_LargeAreaEmphasis_left for CN versus MCI with ROC AUC\u0026thinsp;=\u0026thinsp;0.69. The features' mean values were able to demonstrate the incremental deterioration between groups of consecutive AD stages with statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this study, we identify significant radiomic features within the hippocampus-amygdala connecting region and their utility in tracking AD progression. By limiting these specific biomarkers, we offer a simple but clinically relevant approach to AD diagnosis and monitoring and demonstrate the role of radiomics in early detection and intervention.\u003c/p\u003e","manuscriptTitle":"FDG-PET Detects Amygdala-Hippocampal Connection Volume and Heterogeneity in Alzheimer's Disease Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 18:25:16","doi":"10.21203/rs.3.rs-6252826/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":"7abe790a-aadf-4076-8a66-bb35398b0862","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-15T14:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 18:25:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6252826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252826","identity":"rs-6252826","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}