Cross-sectional and longitudinal trajectories of structural connectome aging diverge: a diffusion MRI study across the adult lifespan

Cross-sectional and longitudinal trajectories of structural connectome aging diverge: a diffusion MRI study across the adult lifespan | 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 Cross-sectional and longitudinal trajectories of structural connectome aging diverge: a diffusion MRI study across the adult lifespan Saeid Rezaei Afshar, G. Reza Jafari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9676341/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 Cross-sectional studies of structural-connectome organization in aging report inconsistent findings, and it is not always clear whether the inconsistency reflects pipeline differences or limits of cross-sectional design. We addressed this directly using diffusion-MRI data from 194 healthy adults aged 21–89 years (377 scans) drawn from the Dallas Lifespan Brain Study, of whom 115 had longitudinal follow-up. Whole-brain structural connectomes were reconstructed using constrained spherical deconvolution tractography over the 116-region Automated Anatomical Labeling atlas, and network robustness was quantified through threshold and attack simulations. We report cross-sectional age associations on baseline-only data with FDR correction, bootstrap mediation by fractional anisotropy, paired-difference and linear mixed-effects models for within-subject change, ICC(3,1) test–retest reliability, an explicit returner-versus-non-returner attrition test, and machine-learning age classification with permutation testing. Cross-sectionally, FA showed steep decline with age (r = -0.629, pFDR < 0.001), while topology metrics showed only modest, mostly positive associations (clustering: r = +0.246, pFDR = 0.003; threshold h50: r = +0.138, n.s.). Bootstrap mediation showed no reliable indirect effect through FA (a×b = +0.074, 95% CI [-0.003, +0.150]). Within the same individuals, longitudinal tracking revealed significant decline in robustness (t = -2.94, p = 0.004), threshold AUC, clustering, density, mean strength, and number of connections (all pFDR < 0.05); FA itself did not change reliably within subjects. ICCs of all network metrics were poor (< 0.30) and returners did not differ from non-returners on any baseline measure. Logistic regression separated younger (<50 yr) from older (≥50 yr) adults at 77.7% accuracy (AUC = 0.85; permutation p = 0.001). Cross-sectional and longitudinal designs yield qualitatively different conclusions about the aging connectome. Computational Neuroscience Biophysics structural connectome aging diffusion MRI tractography longitudinal network robustness fractional anisotropy reliability brain age Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supplementary.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. 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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-9676341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638029843,"identity":"6476809a-3452-4a75-9294-e56d885c74a9","order_by":0,"name":"Saeid Rezaei Afshar","email":"","orcid":"https://orcid.org/0009-0004-0847-8823","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Saeid","middleName":"Rezaei","lastName":"Afshar","suffix":""},{"id":638029844,"identity":"1ef76fb3-65f3-4605-a93c-321db46820a4","order_by":1,"name":"G. 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We addressed this directly using diffusion-MRI data from 194 healthy adults aged 21–89 years (377 scans) drawn from the Dallas Lifespan Brain Study, of whom 115 had longitudinal follow-up. Whole-brain structural connectomes were reconstructed using constrained spherical deconvolution tractography over the 116-region Automated Anatomical Labeling atlas, and network robustness was quantified through threshold and attack simulations. We report cross-sectional age associations on baseline-only data with FDR correction, bootstrap mediation by fractional anisotropy, paired-difference and linear mixed-effects models for within-subject change, ICC(3,1) test–retest reliability, an explicit returner-versus-non-returner attrition test, and machine-learning age classification with permutation testing. Cross-sectionally, FA showed steep decline with age (r = -0.629, pFDR \u0026lt; 0.001), while topology metrics showed only modest, mostly positive associations (clustering: r = +0.246, pFDR = 0.003; threshold h50: r = +0.138, n.s.). Bootstrap mediation showed no reliable indirect effect through FA (a×b = +0.074, 95% CI [-0.003, +0.150]). Within the same individuals, longitudinal tracking revealed significant decline in robustness (t = -2.94, p = 0.004), threshold AUC, clustering, density, mean strength, and number of connections (all pFDR \u0026lt; 0.05); FA itself did not change reliably within subjects. ICCs of all network metrics were poor (\u0026lt; 0.30) and returners did not differ from non-returners on any baseline measure. Logistic regression separated younger (\u0026lt;50 yr) from older (≥50 yr) adults at 77.7% accuracy (AUC = 0.85; permutation p = 0.001). 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