{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T06:51:37Z","timestamp":1782370297465,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,22]]},"group-title":"Neuroscience","reference-count":39,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Normal brain aging and Alzheimer\u2019s disease both involve progressive structural brain alterations, making it challenging to distinguish pathological neurodegeneration from normative aging-related atrophy. This study investigated whether Alzheimer\u2019s disease exhibits radiomic patterns that mimic, diverge from, or selectively perturb age-associated structural brain changes. T1-weighted magnetic resonance imaging scans from the Alzheimer\u2019s Disease Neuroimaging Initiative were analyzed using a region-wise radiomics framework across 10 anatomically defined brain regions. Radiomic features were extracted following automated segmentation, bias field correction, and intensity normalization. Age-associated radiomic patterns were first identified in cognitively normal subjects using Spearman correlation analysis. Features demonstrating significant age sensitivity were subsequently compared between cognitively normal and Alzheimer\u2019s disease cohorts across age bins using Welch\u2019s two-sample t-tests with permutation-based significance estimation and false discovery rate correction. Medial temporal and limbic regions, particularly the hippocampus, entorhinal cortex, and cingulum, demonstrated consistent age-aligned radiomic trajectories with systematic, statistically significant disease-related shifts across all age bins, supported by large effect sizes and bootstrap-validated confidence intervals. In contrast, several other regions demonstrated more heterogeneous and less stable patterns of group separation across age bins. Secondary analysis using late mild cognitive impairment subjects demonstrated that these radiomic divergences are detectable at the transition from normal cognition to mild cognitive impairment, with statistically significant CN\u2013LMCI separation but no significant LMCI\u2013AD separation, positioning the identified markers as early-stage rather than late-stage indicators of neurodegeneration. These findings indicate that Alzheimer\u2019s disease does not uniformly mimic normal aging across the brain but instead selectively perturbs radiomic features associated with normative aging trajectories. The identified markers represent promising candidates for age-adjusted radiomic biomarkers, warranting validation in independent cohorts to establish their generalisability. The fully automated nature of the analytical pipeline \u2014 spanning segmentation, feature extraction, and statistical comparison without manual annotation \u2014 may facilitate scalable validation of these biomarkers in larger neuroimaging cohorts.<\/jats:p>","DOI":"10.64898\/2026.06.17.732875","type":"posted-content","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:00:18Z","timestamp":1782180018000},"source":"Crossref","is-referenced-by-count":0,"title":["Alzheimer\u2019s Disease Selectively Perturbs Age-Sensitive Brain Radiomic Features Across the Disease Continuum"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7878-9781","authenticated-orcid":false,"given":"Megha Suman","family":"Sharma","sequence":"first","affiliation":[{"name":"Radpretation Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2764-4326","authenticated-orcid":false,"given":"Rajat","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Radpretation Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4965-5509","authenticated-orcid":false,"given":"Nikita","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Radpretation Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Radpretation Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Kaushik","sequence":"additional","affiliation":[{"name":"Radpretation Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026062423402095000_2026.06.17.732875v1.1","doi-asserted-by":"crossref","unstructured":"D. G. Altman , Practical Statistics for Medical Research. London, U.K.: Chapman & Hall, 1991.","DOI":"10.1201\/9780429258589"},{"key":"2026062423402095000_2026.06.17.732875v1.2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"2026062423402095000_2026.06.17.732875v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789"},{"key":"2026062423402095000_2026.06.17.732875v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2017.10.001"},{"key":"2026062423402095000_2026.06.17.732875v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117026"},{"key":"2026062423402095000_2026.06.17.732875v1.6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-72535-0"},{"key":"2026062423402095000_2026.06.17.732875v1.7","doi-asserted-by":"publisher","DOI":"10.2463\/mrms.rev.2024-0053"},{"key":"2026062423402095000_2026.06.17.732875v1.8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0213459"},{"key":"2026062423402095000_2026.06.17.732875v1.9","doi-asserted-by":"crossref","unstructured":"B. Efron and R. J. Tibshirani , An Introduction to the Bootstrap. New York, NY, USA: Chapman & Hall, 1993.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"2026062423402095000_2026.06.17.732875v1.10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234255"},{"key":"2026062423402095000_2026.06.17.732875v1.11","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10034-2"},{"key":"2026062423402095000_2026.06.17.732875v1.12","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"2026062423402095000_2026.06.17.732875v1.13","doi-asserted-by":"publisher","DOI":"10.1172\/JCI158453"},{"key":"2026062423402095000_2026.06.17.732875v1.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.041"},{"key":"2026062423402095000_2026.06.17.732875v1.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.08.023"},{"key":"2026062423402095000_2026.06.17.732875v1.16","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(09)70299-6"},{"key":"2026062423402095000_2026.06.17.732875v1.17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"2026062423402095000_2026.06.17.732875v1.18","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2025.1693840"},{"key":"2026062423402095000_2026.06.17.732875v1.19","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181b16431"},{"key":"2026062423402095000_2026.06.17.732875v1.20","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910150117"},{"key":"2026062423402095000_2026.06.17.732875v1.21","doi-asserted-by":"publisher","DOI":"10.1016\/j.nic.2022.04.010"},{"key":"2026062423402095000_2026.06.17.732875v1.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2021.101311"},{"key":"2026062423402095000_2026.06.17.732875v1.23","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000001383"},{"key":"2026062423402095000_2026.06.17.732875v1.24","doi-asserted-by":"publisher","DOI":"10.1038\/s44400-025-00031-1"},{"key":"2026062423402095000_2026.06.17.732875v1.25","unstructured":"PyRadiomics contributors, \u201cPyRadiomics documentation,\u201d [Online]. Available: https:\/\/pyradiomics.readthedocs.io\/en\/latest\/."},{"key":"2026062423402095000_2026.06.17.732875v1.26","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25438"},{"key":"2026062423402095000_2026.06.17.732875v1.27","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000002864"},{"key":"2026062423402095000_2026.06.17.732875v1.28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2046908"},{"key":"2026062423402095000_2026.06.17.732875v1.29","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2001.0978"},{"key":"2026062423402095000_2026.06.17.732875v1.30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.12.025"},{"key":"2026062423402095000_2026.06.17.732875v1.31","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"2026062423402095000_2026.06.17.732875v1.32","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"2026062423402095000_2026.06.17.732875v1.33","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(13)70124-8"},{"key":"2026062423402095000_2026.06.17.732875v1.34","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/34.1-2.28"},{"key":"2026062423402095000_2026.06.17.732875v1.35","doi-asserted-by":"publisher","DOI":"10.1109\/42.511747"},{"key":"2026062423402095000_2026.06.17.732875v1.36","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000030"},{"key":"2026062423402095000_2026.06.17.732875v1.37","doi-asserted-by":"publisher","DOI":"10.1109\/42.906424"},{"key":"2026062423402095000_2026.06.17.732875v1.38","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3574366"},{"key":"2026062423402095000_2026.06.17.732875v1.39","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.06.17.732875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T06:43:22Z","timestamp":1782369802000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.06.17.732875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":39,"URL":"https:\/\/doi.org\/10.64898\/2026.06.17.732875","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"subtype":"preprint"}}