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Mapping streamlines to an anatomical parcellation yields structural connectomes. However, in older adults, where white matter alterations and atrophy are common, the choice of reference template and tissue segmentation could be particularly consequential for obtaining accurate, interpretable structural connectomes. Using a conventional pipeline, we evaluated how these two factors, the normalization template and the segmentation used to constrain anatomically guided tractography, affect connectome estimates in cognitively healthy elderly participants. These methodological choices produced systematic and significant differences in network topology and their relationships with clinically relevant variables across standard connectome measures, particularly in patients with white matter hyperintensities. Age-appropriate normalization and lesion-aware anatomically constrained tractography yielded networks with more plausible anatomy and more consistent relationships with cognition and imaging measures, whereas unconstrained tracking inflated density without improving interpretability. These findings demonstrate that structural connectome outcomes are contingent upon template and segmentation selection. We, therefore, advocate for explicit reporting of these methodological parameters and recommend the use of age-appropriate templates combined with white matter lesion-aware segmentation incorporating anatomically informed constraints.<\/jats:p>","DOI":"10.1162\/imag.a.1384","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T20:01:42Z","timestamp":1789156902000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Mind the brain age: How segmentation and template selection reshape structural connectomes"],"prefix":"10.1162","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0537-1918","authenticated-orcid":false,"given":"Carlo","family":"Ferritto","sequence":"first","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN \u2014 ERL U 1228, Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Lioi","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0261-1265","authenticated-orcid":false,"given":"Pierre-Yves","family":"Jonin","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN \u2014 ERL U 1228, Rennes, France"},{"name":"Neurology Department, Rennes University Hospital, Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Coloigner","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN \u2014 ERL U 1228, Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"issue":"4","key":"2026093015554923700_IMAG.a.1384-b1","doi-asserted-by":"crossref","first-page":"e3841","DOI":"10.1002\/nbm.3841","article-title":"Imaging brain microstructure with diffusion MRI: Practicality and applications","volume":"32","author":"Alexander","year":"2019","journal-title":"NMR in Biomedicine"},{"issue":"1","key":"2026093015554923700_IMAG.a.1384-b2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","article-title":"Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"Avants","year":"2008","journal-title":"Medical Image Analysis"},{"issue":"3","key":"2026093015554923700_IMAG.a.1384-b3","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A reproducible evaluation of ANTs similarity metric performance in brain image registration","volume":"54","author":"Avants","year":"2011","journal-title":"NeuroImage"},{"issue":"4","key":"2026093015554923700_IMAG.a.1384-b4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12021-011-9109-y","article-title":"An open source multivariate framework for n-tissue segmentation with evaluation on public data","volume":"9","author":"Avants","year":"2011","journal-title":"Neuroinformatics"},{"key":"2026093015554923700_IMAG.a.1384-b5","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3389\/fnagi.2016.00292","article-title":"Global efficiency of structural networks mediates cognitive control in mild cognitive impairment","volume":"8","author":"Berlot","year":"2016","journal-title":"Frontiers in Aging Neuroscience"},{"key":"2026093015554923700_IMAG.a.1384-b6","doi-asserted-by":"crossref","first-page":"102325","DOI":"10.1016\/j.nicl.2020.102325","article-title":"Structural network efficiency predicts cognitive decline in cerebral small vessel disease","volume":"27","author":"Boot","year":"2020","journal-title":"NeuroImage: Clinical"},{"key":"2026093015554923700_IMAG.a.1384-b7","doi-asserted-by":"crossref","first-page":"102667","DOI":"10.1016\/j.nicl.2021.102667","article-title":"Cognition mediates the relation between structural network efficiency and gait in small vessel disease","volume":"30","author":"Cai","year":"2021","journal-title":"NeuroImage: Clinical"},{"issue":"1","key":"2026093015554923700_IMAG.a.1384-b8","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TMI.2014.2352414","article-title":"COMMIT: Convex optimization modeling for microstructure informed tractography","volume":"34","author":"Daducci","year":"2015","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2026093015554923700_IMAG.a.1384-b9","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.neuroimage.2013.03.053","article-title":"The parcellation-based connectome: Limitations and extensions","volume":"80","author":"De Reus","year":"2013","journal-title":"NeuroImage"},{"key":"2026093015554923700_IMAG.a.1384-b10","unstructured":"Dhollander, T., Raffelt, D., & Connelly, A. 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