{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:10:24Z","timestamp":1785409824957,"version":"3.56.0"},"posted":{"date-parts":[[2025,5,11]]},"group-title":"Neuroscience","reference-count":25,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T00:00:00Z","timestamp":1746921600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"crossref","award":["MH121868"],"award-info":[{"award-number":["MH121868"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"crossref","award":["MH121867"],"award-info":[{"award-number":["MH121867"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"crossref","award":["EB027585"],"award-info":[{"award-number":["EB027585"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"crossref","award":["AG060942"],"award-info":[{"award-number":["AG060942"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"crossref","award":["AG066567"],"award-info":[{"award-number":["AG066567"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["1934292"],"award-info":[{"award-number":["1934292"]}]},{"id":[{"id":"https:\/\/ror.org\/04drjs621","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,5,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Non-invasive assessment of brain blood vessels with magnetic resonance (MR) imaging provides important information about brain health and aging. Time-of-flight MR angiography (TOF-MRA) in particular is commonly used to assess the morphology of blood vessels, but acquisition of MRA is time-consuming and is not as commonly employed in research studies or in the clinic as the more standard T1- or T2-weighted MR contrasts (T1w\/T2w). To enable quantification of brain blood vessel morphology in T1w\/T2w images, we trained a neural network model, anat2vessels, on a dataset with paired MR\/MRA. The model provides accurate segmentations as assessed in cross-validation on ground truth images, particularly in cases where T2w images are used. In addition, correlation between features that are extracted from model-based vessel segmentations and from ground truth account for as much as 78% of the variance in these features. We further evaluated the model in another dataset that does not include MRA and found that\n                  <jats:monospace>anat2vessels<\/jats:monospace>\n                  -based vessel morphology features contain information about aging that is not captured by cortical thickness features that are routinely extracted from T1w\/T2w images. Moreover, we found that vessel morphology features are associated with individual variability in blood pressure and cognitive abilities. Taken together these results suggest that\n                  <jats:monospace>anat2vessels<\/jats:monospace>\n                  could be fruitfully applied to a range of existing and new datasets to assess the role of brain blood vessels in aging and brain health. The methods are provided as open-source software in\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/nrdg\/anat2vessels\/\">https:\/\/github.com\/nrdg\/anat2vessels\/<\/jats:ext-link>\n                  .\n                <\/jats:p>","DOI":"10.1101\/2025.05.06.652518","type":"posted-content","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:40:11Z","timestamp":1747010411000},"source":"Crossref","is-referenced-by-count":0,"title":["Vessels hiding in plain sight: quantifying brain vascular morphology in anatomical MR images using deep learning"],"prefix":"10.64898","author":[{"given":"Asa","family":"Gilmore","sequence":"first","affiliation":[{"name":"Department of Psychology, University of Washington, Guthrie 119A, Seattle, 98195, WA, USA"},{"name":"eScience Institute, University of Washington, 1410 NE Campus Way, Seattle, 98195, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anita Esi","family":"Eshun","sequence":"additional","affiliation":[{"name":"Kumasi Centre for Collaborative Research, Kumasi, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Washington, 98195, WA, USA"},{"name":"Roger and Angie Karalis Johnson Retina Center, University of Washington, 98195, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Washington, 98195, WA, USA"},{"name":"Roger and Angie Karalis Johnson Retina Center, University of Washington, 98195, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-1985","authenticated-orcid":false,"given":"Ariel","family":"Rokem","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Washington, Guthrie 119A, Seattle, 98195, WA, USA"},{"name":"eScience Institute, University of Washington, 1410 NE Campus Way, Seattle, 98195, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026041121260807000_2025.05.06.652518v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/S41586-022-04554-Y"},{"key":"2026041121260807000_2025.05.06.652518v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5932"},{"key":"2026041121260807000_2025.05.06.652518v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.03.056"},{"key":"2026041121260807000_2025.05.06.652518v1.4","doi-asserted-by":"publisher","DOI":"10.1111\/psyp.13796"},{"key":"2026041121260807000_2025.05.06.652518v1.5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1215740"},{"key":"2026041121260807000_2025.05.06.652518v1.6","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2007.01233.x"},{"key":"2026041121260807000_2025.05.06.652518v1.7","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2021.7009"},{"key":"2026041121260807000_2025.05.06.652518v1.8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2011.03508.x"},{"key":"2026041121260807000_2025.05.06.652518v1.9","doi-asserted-by":"publisher","DOI":"10.2463\/mrms.rev.2021-0140"},{"key":"2026041121260807000_2025.05.06.652518v1.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2019.12.008"},{"key":"2026041121260807000_2025.05.06.652518v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-91669-3"},{"key":"2026041121260807000_2025.05.06.652518v1.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"2026041121260807000_2025.05.06.652518v1.13","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240263"},{"key":"2026041121260807000_2025.05.06.652518v1.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2008.03.022"},{"key":"2026041121260807000_2025.05.06.652518v1.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.09.018"},{"issue":"12","key":"2026041121260807000_2025.05.06.652518v1.16","first-page":"1","article-title":"COSTA: A multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation","volume":"PP","year":"2024","journal-title":"IEEE Trans. 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