{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:14:08Z","timestamp":1788282848562,"version":"build-2803163510"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":202,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hevolution"},{"name":"American Federation for Aging Research New Investigator Awardee"},{"name":"Aging Biology and Geroscience Research","award":["PT# 24-4274 PG014621"],"award-info":[{"award-number":["PT# 24-4274 PG014621"]}]},{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","award":["811245"],"award-info":[{"award-number":["811245"]}],"id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Simons Foundation International"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. Deep learning-derived cerebral blood volume (DeepCBV) maps, synthesized from non-contrast MRI, offer a scalable alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that combines predictions from two separate three-dimensional convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps generated by a pre-trained three-dimensional patch-based deep learning model. Each model was trained and validated on 2851 scans (1507 females) from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer\u2019s disease (AD) using 1233 subjects. The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error of 3.95 years (R2 = 0.943), outperforming models trained on MRI (mean absolute error = 4.10) or DeepCBV alone (mean absolute error = 4.49). Saliency maps revealed complementary modality contributions: MRI emphasized white matter and cortical atrophy, while DeepCBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with known patterns of normal ageing. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN &amp;lt; MCI &amp;lt; AD) and correlated with cognitive impairment (Clinical Dementia Rating Sum of Boxes \u2374 = 0.403; Mini-Mental State Examination \u2374 = \u22120.310). DeepCBV-based BrainAGE showed a particularly strong separation between stable versus progressive MCI (Mann\u2013Whitney U = 2.177 \u00d7 104, P = 4.43 \u00d7 10\u22128), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. Integrating structural MRI with deep learning-derived vascular measures substantially enhances BrainAGE estimation and improves sensitivity to MCI and Alzheimer\u2019s disease progression, supporting its potential role in risk stratification, early detection and monitoring of therapeutic response. By enabling a functional-like assessment from routine MRI, this approach lowers barriers to multimodal evaluation and provides a clinically actionable biomarker for large-scale ageing and dementia studies.<\/jats:p>","DOI":"10.1093\/braincomms\/fcag283","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:33:02Z","timestamp":1784633582000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0168-4893","authenticated-orcid":false,"given":"Jordan","family":"Jomsky","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Columbia University , New York, NY 10027-7041 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical 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,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5252-8048","authenticated-orcid":false,"given":"Tal","family":"Nuriel","sequence":"additional","affiliation":[{"name":"Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University , New York, NY ,","place":["USA"]},{"name":"Department of Pathology and Cell Biology, Columbia University , New York, NY ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3797-0628","authenticated-orcid":false,"given":"Andrew","family":"Laine","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Columbia University , New York, NY 10027-7041 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4777-1502","authenticated-orcid":false,"given":"Scott A","family":"Small","sequence":"additional","affiliation":[{"name":"Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University , New York, NY ,","place":["USA"]},{"name":"Department of Neurology, Columbia University , New York, NY 10027 ,","place":["USA"]},{"name":"Department of Psychiatry, Columbia University , New York, NY 10027 ,","place":["USA"]},{"name":"Department of Radiology, Columbia University , New York, NY ,","place":["USA"]},{"name":"New York State Psychiatric Institute , New York, NY ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4803-0279","authenticated-orcid":false,"given":"Jia","family":"Guo","sequence":"additional","affiliation":[{"name":"Zuckerman Institute, Columbia University , New York, NY 10027-7041 ,","place":["USA"]},{"name":"Department of Psychiatry, Columbia University , New York, NY 10027 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