Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps

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ID: 321873
2026
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Abstract
Abstract Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging 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 3D convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps generated by a pre-trained 3D patch-based deep learning model. Each model was trained and validated on 2,851 scans (1,507 females) from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using 1,233 subjects. The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error (MAE) of 3.95 years (R2 = 0.943), outperforming models trained on MRI (MAE = 4.10) or DeepCBV alone (MAE = 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 aging. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (Clinical Dementia Rating Sum of Boxes ⍴ = 0.403; Mini-Mental State Examination ⍴ = -0.310). DeepCBV-based BrainAGE showed particularly strong separation between stable vs. progressive MCI (Mann-Whitney U = 2.177 × 104, p = 4.43 × 10-8), 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 AD progression, supporting its potential role in risk stratification, early detection, and monitoring of therapeutic response. By enabling functional-like assessment from routine MRI, this approach lowers barriers to multimodal evaluation and provides a clinically actionable biomarker for large-scale aging and dementia studies.
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Authors Jordan Jomsky, Zongyu Li, Kay C. Igwe, Yiren Zhang, Max Lashley, Tal Nuriel, Andrew Laine, Scott Small, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative and for the Alzheimer’s Disease Neuroimaging Initiative, Howard Rosen, Bradford C Dickerson, Kimoko Domoto-Reilly, David Knopman, Bradley F Boeve, Adam L Boxer, John Kornak, Bruce L. Miller, William W. Seeley, Maria‐Luisa Gorno‐Tempini, Scott McGinnis, Maria Luisa Mandelli
Journal Brain communications
Year 2026
DOI
10.1093/braincomms/fcag283
URL
https://doi.org/10.1093/braincomms/fcag283
Keywords Keywords not found

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