Associations of Brain Structure and Neuropsychological Function With Artificial Intelligence Estimates of Biological Vascular Age

BACKGROUND: Accelerated vascular aging, assessed as artificial intelligence–based vascular age (AIVA), is associated with small vessel disease that may impact brain structure and neuropsychological function. METHODS: In a cross-section of Framingham Heart Study participants, AIVA was estimated using a validated convolutional neural network trained to predict carotid-femoral pulse wave velocity from a normalized pressure waveform. Brain structure was assessed using magnetic resonance imaging with diffusion tensor imaging, and neuropsychological function was assessed using a standardized test battery. Analyses included magnetic resonance imaging (N=2313) and neuropsychological (N=3001) samples. We used multivariable linear and logistic regression to relate AIVA to brain structural and neuropsychological functional measures. RESULTS: The mean±SD age across participants was 62±11 years; 56% were women. In multivariable models, higher AIVA was associated with worse markers of cerebral small vessel disease (mean white matter free water: β [per SD], 0.09 [95% CI, 0.04–0.14]; P <0.001; peak width of skeletonized mean diffusivity: β , 0.05 [95% CI, 0.00–0.10]; P =0.045; white matter hyperintensity volume: β , 0.08 [95% CI, 0.03–0.12]; P <0.001) and higher odds of cerebrovascular injury (presence of extensive white matter hyperintensities: odds ratio [per SD], 1.22 [95% CI, 1.02–1.47]; P =0.03; presence of brain infarcts: odds ratio, 1.44 [95% CI, 1.02–2.04]; P =0.04). In addition, higher AIVA was associated with worse performance on Trails B-A ( β , −0.05 [95% CI, −0.10 to −0.01]; P =0.03), similarities ( β , −0.06 [95% CI, −0.11 to −0.01]; P =0.02), and global cognition ( β , − 0.07 [95% CI, −0.12 to −0.03]; P =0.002) and higher odds of prevalent depressive symptoms (odds ratio, 1.24 [95% CI, 1.08–1.41]; P =0.002) and high Center for Epidemiologic Studies Depression Scale score (odds ratio, 1.20 [95% CI, 1.00–1.44]; P =0.047). Vascular brain injury markers partially mediated the associations of AIVA with global cognition score and presence of depressive symptoms. CONCLUSIONS: Peripheral pressure waveform AIVA may be a novel, noninvasive indicator of subclinical vascular brain injury and neuropsychological function.

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Journal
Arteriosclerosis Thrombosis and Vascular Biology
Published
2026-09-24
DOI
https://doi.org/10.1161/atvbaha.126.325247
Primary Topic
Cardiovascular Health and Disease Prevention
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article
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article

Associations of Brain Structure and Neuropsychological Function With Artificial Intelligence Estimates of Biological Vascular Age

Ramachandran Srini Vasan, Sudha S. Seshadri, Naomi M. Hamburg, Gary F. Mitchell et al.
Arteriosclerosis Thrombosis and Vascular Biology
Cardiovascular Health and Disease Prevention
article

Associations of Brain Structure and Neuropsychological Function With Artificial Intelligence Estimates of Biological Vascular Age

Ramachandran Srini Vasan, Sudha S. Seshadri, Naomi M. Hamburg, Gary F. Mitchell, Leroy Leon Cooper, Alexa S. Beiser, Emelia J. Benjamin, Sokratis Charisis, Ayantika Banerjee, Timothy J. Korzinski, David J. Hamel-Sellman
article en

Abstract

BACKGROUND: Accelerated vascular aging, assessed as artificial intelligence–based vascular age (AIVA), is associated with small vessel disease that may impact brain structure and neuropsychological function. METHODS: In a cross-section of Framingham Heart Study participants, AIVA was estimated using a validated convolutional neural network trained to predict carotid-femoral pulse wave velocity from a normalized pressure waveform. Brain structure was assessed using magnetic resonance imaging with diffusion tensor imaging, and neuropsychological function was assessed using a standardized test battery. Analyses included magnetic resonance imaging (N=2313) and neuropsychological (N=3001) samples. We used multivariable linear and logistic regression to relate AIVA to brain structural and neuropsychological functional measures. RESULTS: The mean±SD age across participants was 62±11 years; 56% were women. In multivariable models, higher AIVA was associated with worse markers of cerebral small vessel disease (mean white matter free water: β [per SD], 0.09 [95% CI, 0.04–0.14]; P <0.001; peak width of skeletonized mean diffusivity: β , 0.05 [95% CI, 0.00–0.10]; P =0.045; white matter hyperintensity volume: β , 0.08 [95% CI, 0.03–0.12]; P <0.001) and higher odds of cerebrovascular injury (presence of extensive white matter hyperintensities: odds ratio [per SD], 1.22 [95% CI, 1.02–1.47]; P =0.03; presence of brain infarcts: odds ratio, 1.44 [95% CI, 1.02–2.04]; P =0.04). In addition, higher AIVA was associated with worse performance on Trails B-A ( β , −0.05 [95% CI, −0.10 to −0.01]; P =0.03), similarities ( β , −0.06 [95% CI, −0.11 to −0.01]; P =0.02), and global cognition ( β , − 0.07 [95% CI, −0.12 to −0.03]; P =0.002) and higher odds of prevalent depressive symptoms (odds ratio, 1.24 [95% CI, 1.08–1.41]; P =0.002) and high Center for Epidemiologic Studies Depression Scale score (odds ratio, 1.20 [95% CI, 1.00–1.44]; P =0.047). Vascular brain injury markers partially mediated the associations of AIVA with global cognition score and presence of depressive symptoms. CONCLUSIONS: Peripheral pressure waveform AIVA may be a novel, noninvasive indicator of subclinical vascular brain injury and neuropsychological function.

Arteriosclerosis Thrombosis and Vascular Biology
Vassar College (US), Boston Medical Center (US), The University of Texas at San Antonio Health Science Center (US), Institute for Neurodegenerative Disorders (US), Framingham Heart Study (US), The University of Texas at San Antonio (US)
Good health and well-being
Openalex Percentile: Top 11%
Cardiovascular Health and Disease Prevention
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