Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms

2026-05-16

AI Summary

This study addresses the objective assessment of cerebrovascular aging and the identification of accelerated aging in patients with neurological disorders by integrating hemodynamic biomarkers. Specifically, it proposes a machine learning–based regression model that combines transcranial Doppler (TCD)–derived middle cerebral artery blood flow velocities, pulse waveform morphological features extracted via the MOCAIP algorithm, and heart rate variability to predict cerebrovascular age. The approach is novel in its concurrent use of MOCAIP-derived TCD features and heart rate variability for cerebrovascular age estimation. Results demonstrate that the model slightly overestimates cerebrovascular age by an average of 3.69 years in healthy individuals, while patients with various neurological diseases exhibit significantly accelerated cerebrovascular aging, thereby validating the method’s efficacy and clinical potential for evaluating cerebrovascular aging.

Abstract

Defining vascular age in terms of physiological function has become one focal point of the extensive studies to categorize and track chronological age. Transcranial Doppler (TCD) is a method by which cerebral blood flow velocity is measured along the major arteries feeding the human brain. This study aims to use features extracted from TCD to estimate chronological age and assess accelerated aging in subjects with various brain diseases. We predict subjects with various brain diseases to present with accelerated cerebrovascular aging when tested on various regression models trained by healthy subjects. 168 healthy subjects and 277 diseased subjects with bilateral TCD recordings of the middle cerebral artery were analyzed using the Morphological Analysis and Clustering of Intracranial Pressure (MOCAIP) algorithm. MOCAIP-generated features and heart rate variability features were used as input features for regression models to predict the brain vascular age. 66 subjects with acute stroke, 27 subjects with post stroke, 26 subjects with Alzheimer's disease, 23 subjects with mild cognitive impairment, and 135 established subjects were tested against the machine learning model to assess for accelerated cerebrovascular age. The trained model, on average, predicted healthy subjects' cerebrovascular age to be 3.69 years above their chronological age. Subjects with different disease conditions exhibited varying levels of age acceleration. The differences in healthy and diseased subjects' performances suggest that features generated using TCD may be relevant when evaluating accelerated cerebrovascular aging. Moreover, imbalanced datasets have been observed to affect the performance of machine-learning-based brain age prediction models.

Problem

• vascular age
• cerebral blood flow velocity
• accelerated aging
• brain diseases
• Transcranial Doppler

Innovation

• Transcranial Doppler
• cerebrovascular age
• MOCAIP algorithm
• machine learning
• accelerated aging