Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

Abstract

Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

“…The dataset used here came from a single-centre, observational cohort study of 1213 subjects [ 29 , 34 , 35 ]. The participants were home-dwelling elderly volunteers, aged 69 to 85, without relevant psychiatric, neurological, or systemic disorders.…”

“…In the machine-learning literature, brain age is understood as the model estimate to be compared with the given or chronological age [ 29 ]. However, this approach depends on the model’s capacity to accurately predict the brain’s biological age.…”

Comparison of various machine learning regression models based on Human age prediction

“…The authors indicated that the cortical thickness in temporal parietal lobes exhibited better prediction accuracy than frontal and occipital lobes. Additionally, they suggested that integrating the prediction model and interpretation process could help to reduce the gap between chronological and real brain age [16].…”

Novel Approaches to Memory and Aging: The Editorial

“…Gómez-Ramírez et al [ 15 ] applied machine learning from Magnetic Resonance Imaging (MRI) brain segmentation and cortical parcellation to assess age-related brain changes in normal brain aging. They found brain-to-intracranial-volume ratio to be the most significant aspect in predicting age, followed by hippocampal volume, and discovered cortical thickness in temporal and parietal lobes to be more predictive than frontal and occipital lobes.…”