Normal Brain Aging: Prediction of Age, Sex and White Matter Hyperintensities Using a MR Image-Based Machine Learning Technique

Abstract

A better understanding of normal human brain aging is required to better study age-related neurodegeneration including cognitive impairment. We propose an automatic deep-learning method to analyze the predictive ability of magnetic resonance images with respect to age, sex and the presence of an age-related pathology (white matter hyperintensity, WMH). Experiments performed in a large dataset, containing 200 normal subjects, resulted in average accuracy rates to predict subject age (82.0%), sex (79.5%), and WMH occurrence (72.5%) when combining handcrafted texture and convolutional features. Positive and negative correlations between other extracted features and the subject characteristics (age, sex and WMH occurrence) were also observed. Even though human brain variability due to age, sex and WMH occurrence in structural magnetic resonance imaging may be subtle (and often not observable by human specialists), our results demonstrate that MR images alone contain relevant information that can better characterize the aging process and some demographic information of the population.