The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases.
Keywords: brain age prediction; complex network; gray matter volume; interpretable machine learning models.