Dynamic brain network evolution in normal aging based on computational experiments

The mechanisms of normal aging of the human brain are insufficiently understood at present. This lack of systematic understanding impedes the exploration of new treatments for age-related diseases and approaches to extend our lifespan. The objective of this study was to develop a novel evolution model to simulate the dynamic alteration processes in functional brain networks that occur during normal aging, using computational experiments. Six global topological properties and a nodal metric were applied to characterize functional magnetic resonance imaging data on the brain networks of individuals from three different age groups. Comparing these real-world results to our simulation results showed that our evolution model captures well the dynamic processes of normal aging in functional brain networks. Our research shows that a tradeoff exists between the constraints on the degree distribution and the tendency toward clustered connections of functional brain networks during normal aging. These computational experiments provide a more comprehensive perspective that addresses dynamic alterations across a large time scale, which traditional research techniques cannot achieve. Our model is therefore of profound significance for exploring the mechanisms of normal aging. Computational modeling was used to simulate aging of the functional brain network.Topological properties of young, middle-aged, and old brain networks were studied.Our data-driven model simulated the continuous evolution process of network aging.Our model accurately emulated the changes that occur in real networks with age.This is the first model of functional brain network aging on a large time scale.