REORGANIZATION OF BRAIN CONNECTIVITY WITH AGE: DYNAMIC SIMULATIONS AND TOPOLOGICAL ANALYSES IN HUMAN STRUCTURAL NETWORKS

Brain aging involves alterations in structural and functional connectivity, affecting the modular organization of neural networks. In this study, DTI and fMRI imaging data from 147 individuals aged between 10 and 80 years were analyzed to construct structural (SC) and functional connectivity (FC) matrices. Analysis with Graph theory tools reveals a decrease in topological segregation with age, reflected in lower modularity and clustering coefficient, along with an increase in the participation coefficient, suggesting a compensatory reorganization towards greater integration. Using a dynamic mean field model (DMF) coupled to the SC, we simulated functional connectivity (sFC) and dynamic functional connectivity (dFC) patterns, that were optimized per subject through the global coupling parameter (G). Simulated FC was compared with empirical data using Pearson correlation, while dFC was assessed via the Kolmogorov-Smirnov distance between empirical and simulated matrices. Significant correlations were found between topological metrics of structural connectivity (such as density and modularity) and the richness of simulated dynamics, suggesting that certain structural properties condition the brain’s capacity to sustain multiple functional states. These findings provide insights into how aging-related changes in structural organization may lead to modifications in brain functional dynamics, offering a computational framework to study trajectories of healthy or pathological aging and potential functional biomarkers.