Resting-state functional dynamic connectivity and healthy aging: A sliding-window network analysis.

BACKGROUND Graph theory has been widely used to study structural and functional brain connectivity changes in healthy aging, and occasionally with clinical samples; in both cases, during task-related and resting-state experiments. Recent studies have focused their interest on dynamic changes during a resting-state fMRI register in order to identify differences in non-stationary patterns associated with the aging process. The objective of this study was to characterize resting-state fMRI network dynamics in order to study the healthy aging process. METHOD 114 healthy older adults were measured in a resting-state paradigm using fMRI. A sliding-window approach to graph theory was used to measure the mean degree, average path length, clustering coefficient, and small-worldness of each subnetwork, and the impact of age and time in each graph measure was assessed. RESULTS A combined effect of age and time was detected in mean degree, average path length, and small-worldness, where participants aged 75 to 79 showed a curvilinear trend with reduced network density and increased small-world coefficient in the middle of the register. CONCLUSION An effect of age was observed on average path length, with younger participants showing slightly lower scores.