Age related independent set changes on functional brain connectivity networks

The human brain’s complexity arises not merely from the number of neurons but from the intricate networks they form. Functional brain networks, characterized by their complex connectivity patterns, play a crucial role in cognitive and behavioral processes. Functional connectivity (FC), typically assessed using functional magnetic resonance imaging (fMRI), reveals temporal correlations between neural activities in different brain regions. These networks contribute to modular organization of the brain, which facilitates efficient information processing, such as attention and memory. Aging is known to affect cognitive performance with a decline in the efficiency of brain networks, leading to less effective communication between different brain regions. The altered modular organization of the network topology of the brain can be analyzed with graph theory. This study examines age-related changes in independent sets within functional brain networks from a graph theoretical perspective. An independent set in a network is a subset of nodes that share no direct connections. A maximum independent set is the largest such subset, containing the greatest number of mutually non-adjacent nodes within the graph. We utilize fMRI data to construct these networks and apply graph-theoretical methods to compare independent sets between young and older adults in both resting and task states. The analyses show that aging is associated with a rise in the size of independent sets, reflecting age-related functional fragmentation and reduced network integration. Control analyses using density-matched networks confirmed that these group differences are largely driven by underlying differences in network density, though the maximum independent set (MIS) uniquely reveals which regions become functionally isolated.