Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism—hypergraph cardinality—we investigate individual variations in two separate, complementary data sets. The first data set (“multi-task”) consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set (“age-memory”), in which 95 individuals, aged 18–75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.Author Summary: Complex patterns of activity in each individual human brain generate the unique range of thoughts and behaviors that person experiences. Individual differences in ability, age, state of mind, and other characteristics are tied to differences in brain activity, but determination of the exact nature of these relationships has been limited by the intrinsic complexity of the brain. Here, we apply dynamic network theory to quantify fundamental features of individual neural activity. We represent functional connections between brain regions as a time varying network, and then identify groups of these interactions that exhibit similar behavior over time. The result of this construction is referred to as a hypergraph, and each grouping within the hypergraph is called a hyperedge. We find that the number of these hyperedges in an individual’s hypergraph is a trait-like metric, with considerable variation across the population of subjects, but remarkable consistency within each subject as they perform different tasks. We find a significant correspondence between this metric and the subject’s age, indicating that the dynamics of functional brain activity in older individuals tends to be more dynamically segregated. This new insight into age-related changes in the dynamics of cognitive processing expands our knowledge of the effects of age on brain function and confirms our methods as promising for quantifying and examining individual differences.