# Individual Differences in Dynamic Functional Brain Connectivity Across the Human Lifespan

arXiv: https://arxiv.org/abs/1606.09545 · HTML: https://mlsanity.com/paper/1606.09545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints](https://mlsanity.com/paper/2510.09175/) (2025)

## Abstract (arXiv)

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Understanding and quantifying these differences is a necessary first step towards developing predictive methods derived from network topology. Here, we present a method to quantify individual differences in brain functional dynamics by applying hypergraph analysis, a method from dynamic network theory. Using a summary metric derived from the hypergraph formalism---hypergraph cardinality---we investigate individual variations in two separate and complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observed that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, one of the memory tasks evinced 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 of varying ages 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 age-related changes in brain function can be better understood by taking an integrative approach that incorporates information about the dynamics of functional interactions.