Individual Differences in Dynamic Functional Brain Connectivity Across the Human Lifespan

TL;DR

This study introduces hypergraph analysis to quantify individual differences in brain functional dynamics across the lifespan, revealing age-related variations in network topology that are consistent within individuals and linked to cognitive tasks.

Contribution

The paper presents a novel application of hypergraph formalism to measure individual differences in brain dynamics and demonstrates its relation to age across two diverse datasets.

Findings

Hypergraph cardinality varies across individuals but is consistent within individuals across tasks.

A marginally significant correlation between hypergraph cardinality and age was observed in a memory task.

The correlation between hypergraph cardinality and age becomes significant with a broader age range.

Abstract

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.