UC Santa Barbara

The human brain is a complex system in which interactions of billions of neurons give rise to behavior. fMRI allows researchers to measure the functional activity of the working brain, allowing both the localization of specific functions within the brain and the investigation of multivariate patterns of functional activation. These patterns have been found to correspond both to short-term brain states such as focused attention or daydreaming, and to characteristics such as age or disease. Functional patterns also show substantial variation across individuals. Understanding the correspondence of distributed functional activity to these various factors is an ongoing research area.

Network science is a valuable tool for representing complex brain function, providing a framework for quantifying multivariate activity as a network of interactions. Here, we build upon recent advances in dynamic network science, using time-evolving networks to investigate how the organization of brain dynamics is related to demographics and brain states.

We use \textit{hypergraphs} to analyze brain network dynamics during different cognitive tasks and the transitions between them. We identify the presence of \textit{hyperedges}, groups of functional interactions that fluctuate coherently in strength over time both within and across brain states. We develop metrics to quantify the variation of hyperedge structure between tasks and across individuals. We find that the spatial location of hyperedges is relatively consistent across individuals, serving as a signature of a cognitive task, while hyperedge size exhibits variation across individuals but remains consistent between tasks.

We also investigate the variation of brain dynamics across the human lifespan, using both hypergraphs and dynamic clusters, or \textit{communities}, of brain regions with similar activity. We find significant relationships between age and dynamic organization: younger subjects tend to have larger hyperedges, as well as less fragmented and more coherent communities, and their brain regions tend to switch between communities less often. Further, the dynamics of different cognitive brain systems respond differently to aging.

Finally, we propose and evaluate a method of targeted node removal during the data-driven detection of communities, using synthetic and fMRI-derived networks to show that the method can improve identification of multi-scale community structure, and help to resolve key features of community dynamics.