M. Puck Rombach

TL;DR: In this article, a meso-scale feature known as core-periphery structure is proposed to identify densely connected core nodes and sparsely connected peripheral nodes in a network.

TL;DR: In this article, the authors measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions, and they find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery, whereas connectivity changes frequently.

TL;DR: This paper develops a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely connected core nodes and sparsely connected peripheral nodes in a network.

TL;DR: This paper develops a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely connected core nodes and sparsely connected peripheral nodes in a network.

TL;DR: The results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked, which enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.