Task-Based Core-Periphery Organization of Human Brain Dynamics

Abstract

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we 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 that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.

Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach

“…1. Furthermore, recent evidence indicates a core common network exists in human and macaque brains and that it is tightly connected [2]. In this work we develop, for the first time, a group-wise core SCN extraction algorithm which guarantees a connected network output.…”

“…The resulting graph can be a set of disconnected subgraphs. This is problematic, recent studies have shown the core network to be tightly connected [2]. However, extracting connected group-wise core SCN is far from simple: an algorithm to find the largest core network of a population cannot find an approximated solution in polynomial time.…”

Social influence and the brain: persuasion, susceptibility to influence and retransmission

“…First, data analysis approaches that move beyond traditional mean activation estimates will offer new perspectives on social influence, for instance, by examining neural networks rather than individual regions [51,52]. Specific examples of this would include using techniques derived from graph theory [53][54][55][56], connectivity analysis [57], or cognitive architectures [58,59].…”

Chronological Prospective of Behavioral Decision Making: A Three Realm Model for Cognitive Processing and Behavior

“…There has been burgeoning evidence on the dynamism of network modules that have been shown to continually evolve and reconfigure across time and cognitive states [20]. fMRI studies have also shown existence of less modular configurations in the brain during resting state where there is a cross-talk between different modules [21].…”