Emergence of Rich-Club Topology and Coordinated Dynamics in Development of Hippocampal Functional NetworksIn Vitro

Abstract

Recent studies demonstrated that the anatomical network of the human brain shows a "rich-club" organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called "hub neurons"). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a "rich-get-richer" growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level.

Multi-scale brain networks

“…The meaning of “scale” can vary depending on context; here we focus on three possible definitions relevant to the study of brain networks. First, a network’s spatial scale refers to the granularity at which its nodes and edges are defined and can range from that of individual cells and synapses (Jarrell et al, 2012; Shimono and Beggs, 2015; Schroeter et al, 2015; Lee et al, 2016) to brain regions and large-scale fiber tracts (Bullmore and Bassett, 2011). Second, networks can be characterized over temporal scales with precision ranging from sub-millisecond (Khambhati et al, 2015; Burns et al, 2014) to that of the entire lifespan (Zuo et al, 2010; Betzel et al, 2014; Gu et al, 2015b), to evolutionary changes across different species (van den Heuvel et al, 2016).…”

The Cluster Variation Method: A Primer for Neuroscientists

“…Recent studies of both structural and functional connectivity have uncovered several topological themes, including “rich clubs” in which hubs are densely interconnected in a “hub complex” [60,61]. These hub complexes are a set of hub regions that are more densely interconnected than predicted by chance alone [62].…”

Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex

“…Thus, it has been argued that maintenance of such a costly network component should offer advantages to the brain's computational performance. In line with this thought, mature rich-clubs were indeed shown to be of great importance for routing of spontaneous activity flow in the network frequently acting as brokers for spontaneous multi-unit activity, suggesting a role of rich-clubs for orchestrating coordinated activity in the network, for example switching between different network states (Crossley et al, 2013; Leech and Sharp, 2014; Senden et al, 2014; Schroeter et al, 2015). …”

“…Also, structural rich-club nodes have higher levels of metabolic energy consumption than peripheral nodes (Collin et al, 2014b). Structural rich clubs were shown to connect early and are maintained through in maturation (“rich-get-richer”), suggesting that these nodes may have a key developmental role (Schroeter et al, 2015). Thus, it has been argued that maintenance of such a costly network component should offer advantages to the brain's computational performance.…”