A Network Convergence Zone in the Hippocampus

<div><p>The hippocampal formation is a key structure for memory function in the brain. The functional anatomy of the brain suggests that the hippocampus may be a convergence zone, as it receives polysensory input from distributed association areas throughout the neocortex. However, recent quantitative graph-theoretic analyses of the static large-scale connectome have failed to demonstrate the centrality of the hippocampus; in the context of the whole brain, the hippocampus is not among the most connected or reachable nodes. Here we show that when communication dynamics are taken into account, the hippocampus is a key hub in the connectome. Using a novel computational model, we demonstrate that large-scale brain network topology is organized to funnel and concentrate information flow in the hippocampus, supporting the long-standing hypothesis that this region acts as a critical convergence zone. Our results indicate that the functional capacity of the hippocampus is shaped by its embedding in the large-scale connectome.</p></div

Neighbourhood of CA1.

<p>Nodes TFM and TFL have large in-degrees and low out-degrees, causing traffic to be funneled towards CA1. The nodes are spatially positioned in a way that coincides with the directionality of edges, i.e. information is projected from top to bottom.</p

Assortativity.

<p>The degree of each node is compared to its neighbours' mean out-degree (a) and in-degree (b).</p

Role of network topology and directionality.

<p>The mean and standard deviation of CA1 node metrics: (a) arrivals, (b) utilization and (c) node contents. Data represent 100 simulations on the original macaque brain network (red), a single simulation for 100 randomized surrogate networks (green) and a single simulation for 100 surrogate networks with randomly reversed directions (blue).</p

Discrete-event simulation.

<p>Schematic showing the propagation of two signal units in a simple 3-node, 2-pathway network.</p

CA1 as a communication outlier.

<p>Communication metrics (node contents and arrivals) are compared to connectivity metrics, including in-degree (a,b), neighbours' mean in-degree (c,d) and neighbours' mean out-degree (e,f). In panels c-f, “neighbours” refers to nodes that project to CA1.</p

Node metrics.

<p>(a) Three local metrics of communication efficiency (utilization, node contents and arrivals) and information flow are shown for all 242 nodes of the network. averaged over 500 simulations (, , ). (b) Inflated surface renderings showing the anatomical distribution for the arrivals statistic, for the lateral and medial surfaces.</p

Degree imbalances.

<p>(a) The total number of signal units that traversed a particular connection. (b) The ten most traversed connections. (c) A histogram of all connections in (a), showing the distribution of signal traffic on all connections. (d) The relationship between in-degree and out-degree for all nodes in the network.</p

Exploring the Morphospace of Communication Efficiency in Complex Networks

<div><p>Graph theoretical analysis has played a key role in characterizing global features of the topology of complex networks, describing diverse systems such as protein interactions, food webs, social relations and brain connectivity. How system elements communicate with each other depends not only on the structure of the network, but also on the nature of the system's dynamics which are constrained by the amount of knowledge and resources available for communication processes. Complementing widely used measures that capture efficiency under the assumption that communication preferentially follows shortest paths across the network (“routing”), we define analytic measures directed at characterizing network communication when signals flow in a random walk process (“diffusion”). The two dimensions of routing and diffusion efficiency define a morphospace for complex networks, with different network topologies characterized by different combinations of efficiency measures and thus occupying different regions of this space. We explore the relation of network topologies and efficiency measures by examining canonical network models, by evolving networks using a multi-objective optimization strategy, and by investigating real-world network data sets. Within the efficiency morphospace, specific aspects of network topology that differentially favor efficient communication for routing and diffusion processes are identified. Charting regions of the morphospace that are occupied by canonical, evolved or real networks allows inferences about the limits of communication efficiency imposed by connectivity and dynamics, as well as the underlying selection pressures that have shaped network topology.</p> </div

Multi-objective optimization in the efficiency morphospace.

<p>Results shown are for evolutionary processes driven by network efficiency measures for networks with and . Blue and red squares indicate the reference points of regular lattices and randomized networks respectively. Green points indicate the initial seed population. Gray circles indicate evolving networks over epochs, with darker shades of gray indicating networks encountered in later epochs. Orange points show Pareto-front (non-dominated) solutions. (a) Snapshots illustrating the expansion of the Pareto fronts at epochs 2, 25, 50, 100, and 200. (b) Final solutions were reached after 517, 704, 977, and 433 epochs for fronts 1, 2, 3, and 4 respectively. Black asterisks denote positions of the example graphs shown in insets. Yellow points show dominated solutions of the final populations. Grey points show coordinates visited during the evolutionary process at different epochs (denoted by the gray-level).</p