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A network convergence zone in the hippocampus.

Mišić B, Goñi J, Betzel RF, Sporns O, McIntosh AR - PLoS Comput. Biol. (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest Centre, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada; Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America.

ABSTRACT
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.

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Node metrics.(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.
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pcbi-1003982-g002: Node metrics.(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.

Mentions: The first step of the analysis focuses on various communication metrics for node CA1 relative to the rest of the network. Notably, CA1 experiences a high throughput of signal traffic (Fig. 2). Fig. 2A shows the complete information flow profile for the network, with mean utilization, node contents and total arrivals at each node, while Fig. 2B shows the spatial distribution of total arrivals. For all three metrics, CA1 is ranked #7 out of 242 nodes, placing it in the top 3%. Note that there are several other brain regions that experience high traffic, including areas 13a (anterior insular cortex), 32 (anterior cingulate cortex), 23c (ventral posterior cingulate cortex) and 31 (dorsal posterior cingulate cortex). These high-degree areas are part of the putative rich club of hub nodes and their role in network communication is explored in another report [18].


A network convergence zone in the hippocampus.

Mišić B, Goñi J, Betzel RF, Sporns O, McIntosh AR - PLoS Comput. Biol. (2014)

Node metrics.(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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256084&req=5

pcbi-1003982-g002: Node metrics.(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.
Mentions: The first step of the analysis focuses on various communication metrics for node CA1 relative to the rest of the network. Notably, CA1 experiences a high throughput of signal traffic (Fig. 2). Fig. 2A shows the complete information flow profile for the network, with mean utilization, node contents and total arrivals at each node, while Fig. 2B shows the spatial distribution of total arrivals. For all three metrics, CA1 is ranked #7 out of 242 nodes, placing it in the top 3%. Note that there are several other brain regions that experience high traffic, including areas 13a (anterior insular cortex), 32 (anterior cingulate cortex), 23c (ventral posterior cingulate cortex) and 31 (dorsal posterior cingulate cortex). These high-degree areas are part of the putative rich club of hub nodes and their role in network communication is explored in another report [18].

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest Centre, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada; Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America.

ABSTRACT
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.

Show MeSH