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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.

Show MeSH
Assortativity.The degree of each node is compared to its neighbours' mean out-degree (a) and in-degree (b).
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pcbi-1003982-g005: Assortativity.The degree of each node is compared to its neighbours' mean out-degree (a) and in-degree (b).

Mentions: It is possible that the communication profile of CA1 is not determined by its own degree properties, but by the connectivity in its local neighbourhood. Given the diffusive dynamics of the present model, it may be that signal traffic converges to CA1 because of the nodes that project to it (i.e. its in-neighbours). One possibility is that the in-neighbours of CA1 collectively have a higher than average in-degree, and that CA1 is statistically more likely to experience higher levels of signal traffic. A second possibility is that the in-neighbours of CA1 collectively have a lower than average out-degree, thus funneling signal traffic to CA1. The plots in Figs. 4 C-F explore these possibilities and suggest that neither is likely, because neither the mean in-degree nor out-degree of the in-neighbours of CA1 is able to explain the high levels of signal traffic at that node. Furthermore, the assortativity plots in Fig. 5A and B suggest that the in-neighbours of CA1 have neither higher than expected in-degree nor lower than expected out-degree.


A network convergence zone in the hippocampus.

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

Assortativity.The degree of each node is compared to its neighbours' mean out-degree (a) and in-degree (b).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003982-g005: Assortativity.The degree of each node is compared to its neighbours' mean out-degree (a) and in-degree (b).
Mentions: It is possible that the communication profile of CA1 is not determined by its own degree properties, but by the connectivity in its local neighbourhood. Given the diffusive dynamics of the present model, it may be that signal traffic converges to CA1 because of the nodes that project to it (i.e. its in-neighbours). One possibility is that the in-neighbours of CA1 collectively have a higher than average in-degree, and that CA1 is statistically more likely to experience higher levels of signal traffic. A second possibility is that the in-neighbours of CA1 collectively have a lower than average out-degree, thus funneling signal traffic to CA1. The plots in Figs. 4 C-F explore these possibilities and suggest that neither is likely, because neither the mean in-degree nor out-degree of the in-neighbours of CA1 is able to explain the high levels of signal traffic at that node. Furthermore, the assortativity plots in Fig. 5A and B suggest that the in-neighbours of CA1 have neither higher than expected in-degree nor lower than expected out-degree.

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