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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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Discrete-event simulation.Schematic showing the propagation of two signal units in a simple 3-node, 2-pathway network.
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pcbi-1003982-g001: Discrete-event simulation.Schematic showing the propagation of two signal units in a simple 3-node, 2-pathway network.

Mentions: To investigate the role of the hippocampus in large-scale network communication, we modeled a macaque brain anatomical network as a communication system. The structural network was derived from the online Collation of Connectivity data on the Macaque brain (CoCoMac) [19], [20], while information flow was modeled as a discrete-event queueing network [18], [21](Fig. 1). Signal units were continually generated at randomly-selected grey matter nodes in the network and assigned randomly-selected destination nodes. They then diffused through the network via white matter projections. Grey matter nodes were modeled as servers with a finite buffer capacity, such that if a signal unit arrived at an occupied node, a queue was formed. Upon reaching its destination node, the signal unit was removed from the network.


A network convergence zone in the hippocampus.

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

Discrete-event simulation.Schematic showing the propagation of two signal units in a simple 3-node, 2-pathway network.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003982-g001: Discrete-event simulation.Schematic showing the propagation of two signal units in a simple 3-node, 2-pathway network.
Mentions: To investigate the role of the hippocampus in large-scale network communication, we modeled a macaque brain anatomical network as a communication system. The structural network was derived from the online Collation of Connectivity data on the Macaque brain (CoCoMac) [19], [20], while information flow was modeled as a discrete-event queueing network [18], [21](Fig. 1). Signal units were continually generated at randomly-selected grey matter nodes in the network and assigned randomly-selected destination nodes. They then diffused through the network via white matter projections. Grey matter nodes were modeled as servers with a finite buffer capacity, such that if a signal unit arrived at an occupied node, a queue was formed. Upon reaching its destination node, the signal unit was removed from the network.

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