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Cortical network architecture for context processing in primate brain.

Chao ZC, Nagasaka Y, Fujii N - Elife (2015)

Bottom Line: We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity.These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows.This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako-shi, Japan.

ABSTRACT
Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

No MeSH data available.


Causal density in individual subjects.Cortical areas with high causal density (busy traffic) are indicated as red. Results from individual subjects and the summarized results after brain map registration are shown for each latent network structure.DOI:http://dx.doi.org/10.7554/eLife.06121.019
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fig4s1: Causal density in individual subjects.Cortical areas with high causal density (busy traffic) are indicated as red. Results from individual subjects and the summarized results after brain map registration are shown for each latent network structure.DOI:http://dx.doi.org/10.7554/eLife.06121.019


Cortical network architecture for context processing in primate brain.

Chao ZC, Nagasaka Y, Fujii N - Elife (2015)

Causal density in individual subjects.Cortical areas with high causal density (busy traffic) are indicated as red. Results from individual subjects and the summarized results after brain map registration are shown for each latent network structure.DOI:http://dx.doi.org/10.7554/eLife.06121.019
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4584448&req=5

fig4s1: Causal density in individual subjects.Cortical areas with high causal density (busy traffic) are indicated as red. Results from individual subjects and the summarized results after brain map registration are shown for each latent network structure.DOI:http://dx.doi.org/10.7554/eLife.06121.019
Bottom Line: We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity.These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows.This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako-shi, Japan.

ABSTRACT
Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

No MeSH data available.