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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.


Maximum flow between areas in individual subjects.(A) The maximum information flows between cortical areas in all subjects. For each subject (column) and each structure (row), the maximal loadings of connections from each of the seven cortical areas (source, y-axis) to the others (sink, x-axis) are shown. Seven cortical areas were manually identified: the visual cortex (V), the parietal cortex (P), the posterior temporal cortex (pT), the anterior temporal cortex (aT), the motor cortex (M), the prefrontal cortex (PFC), and the medial PFC (mPF). The seven areas identified in all subjects are shown on the registered brain map on the right. (B) The average maximum information flow between areas across subjects presented in two different formats: matrices (left) and arrows (right).DOI:http://dx.doi.org/10.7554/eLife.06121.021
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fig4s3: Maximum flow between areas in individual subjects.(A) The maximum information flows between cortical areas in all subjects. For each subject (column) and each structure (row), the maximal loadings of connections from each of the seven cortical areas (source, y-axis) to the others (sink, x-axis) are shown. Seven cortical areas were manually identified: the visual cortex (V), the parietal cortex (P), the posterior temporal cortex (pT), the anterior temporal cortex (aT), the motor cortex (M), the prefrontal cortex (PFC), and the medial PFC (mPF). The seven areas identified in all subjects are shown on the registered brain map on the right. (B) The average maximum information flow between areas across subjects presented in two different formats: matrices (left) and arrows (right).DOI:http://dx.doi.org/10.7554/eLife.06121.021


Cortical network architecture for context processing in primate brain.

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

Maximum flow between areas in individual subjects.(A) The maximum information flows between cortical areas in all subjects. For each subject (column) and each structure (row), the maximal loadings of connections from each of the seven cortical areas (source, y-axis) to the others (sink, x-axis) are shown. Seven cortical areas were manually identified: the visual cortex (V), the parietal cortex (P), the posterior temporal cortex (pT), the anterior temporal cortex (aT), the motor cortex (M), the prefrontal cortex (PFC), and the medial PFC (mPF). The seven areas identified in all subjects are shown on the registered brain map on the right. (B) The average maximum information flow between areas across subjects presented in two different formats: matrices (left) and arrows (right).DOI:http://dx.doi.org/10.7554/eLife.06121.021
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4584448&req=5

fig4s3: Maximum flow between areas in individual subjects.(A) The maximum information flows between cortical areas in all subjects. For each subject (column) and each structure (row), the maximal loadings of connections from each of the seven cortical areas (source, y-axis) to the others (sink, x-axis) are shown. Seven cortical areas were manually identified: the visual cortex (V), the parietal cortex (P), the posterior temporal cortex (pT), the anterior temporal cortex (aT), the motor cortex (M), the prefrontal cortex (PFC), and the medial PFC (mPF). The seven areas identified in all subjects are shown on the registered brain map on the right. (B) The average maximum information flow between areas across subjects presented in two different formats: matrices (left) and arrows (right).DOI:http://dx.doi.org/10.7554/eLife.06121.021
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.