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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.


Related in: MedlinePlus

Robust connectivity across subjects.For each connectivity statistics (causal density, causal outflow, or maximum flow between areas) from each structure, we compared its values (after brain map registration) between each subject pair. As results, 15 correlation coefficients were acquired for each connectivity statistics to show how similar the connectivity statistics across subjects in all structures. The average correlation coefficients for causal density, causal outflow, and maximum flow between areas were 0.66 ± 0.08, 0.60 ± 0.05, and 0.69 ± 0.26, respectively (mean ± std, n = 15: 3 pairs, 5 structures). High correlations in connectivity statistics among subjects indicate that connectivity in each structure is robust across subjects.DOI:http://dx.doi.org/10.7554/eLife.06121.022
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fig4s4: Robust connectivity across subjects.For each connectivity statistics (causal density, causal outflow, or maximum flow between areas) from each structure, we compared its values (after brain map registration) between each subject pair. As results, 15 correlation coefficients were acquired for each connectivity statistics to show how similar the connectivity statistics across subjects in all structures. The average correlation coefficients for causal density, causal outflow, and maximum flow between areas were 0.66 ± 0.08, 0.60 ± 0.05, and 0.69 ± 0.26, respectively (mean ± std, n = 15: 3 pairs, 5 structures). High correlations in connectivity statistics among subjects indicate that connectivity in each structure is robust across subjects.DOI:http://dx.doi.org/10.7554/eLife.06121.022

Mentions: The five structures are shown in Figure 4 (Structures 1 and 2) and Figure 5 (Structures 3, 4, and 5). Each structure represented a unique functional network dynamics, described by its compositions in the three tensor dimensions. The first tensor dimension (panel A) represented the differences across comparisons for each structure. We identified the significant differences and reconstructed the activation levels to show how each structure was activated under different scenarios and conditions (see the ‘Materials and methods’). The second tensor dimension (panel B) represented spectro-temporal dynamics for each structure. The third tensor dimension (panel C) represented the anatomical connectivity pattern for each structure. We measured three connectivity statistics: (1) causal density is the sum of all outgoing and incoming causality for each area, showing areas with busy interactions; (2) causal outflow is the net outgoing causality of each area, indicating the sources and sinks of interactions; and (3) maximum flow between areas is the maximal causality of all connections between cortical areas (7 areas found with busy interactions were chosen) (see results for individual subjects in Figure 4—figure supplements 1–3). The extracted statistics were robust across all subjects with different electrode placements suggesting that the structures were bilaterally symmetric across hemispheres (Figure 4—figure supplement 4).10.7554/eLife.06121.018Figure 4.Network structures for perception of context and response.


Cortical network architecture for context processing in primate brain.

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

Robust connectivity across subjects.For each connectivity statistics (causal density, causal outflow, or maximum flow between areas) from each structure, we compared its values (after brain map registration) between each subject pair. As results, 15 correlation coefficients were acquired for each connectivity statistics to show how similar the connectivity statistics across subjects in all structures. The average correlation coefficients for causal density, causal outflow, and maximum flow between areas were 0.66 ± 0.08, 0.60 ± 0.05, and 0.69 ± 0.26, respectively (mean ± std, n = 15: 3 pairs, 5 structures). High correlations in connectivity statistics among subjects indicate that connectivity in each structure is robust across subjects.DOI:http://dx.doi.org/10.7554/eLife.06121.022
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fig4s4: Robust connectivity across subjects.For each connectivity statistics (causal density, causal outflow, or maximum flow between areas) from each structure, we compared its values (after brain map registration) between each subject pair. As results, 15 correlation coefficients were acquired for each connectivity statistics to show how similar the connectivity statistics across subjects in all structures. The average correlation coefficients for causal density, causal outflow, and maximum flow between areas were 0.66 ± 0.08, 0.60 ± 0.05, and 0.69 ± 0.26, respectively (mean ± std, n = 15: 3 pairs, 5 structures). High correlations in connectivity statistics among subjects indicate that connectivity in each structure is robust across subjects.DOI:http://dx.doi.org/10.7554/eLife.06121.022
Mentions: The five structures are shown in Figure 4 (Structures 1 and 2) and Figure 5 (Structures 3, 4, and 5). Each structure represented a unique functional network dynamics, described by its compositions in the three tensor dimensions. The first tensor dimension (panel A) represented the differences across comparisons for each structure. We identified the significant differences and reconstructed the activation levels to show how each structure was activated under different scenarios and conditions (see the ‘Materials and methods’). The second tensor dimension (panel B) represented spectro-temporal dynamics for each structure. The third tensor dimension (panel C) represented the anatomical connectivity pattern for each structure. We measured three connectivity statistics: (1) causal density is the sum of all outgoing and incoming causality for each area, showing areas with busy interactions; (2) causal outflow is the net outgoing causality of each area, indicating the sources and sinks of interactions; and (3) maximum flow between areas is the maximal causality of all connections between cortical areas (7 areas found with busy interactions were chosen) (see results for individual subjects in Figure 4—figure supplements 1–3). The extracted statistics were robust across all subjects with different electrode placements suggesting that the structures were bilaterally symmetric across hemispheres (Figure 4—figure supplement 4).10.7554/eLife.06121.018Figure 4.Network structures for perception of context and response.

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.


Related in: MedlinePlus