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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.


Independent component analysis (ICA) results from 3 subjects.The spatial distribution of each IC and its time course are shown for each subject. For each IC, the size of each circle represents the relative contribution of the activity from the electrode to the IC, where red and blue colors represent the positive and negative contributions, respectively. For clarity, the time courses shown were obtained by averaging source signals of the IC over one trial type (CmRf trials under C+ condition), and the y-axis is not shown. For each time course, three red vertical lines represent the events (a), (d) and (e) described in Figure 2. The ICs that were removed from analysis are labeled (see Table 1).DOI:http://dx.doi.org/10.7554/eLife.06121.015
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fig3s1: Independent component analysis (ICA) results from 3 subjects.The spatial distribution of each IC and its time course are shown for each subject. For each IC, the size of each circle represents the relative contribution of the activity from the electrode to the IC, where red and blue colors represent the positive and negative contributions, respectively. For clarity, the time courses shown were obtained by averaging source signals of the IC over one trial type (CmRf trials under C+ condition), and the y-axis is not shown. For each time course, three red vertical lines represent the events (a), (d) and (e) described in Figure 2. The ICs that were removed from analysis are labeled (see Table 1).DOI:http://dx.doi.org/10.7554/eLife.06121.015

Mentions: To analyze the large-scale ECoG dataset, we identified cortical areas over the 128 electrodes in the array by independent component analysis (ICA). Each independent component (IC) represented a cortical area with statistically independent source signals (Figure 3—figure supplement 1, and experimental parameters in Table 1).10.7554/eLife.06121.013Table 1.


Cortical network architecture for context processing in primate brain.

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

Independent component analysis (ICA) results from 3 subjects.The spatial distribution of each IC and its time course are shown for each subject. For each IC, the size of each circle represents the relative contribution of the activity from the electrode to the IC, where red and blue colors represent the positive and negative contributions, respectively. For clarity, the time courses shown were obtained by averaging source signals of the IC over one trial type (CmRf trials under C+ condition), and the y-axis is not shown. For each time course, three red vertical lines represent the events (a), (d) and (e) described in Figure 2. The ICs that were removed from analysis are labeled (see Table 1).DOI:http://dx.doi.org/10.7554/eLife.06121.015
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4584448&req=5

fig3s1: Independent component analysis (ICA) results from 3 subjects.The spatial distribution of each IC and its time course are shown for each subject. For each IC, the size of each circle represents the relative contribution of the activity from the electrode to the IC, where red and blue colors represent the positive and negative contributions, respectively. For clarity, the time courses shown were obtained by averaging source signals of the IC over one trial type (CmRf trials under C+ condition), and the y-axis is not shown. For each time course, three red vertical lines represent the events (a), (d) and (e) described in Figure 2. The ICs that were removed from analysis are labeled (see Table 1).DOI:http://dx.doi.org/10.7554/eLife.06121.015
Mentions: To analyze the large-scale ECoG dataset, we identified cortical areas over the 128 electrodes in the array by independent component analysis (ICA). Each independent component (IC) represented a cortical area with statistically independent source signals (Figure 3—figure supplement 1, and experimental parameters in Table 1).10.7554/eLife.06121.013Table 1.

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.