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Dynamic social adaptation of motion-related neurons in primate parietal cortex.

Fujii N, Hihara S, Iriki A - PLoS ONE (2007)

Bottom Line: Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other.Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations.Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Symbolic Cognitive Development, RIKEN Brain Science Institute, Wako, Japan. na@brain.riken.jp

ABSTRACT
Social brain function, which allows us to adapt our behavior to social context, is poorly understood at the single-cell level due largely to technical limitations. But the questions involved are vital: How do neurons recognize and modulate their activity in response to social context? To probe the mechanisms involved, we developed a novel recording technique, called multi-dimensional recording, and applied it simultaneously in the left parietal cortices of two monkeys while they shared a common social space. When the monkeys sat near each other but did not interact, each monkey's parietal activity showed robust response preference to action by his own right arm and almost no response to action by the other's arm. But the preference was broken if social conflict emerged between the monkeys-specifically, if both were able to reach for the same food item placed on the table between them. Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other. Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations. Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network.

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Proportion of MR neurons classified by Actor and Action Indices in positions A–C.M1's and M2's MR neurons (Rows A and B, respectively) were classified into nine groups using the Actor Index and Action Index. Each bar represents the proportions of the corresponding indices. Columns represent positions A–C. Proportions were calculated relative to the total number (shown on top of each graph) of MR neurons in each position. Bars in red indicate statistically significant modulation of that group of neurons in that position compared with the activity of the same group of neurons in position A.
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pone-0000397-g005: Proportion of MR neurons classified by Actor and Action Indices in positions A–C.M1's and M2's MR neurons (Rows A and B, respectively) were classified into nine groups using the Actor Index and Action Index. Each bar represents the proportions of the corresponding indices. Columns represent positions A–C. Proportions were calculated relative to the total number (shown on top of each graph) of MR neurons in each position. Bars in red indicate statistically significant modulation of that group of neurons in that position compared with the activity of the same group of neurons in position A.

Mentions: Figures 2 and 3 characterize MR parietal neurons using the four independent motion factors. However, these analyses do not reveal the relationships between the factors. For instance, they do not inform us how neurons responded to one motion that was made in response to other motions. Therefore, we introduced two indices—the Actor Index and the Action Index (see Materials and Methods). The Actor Index tells whose action a neuron responded to (“Self”, “Other” or “nsp”), regardless of the responding arm. The Action Index tells which arm movement neurons responded to (“Right”, “Left” or “nsp”), regardless of the actor. Applying these two indices, we categorized MR neurons into nine groups. Associations between MR response combinations and Actor/Action categories are shown in Figure 4. Figure 5 shows the proportional distribution of the nine groups of MR neurons categorized by these indices. In position A, many of the MR neurons (65% in M1; 61% in M2) exclusively responded to motion of “Self” and “Right” hand. In the analysis used in Figures 2 and 3, we could not calculate significance of modulation because neurons were counted multiple times if they showed positive responses in multiple motion factors. In contrast, in Figure 5, neurons were exclusively categorized into nine groups so that there were no multiple counts across categories. Thus, we compared the proportion of each category in position A with that in position B and then again in position C. (Fisher's exact test, p<0.05) In position B, although the proportion of each category was modulated in a pattern similar what was observed in Figures 2 and 3, no significant difference was detected. However, in position C, we found a significant decrease in the self-right category in both monkeys and a significant increase in the nsp-nsp and other-left categories in M2's MR neurons.


Dynamic social adaptation of motion-related neurons in primate parietal cortex.

Fujii N, Hihara S, Iriki A - PLoS ONE (2007)

Proportion of MR neurons classified by Actor and Action Indices in positions A–C.M1's and M2's MR neurons (Rows A and B, respectively) were classified into nine groups using the Actor Index and Action Index. Each bar represents the proportions of the corresponding indices. Columns represent positions A–C. Proportions were calculated relative to the total number (shown on top of each graph) of MR neurons in each position. Bars in red indicate statistically significant modulation of that group of neurons in that position compared with the activity of the same group of neurons in position A.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC1851098&req=5

pone-0000397-g005: Proportion of MR neurons classified by Actor and Action Indices in positions A–C.M1's and M2's MR neurons (Rows A and B, respectively) were classified into nine groups using the Actor Index and Action Index. Each bar represents the proportions of the corresponding indices. Columns represent positions A–C. Proportions were calculated relative to the total number (shown on top of each graph) of MR neurons in each position. Bars in red indicate statistically significant modulation of that group of neurons in that position compared with the activity of the same group of neurons in position A.
Mentions: Figures 2 and 3 characterize MR parietal neurons using the four independent motion factors. However, these analyses do not reveal the relationships between the factors. For instance, they do not inform us how neurons responded to one motion that was made in response to other motions. Therefore, we introduced two indices—the Actor Index and the Action Index (see Materials and Methods). The Actor Index tells whose action a neuron responded to (“Self”, “Other” or “nsp”), regardless of the responding arm. The Action Index tells which arm movement neurons responded to (“Right”, “Left” or “nsp”), regardless of the actor. Applying these two indices, we categorized MR neurons into nine groups. Associations between MR response combinations and Actor/Action categories are shown in Figure 4. Figure 5 shows the proportional distribution of the nine groups of MR neurons categorized by these indices. In position A, many of the MR neurons (65% in M1; 61% in M2) exclusively responded to motion of “Self” and “Right” hand. In the analysis used in Figures 2 and 3, we could not calculate significance of modulation because neurons were counted multiple times if they showed positive responses in multiple motion factors. In contrast, in Figure 5, neurons were exclusively categorized into nine groups so that there were no multiple counts across categories. Thus, we compared the proportion of each category in position A with that in position B and then again in position C. (Fisher's exact test, p<0.05) In position B, although the proportion of each category was modulated in a pattern similar what was observed in Figures 2 and 3, no significant difference was detected. However, in position C, we found a significant decrease in the self-right category in both monkeys and a significant increase in the nsp-nsp and other-left categories in M2's MR neurons.

Bottom Line: Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other.Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations.Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Symbolic Cognitive Development, RIKEN Brain Science Institute, Wako, Japan. na@brain.riken.jp

ABSTRACT
Social brain function, which allows us to adapt our behavior to social context, is poorly understood at the single-cell level due largely to technical limitations. But the questions involved are vital: How do neurons recognize and modulate their activity in response to social context? To probe the mechanisms involved, we developed a novel recording technique, called multi-dimensional recording, and applied it simultaneously in the left parietal cortices of two monkeys while they shared a common social space. When the monkeys sat near each other but did not interact, each monkey's parietal activity showed robust response preference to action by his own right arm and almost no response to action by the other's arm. But the preference was broken if social conflict emerged between the monkeys-specifically, if both were able to reach for the same food item placed on the table between them. Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other. Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations. Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network.

Show MeSH