Amygdala activity for the modulation of goal-directed behavior in emotional contexts

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Introduction

Goal-directed behavior is strongly influenced by the predicted outcome of choices on the basis of repeated experience with multiple objects and actions [1]. However, the outcome of choices often changes, depending on the context [2]. Thus, neurons contributing to goal-directed behavior should integrate information about both behavioral targets and context, which actually involve various brain areas [35]. It is thus difficult to understand the mechanism of context specificity: how is context information created, and how is it integrated with target information?

To address these questions, we used visual scenes as contexts, which often serve as “environment contexts” in real life [2]. For a visual scene to establish context, the subject needs to learn the predictable events, for instance, that a robber may appear [6,7]. It is known that various scenes are discriminated in particular areas in the visual cortices based on their visual features [8], but scene context (e.g., dangerous, safe, rich, poor) may be detected in different brain areas where the visual features are associated with the predictable events. One procedure to examine this mechanism is to make multiple scenes that represent a particular context, which would suggest that a context is created regardless of sensory features.

Importantly, goal-directed behavior must start sometime after the subject enters into a particular environment. Thus, the context information can be available earlier than target information, which may allow separate processing mechanisms. To utilize this temporal feature, it would be better if the environment appeared suddenly and unpredictably, which should be followed by, not preceded by, the activation of the context mechanism.

To this end, we created a new foraging task using visual scenes derived from satellite imagery as environments, each of which was presented in a large portion of the subject’s (macaque monkey) visual field. Within each scene, smaller fractal objects appeared, which the subject either reacted to or ignored. Initially, all the visual scenes and objects were novel, but after repeated experiences they started representing several groups of scenes and targets. In one block of the experiment, many scenes were presented randomly so that the context mechanism had to be activated differently each time after the scene appeared.

As the first step in studying the mechanisms of context, we recorded neuronal activity in the amygdala of monkeys performing the foraging task. The amygdala is highly sensitive to emotional stimuli or contexts [3,9,10]. This has been shown clearly by experiments using passive procedures (e.g., Pavlovian conditioning tasks), especially with fearful objects acting as conditioned stimuli [11]. On the other hand, recent studies showed that the amygdala also contributes to goal-directed behavior [12,13]. These studies together raise a hypothesis that the amygdala promotes goal-directed behavior in emotional or dangerous contexts, which is critical in real life. Our study supports this hypothesis, as shown below.

Results

How the monkey performed the foraging task is shown in Fig 1. Initially, the screen in front of the monkey was dark. Next, a large visual scene chosen from satellite imagery appeared suddenly, which acted as an environment. Each scene contained at least two fractal images (“good” and “bad” objects), which appeared one at a time and were randomized both in sequence and position (Fig 1A). In this example trial, the bad object appeared twice, but the monkey avoided it by saccading to it and then quickly looking away (gaze duration <400 ms). Then, the good object appeared, and the monkey chose it by saccading and holding fixation (FX) (gaze duration >400 ms) and obtained a reward. The scene disappeared after the monkey chose either the good object (with reward) or the bad object (with no reward), thus ending the trial. Similar example trials are shown in S1 Movie.

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Fig 1. Foraging task in environmental contexts.

(A) Example trial from monkey PA with a safe environment (#121 in Fig 1C). Top: sequence of events starting with blank screen during the ITI. Bottom: eye position (magenta: horizontal; green: vertical) and spike activity of one amygdala neuron (spike timing, blue). In this trial, the bad object appeared twice, followed by the good object (top). The monkey made saccades from the fixation point to both objects but quickly returned to the center for the bad object and kept fixating the good object to get a reward. The neuron was nearly silent (only two spikes). (B) Trial from monkey PA with a dangerous environment (#172 in Fig 1C). After the bad object, the robber object appeared and remained until the good object appeared, to which the monkey made a saccade quickly enough to trigger reward delivery. On other trials when the saccade was delayed, the robber object jumped to the good object and no reward was available. See S1 Fig and Materials and methods for detailed procedures. See also S1 and S2 Movies. The same neuron continued to be active until the reward delivery (unlike in the safe environment in A). Performing these trials correctly required the monkey to retrieve the memories of the objects contained in the scene. We refer to each scene as an “environment.” (C) Multiple sets of example scenes (n = 56) and objects (n = 140) for monkey PA. Each scene contained one good object (associated with a big or small reward) and one bad object (associated with no reward). Some scenes contained a third object that acted as a robber object (D/R) or a distractor object (some in S/R and S/P). The robber object tried to attack the good object and forestall the reward (as in S1 Fig). The distractor remained while another object (good or bad) appeared, but never attacked the other object. Each good object was consistently associated with either a big or a small reward. We thus classified these scenes into three groups. D/R: dangerous (with robber) and rich (big reward). S/R: safe (no robber) and rich (big reward). S/P: safe (no robber) and poor (small reward). The trial continued with objects appearing in random sequence and position until the monkey ended the trial by holding fixation on either the good object (followed by reward) or the bad object (followed by no reward). The scene remained until the end of the trial. These example scene images were derived from OpenAerialMap (https://openaerialmap.org). D/R, dangerous and rich; ITI, intertrial interval; S/P, safe and poor; S/R, safe and rich.


https://doi.org/10.1371/journal.pbio.2005339.g001

In another trial (Fig 1B), a different scene with different objects appeared. This scene contained a third type of object in addition to good and bad objects, which we call the “robber object.” In this example, the bad object appeared first, which the monkey avoided. Then, the robber appeared and waited for the good object. When the good object appeared, both the monkey and the robber tried to get it first. In this case, the monkey won the competition and got a reward. Had the monkey’s saccade been slower, the robber would have jumped to the good object and stolen the reward (S1 Fig). Similar procedures have been used for rodents [14,15]. These two scenes can be classified as a “safe” scene (i.e., no robber will come; Fig 1A) and a “dangerous” scene (i.e., robber may come; Fig 1B), respectively. Similar example trials are shown in S2 Movie.

Spike activity of one amygdala neuron was recorded during these trials. It was nearly silent (i.e., only fired two spikes) during the safe scene (Fig 1A, bottom) but started firing immediately after the dangerous scene appeared (Fig 1B, bottom). This result raised the possibility that amygdala neurons process scenes selectively.

However, the difference in neuronal activity or behavior could be due to different visual features of the scenes (as described in the Introduction). To address this issue, we created many scenes (together with objects) for each class of environmental context. Fig 1C shows the example scenes used for monkey PA. We classified the scenes into three groups: (1) D/R: dangerous (robber+) and rich (large reward), (2) S/R: safe (robber−) and rich (large reward), and (3) S/P: safe (robber−) and poor (small reward). We did not use the other possible environment, D/P (dangerous and poor) because we found that this combination led the monkeys to quit the task. Based on the three groups, we investigated two dimensions of emotional context, the dangerous–safe dimension (D/R versus S/R) and the rich–poor dimension (S/R versus S/P). These task dimensions roughly correspond to a common way to conceptualizing emotional dimensions, namely valence and arousal [16]. In some of the safe scenes, another type of “distractor object” could appear, which, like the robber, lingered on the screen but never attacked the reward. Task details for trials with distractor and robber objects are shown in S1 Fig.

All three monkeys learned the many combinations of scenes and objects quickly and accurately. S2A Fig shows the change in the correct choice rate (i.e., choosing good objects) when monkey PI learned four new scenes and eight new objects simultaneously. By the end of the first day of learning (13 trials for each scene, 52 total), the correct choice rate approached 100%. After 2–3-d learning sessions, his performance became almost perfect for all the four scenes. Quick learning similarly occurred for the other scenes that monkey PI experienced (n = 56), and likewise with monkeys PA and SO (S2B Fig). Average SacRT was initially about 150 ms and quickly decreased to about 100 ms, after the monkey started experiencing the foraging task (S2C Fig). The monkeys’ performance for well-practiced environments remained high after initial learning and during neuronal recording.

However, it was still unclear whether the environment context affected behavior. To address this question, we compared SacRT to good/bad objects between different contexts. Fig 2A shows an example comparison between a safe (S/R) scene (top) and a dangerous (D/R) scene (bottom). We assessed SacRT data in multiple scenes for each context (Fig 1C for monkey PA). We found that SacRT was changed by the environmental contexts (Fig 2B): shorter with dangerous scenes (black) than safe scenes (two-sample t test: P < 0.001, t = −11.086, df = 14,000), even though the reward amount was the same (i.e., big). Notably, the whole distribution of SacRTs (including <100 ms) was shifted between these contexts in monkey PA (Fig 2B). In each context (e.g., dangerous), SacRT was also influenced by the object (Fig 2C): shorter for good objects than bad objects (two-sample t test: P < 0.001, t = −10.385, df = 4,161), but only during the late period (roughly >100 ms). These data suggest that the two factors (scene and object) influenced SacRT and did so with different time courses. It is then likely that there are separate neural mechanisms for the context discrimination and for the object discrimination.

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Fig 2. SacRTs in safe and dangerous contexts.

(A) Safe scene (top, #121 in Fig 1C) and dangerous scene (bottom, #172 in Fig 1C), which appeared occasionally and randomly among many others (Fig 1C). The first object can be a good or bad object, or a robber object (in the dangerous scene). (B) Distribution of the SacRT in monkey PA to the first object (good or bad) in the dangerous (D/R) scenes (black) and the safe (S/R) scenes (red). The data are based on all scenes in each group (n = 24 for D/R, n = 20 for S/R, Fig 1C). SacRT distribution is shown using reciprobit plot, in which a straight line indicates a normal (Gaussian) distribution of the speed of the saccade preparation process [16]. (C) SacRT distribution in the dangerous scenes, shown separately for good objects and bad objects. Example scene images were derived from OpenAerialMap (https://openaerialmap.org). D/R, dangerous and rich; S/R, safe and rich; SacRT, saccade reaction time.


https://doi.org/10.1371/journal.pbio.2005339.g002

The SacRT difference between the context and object effects was present in the all monkeys (Fig 3). In order to examine these two factors cleanly, we measured SacRT for the first saccade in a trial (see Fig 2A). Second and subsequent saccades can be influenced by other factors, such as the positions or values of the preceding objects and the presence or absence of the robber/distractor objects. The environmental context affected SacRT across the entire distribution of latencies (Fig 3A), unlike the object value (Fig 3B). In monkey PA (Fig 3A, left), SacRT was shorter with D/R than S/R scenes (two-way ANOVA with scenes and objects, F[2, 18693] = 5.966, P = 0.003, post hoc: Tukey–Kramer, P < 0.001), indicating that the dangerous–safe dimension had a significant effect on SacRT. In monkey PI (Fig 3A, center), SacRT was shorter with S/R than S/P scenes (F[2, 12428] = 21.892, P < 0.001, post hoc: P < 0.001), indicating that the rich–poor dimension had a significant effect on SacRT. In monkey SO (Fig 3A, right), SacRT was different in two dimensions of context: (1) dangerous–safe dimension: shorter with D/R than S/R scenes (F[2, 27266] = 37.405, P < 0.001; post hoc: P < 0.001), and (2) rich–poor dimension: shorter with S/R than S/P scenes (post hoc: P < 0.001). These data suggest that SacRT was affected by the context, but somewhat differently in the three subjects. Interestingly, the context effect was observed in saccades to both good and bad objects, even though the monkey left the bad object quickly after making a saccade to it (to avoid no reward). These data again suggest that the context mechanism starts working early after a scene appears, regardless of the upcoming object.

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Fig 3. Distribution of SacRTs in different contexts and for different targets.

(A) Distributions of the SacRT for three groups of scenes are superimposed: D/R, S/R, and S/P (see Fig 1C). They are shown separately for the good and bad objects. Data are shown for monkeys PA (left), PI (center), and SO (right) after learning (>350, >200, and >200 trials for each environment, respectively). (B) The same SacRT data are shown separately for the three groups of scenes (D/R, S/R, S/P), but data for good (G) and bad (B) objects are superimposed. The numbers of saccades examined are shown in each graph. The effects of scenes (A) and objects (B) on SacRT were independent and orthogonal in all subjects (two-way ANOVA, monkey PA [scene, F(2,18693) = 68.004, P < 0.001; object, F(1,18693) = 381.645, P < 0.001; scene*object, F(2,18693) = 5.966, P = 0.003], monkey PI [scene, F(2,12428) = 18.313, P < 0.001; object, F(1,12428) = 1,216.167, P < 0.001; scene*object, F(2,12428) = 21.892, P < 0.001], monkey SO [scene, F(2,27266) = 84.198, P < 0.001; object, F(1,27266) = 584.585, P < 0.001; scene*object, F(2, 27266) = 37.405, P < 0.001]). Data used to generate these plots can be found at https://osf.io/2yq8p/?view_only=97c4b290514348bb91cdbb9ec1c85e09. B, bad; D/R, dangerous and rich; G, good; S/P, safe and poor; S/R, safe and rich; SacRT, saccade reaction time.


https://doi.org/10.1371/journal.pbio.2005339.g003

SacRT was also influenced by the object (i.e., shorter for good than bad objects) (Fig 3B) regardless of the scene, but only for the right tail of the distribution (roughly >100 ms). These data suggest two separate neural mechanisms for the scene and object effects. First, the object-processing neurons cannot identify the object’s value immediately after the object appears, until about 100 ms [17]. Second, the scene-processing neurons affect the saccade preparatory process before the object appears, because the scene is already present. That the reciprobit plots showed nearly parallel distributions across environment contexts (Fig 3A) suggests that the speed (rather than threshold) of saccade preparation is changed by the contexts [18], namely faster saccades when the scene was dangerous or rich.

These behavioral data suggest that in our foraging task, context-processing neurons should change their activity after the scene appears. Indeed, we found many such neurons in the amygdala. Fig 4 shows the responses of one example neuron in monkey PA to the appearance of many scenes, which were classified in three groups (Fig 4D). This neuron is the same as shown in Fig 1. It started firing in response to D/R scenes but was almost silent when the scene was S/R or S/P. These data suggest that the neuron was sensitive to one dimension of context: dangerous–safe dimension (D/R versus S/R in free-viewing [FV] period, one-way ANOVA, F[2, 29] = 17.932, P < 0.001, post hoc: Tukey–Kramer, P < 0.001; D/R versus S/R in FX period, one-way ANOVA, F[2, 29] = 18.628, P < 0.001, post hoc: Tukey–Kramer, P < 0.001) (Fig 4C, dangerous versus safe). It was not significantly sensitive to the other dimension: rich–poor dimension (S/R versus S/P in FV period, post hoc: Tukey–Kramer, P = 0.935; S/R versus S/P in FX period, post hoc: Tukey–Kramer, P = 0.983) (Fig 4C, rich versus poor). The dangerous–safe difference started quickly, 157 ms after the scene onset (Fig 4B, black triangle). These results suggest that the neuron processed the environmental context in the dangerous–safe dimension.

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Fig 4. Responses of one example neuron in the amygdala to scene environments.

Neuron (#73) in monkey PA that was selectively active in the dangerous context (partially shown in Fig 1). (A–B) The neuron’s activities in the three groups of scenes: dangerous (D/R), rich (S/R), poor (S/P). They are shown separately as spike rasters (A) and are superimposed as SDF. (B). For SDF, each spike was replaced by a Gaussian curve (σ = 10 ms) in this and the following figures. Triangle indicates the onset of the scene response (white) and the onset of the context-dependent differentiation (black). (C) Time course of the neuron’s activity bias in two dimensions of context: (1) dangerous–safe (D versus S): difference in activity between D/R and S/R, (2) rich–poor (R versus P): difference in activity between S/R and S/P. In each dimension, the difference of activity (ΔFR score) was calculated in a sliding 300-ms window (10 ms steps) if it was statistically significant (tested by one-way ANOVA and Tukey–Kramer post hoc test); otherwise the score was put as 0. Then, the scores were divided by the maximum of the difference during free-viewing and fixation periods. ΔFR > 0: D > S (top) and R > P (bottom). (D) The neuron’s response (z-score) to individual scenes in the three groups examined (see Fig 1C). In this and the following figures, the neuronal data are focused on the activity before the first object appeared (see Fig 1A and 1B). This period was divided into the two parts: free-viewing period (from scene onset to fixation start) (left) and fixation period (from fixation start to object onset) (right). Data used to generate these plots can be found at https://osf.io/2yq8p/?view_only=97c4b290514348bb91cdbb9ec1c85e09. D/R, dangerous and rich; FR, firing rate; S/P, safe and poor; S/R, safe and rich; SDF, spike density function.


https://doi.org/10.1371/journal.pbio.2005339.g004

Alternatively, the difference in the neuronal activity might be caused by the different visual features between the environments. This is one reason we used many visual scenes to represent the same context (Fig 1C). In fact, the neuron’s activity was stronger in the D/R context than the S/R or S/P context regardless of the scene-based differences (assessed in FV periods, one-way ANOVA, F[2, 29] = 17.932, P < 0.001; in FX period, one-way ANOVA, F[2, 29] = 18.628, P < 0.001) (Fig 4D). Notably, the neuron’s activity was variable across scenes within the same context, which is evident in D/R context (Fig 4D). Its significance will be examined later (S5 Fig).

This neuron was recorded in monkey PA, whose SacRT was shorter in the dangerous than in the safe context (Figs 2 and 3A, left). According to the reciprobit plot [18], this was caused by the difference in speed of the saccade preparation process. To achieve such a speed increase, the saccade generator (e.g., superior colliculus [SC]) should receive modulatory inputs before the preparation process starts. The neuron in Fig 4 may thus contribute to the faster saccades in the dangerous context. Its activity actually further increased toward the end of the FX period (Fig 4B), after which an object appeared and a saccade occurred.

S3 Fig shows the activity of two amygdala neurons in the other monkeys. The first neuron (S3A Fig) was very active spontaneously. It was first inhibited by virtually all scenes (latency: 94 ms). Its activity then became differential (latency: 416 ms), higher with the rich than the poor scenes (S/R versus S/P in FV period, one-way ANOVA, F[2, 29] = 7.003, P = 0.003, post hoc: Tukey–Kramer, P = 0.015; S/R versus S/P in FX period, one-way ANOVA, F[2, 29] = 8.994, P = 0.001, post hoc: Tukey–Kramer, P = 0.001), but not different between the dangerous and safe scenes (D/R versus S/R in FV period, P = 0.783; D/R versus S/R in FX period, P = 0.747). This neuron was recorded in monkey PI, whose SacRT was shorter with the rich than the poor scenes (Fig 3A, center). The neuron may thus contribute to the faster saccades in the rich context.

The second neuron (S3B Fig) was first activated by virtually all scenes (latency: 76 ms). Its differential activity evolved later (latency: 201 ms for S/P and 1,662 ms for D/R in comparison with S/R) in two dimensions, namely (1) dangerous–safe dimension (D/R versus S/R in FV period, one-way ANOVA, F[2, 29] = 4.407, P = 0.021, post hoc: Tukey–Kramer, P = 0.993; D/R versus S/R in FX period, one-way ANOVA, F[2, 29] = 17.229, P < 0.001, post hoc: Tukey–Kramer, P = 0.008) and (2) rich–poor dimension (S/R versus S/P in FV period, post hoc: Tukey–Kramer, P = 0.030; S/R versus S/P in FX period, post hoc: Tukey–Kramer, P = 0.018). This neuron was recorded in monkey SO, whose SacRT was different in the same two dimensions: (1) D/R < S/R and (2) S/R < S/P. The neuron may thus contribute to the faster saccades in the dangerous and rich contexts.

The combined activity of amygdala neurons in monkeys PA, PI, and SO is shown in Fig 5. After the scene appeared, a majority of the neurons increased their activity (excited type, Fig 5B top), while some neurons eventually decreased their activity (inhibited type, Fig 5B bottom) (Fig 5A). Many of them were activated (or inhibited) immediately after the scene appeared (S4 Fig). Their activity then diverged depending on the context: dangerous–safe dimension (D versus S) and/or rich–poor dimension (R versus P). This occurred more clearly among excited-type neurons (Fig 5B, top). The context effect sometimes changed between the early FV period and the late FX period. During the FV period, the neuronal activity tended to be higher in the rich (red) than poor (blue) context, which was statistically significant in all three monkeys (one-way ANOVA and Tukey–Kramer post hoc test; PA: F[2, 53] = 4.984, P = 0.01, post hoc, P = 0.019; PI: F[2, 53] = 6.937, P = 0.002, post hoc, P = 0.002; SO: F[2, 29] = 103.568, P < 0.001, post hoc, P < 0.001). The significant sensitivity to the dangerous–safe dimension (i.e., black versus red) emerged later in the FX period in two monkeys, PA (F[2, 53] = 25.924, P < 0.001, post hoc, P < 0.001) and SO (F[2, 29] = 51.747, P < 0.001, post hoc, P < 0.001). Inhibited-type neurons (Fig 5B, bottom) overall were clearly inhibited only during the late period (FX). Their inhibitory responses were not clearly related to the context-dependent modulation of SacRT (PA in FV: F[2, 53] = 6.569, P = 0.003 [D versus S: P = 0.017; R versus P: P = 0.753]; PA in FX: F[2, 53] = 0.868, P = 0.426 [D versus S: P = 0.449; R versus P: P = 0.995]; PI in FV: F[2, 53] = 4.846, P = 0.012 [D versus S: P = 0.014; R versus P: P = 0.933]; PI in FX: F[2, 53] = 2.239, P = 0.117 [D versus S: P = 0.167; R versus P: P = 0.994]; SO in FV: F[2, 29] = 2.916, P = 0.070 [D versus S: P = 0.558; R versus P: P = 0.056]; SO in FX: F[2, 29] = 5.427, P = 0.010 [D versus S: P = 0.008; R versus P: P = 0.143]), unlike excited type neurons.

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Fig 5. Responses of amygdala neurons in three monkeys to scene environments.

(A) The numbers of neurons (excited type, inhibited type, others) in monkeys PA, PI, and SO. (B) Average activities in the three groups of environments: dangerous (D/R), rich (S/R), and poor (S/P). They are shown separately for the excited-type neurons (top) and the inhibited-type neurons (bottom) for each monkey. The averaging was based on the normalized z-scores of individual neurons (see Materials and methods). Shaded gray area indicates the free-viewing (left) and fixation (right) periods. (B, bottom) Time course of the activity biases of individual neurons in two dimensions of environmental context: dangerous–safe dimension (D versus S) and rich–poor dimension (R versus P) (same format as Fig 4C). Individual neuron data are sorted by their mean ΔFR scores in the fixation period, high to low scores. In each dimension, the averaged ΔFR scores are shown by a cyan line with black dots. Data used to generate these plots can be found at https://osf.io/2yq8p/?view_only=97c4b290514348bb91cdbb9ec1c85e09. D/R, dangerous and rich; FR, firing rate; S/P, safe and poor; S/R, safe and rich.


https://doi.org/10.1371/journal.pbio.2005339.g005

These data raise the possibility that amygdala neurons, as a population, affect saccadic eye movements based on the rich–poor and dangerous–safe dimensions of context. To further address this question, we compared the neuronal activity during the FX period and SacRT (Fig 6A and 6B), because the FX period is immediately before the saccade (Fig 2A). The effect of the rich–poor context was evaluated by the difference between the poor (S/P) scenes (blue) and the rich (S/R) scenes (red). In monkeys PI and SO, neuronal activity was higher, while SacRT was shorter with the rich than poor scenes (one-way ANOVA and Tukey–Kramer post hoc test; activity in PI: P = 0.006; activity in SO: P < 0.001; SacRT in PI: P < 0.001; SacRT in SO: P < 0.001, S1 Table).

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Fig 6. Amygdala neuronal activity and SacRT modulated by two dimensions of scene context.

(A–B) Neuronal activity during the fixation period (A) and SacRT (B) in three monkeys (PA, PI, SO), shown separately for three groups of scenes: S/P, D/R, and S/R. The data (ordinate) are plotted against the predicted reward value (abscissa), which was defined as: reward amount × success rate (see Materials and methods). Neuronal activity was based on the normalized z-scores; SE is shown by a vertical bar. Asterisk (*) indicates statistically significant contrasts at P < 0.05 (scene context: one-way ANOVA, post hoc: Tukey–Kramer [see S1 Table]; predicted versus actual in D/R: one-sample t test). (C) Relation between the neuronal activity (abscissa) and SacRT (ordinate) for individual scenes. Statistics (Pearson’s correlation) are shown in each graph. The color of each data point indicates the scene context, as used in A and B. Data are based on excited-type neurons (Fig 5). Data used to generate these plots can be found at https://osf.io/2yq8p/?view_only=97c4b290514348bb91cdbb9ec1c85e09. D/R, dangerous and rich; S/P, safe and poor; S/R, safe and rich; SacRT, saccade reaction time.


https://doi.org/10.1371/journal.pbio.2005339.g006

Additionally, the dangerous (D/R) scenes (black) affected the neuronal activity and SacRT. In monkeys PA and SO, neuronal activity with the dangerous (D/R) scenes was higher than the safe scenes, either rich (S/R) or poor (S/P) scenes (activity in PA: P < 0.001; activity in SO: P < 0.001), while SacRT was shorter than the safe scenes (SacRT in PA: P < 0.001; SacRT in SO: P < 0.001).

In Fig 6A and 6B, the neuronal activity and SacRT are plotted against the predicted reward value. Although the reward volume per delivery event was the same between the dangerous and rich scenes (S1A Fig), the predicted reward value was smaller with the dangerous scenes because the reward was sometimes removed by the robber object (S1C Fig). Then, the effect of dangerous context (yellow circle) can be estimated by comparing the safe context with the same predicted reward value (pink circle). According to this analysis, even in monkey PI, the neuronal activity tended to be higher (one-sample t test, PA: P < 0.001, t = 9.628; PI: P = 0.22, t = 1.261; SO: P < 0.001, t = 6.518) and SacRT shorter (one-sample t test, PA: P < 0.001, t = −31.155; PI: P < 0.001, t = −6.863; SO: P < 0.001, t = −18.492) with dangerous than with safe scenes.

These data together suggest that the two dimensions of emotional context (i.e., rich–poor, dangerous–safe) worked independently to affect the neuronal activity and SacRT, and they did so somewhat differently across subjects. Importantly, both the rich and dangerous scenes increased the neuronal activity and decreased SacRT. In fact, there was a significant negative correlation between the neuronal activity and SacRT among all three groups of scenes (poor, rich, dangerous) in all the monkeys (Fig 6C). These data suggest that both rich and dangerous scenes activated amygdala neurons, which in turn led to the facilitation of saccades. Because the context starts working early after a scene appears (but before an object appears), saccades to both good and bad objects were facilitated (Fig 3).

Even though many of these amygdala neurons showed context-dependent activity, their activity was often variable or selective across scenes within the same context (Fig 4). Notably, such variability was different across neurons that are sensitive to the same context in the same monkey (S5A Fig). Presumably, based on the variable variability, amygdala neurons as a population were less variably active in different scenes that belonged to a particular context (S5B Fig). Overall, the scene selectivity tended to be lower in the population activity than in individual neuronal activity, especially before the first saccade (FX period) (S5C Fig) (one-sample t test; PA[D/R]: P < 0.001, t = 5.435; PA[S/R]: P = 0.104, t = –1.653; PA[S/P]: P = 0.986, t = –0.017; PI[D/R]: P = 0.068, t = 1.868; PI[S/R]: P = 0.012, t = 2.606; PO[S/P]: P = 0.023, t = –2.345; SO[D/R]: P = 0.042, t = 2.084; SO[S/R]: P < 0.001, t = 4.750; SO[S/P]: P < 0.001, t = 7.091).

Notably, any context is based on the behavioral outcome associated with each environment. What happens if the outcome is changed? To address this question, we let the subjects experience some of the well-learned scenes, but with a different outcome (i.e., no object, no robber) in a nonforaging task and examined some danger-sensitive neurons (S6A Fig). Although the nonforaging task was presented separately from the foraging task, these neurons were still activated by the dangerous scenes immediately after their appearance in the nonforaging task (S6C, S6E and S6G Fig). Moreover, they expressed scene selectivity that was similar to the selectivity in the foraging task. These results suggest that amygdala neurons can be activated automatically when the subject encounters emotional environments that are no longer associated with emotional events. Their activity decreased quickly, however, suggesting that these automatic responses were suppressed by subsequent events in the trial.

Our data so far have shown that the effects of the environment context were somewhat different across subjects. Does this mean that they have different sensitivities to emotion? To address this question, we compared pupil size and heart rate across the three groups of scenes (Fig 7). The pupil size was affected by both dimensions of context (Fig 7A): larger with the rich than the poor scenes and also larger with the dangerous than the safe scenes. This result was seen in all subjects, suggesting that all of the monkeys were sensitive to both richness and danger. The heart rate (Fig 7B) was higher with the rich than the poor scenes in monkeys PI and SO (but not PA) and with the dangerous than safe scenes in monkey PA and SO (but not PI). This result followed the same pattern seen with amygdala neuronal activity and SacRT (Fig 6) and raises the possibility that both heart rate and saccades are modulated by amygdala neurons, based on the environment context.

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Fig 7. Pupil size and heart rate modulated by two dimensions of environmental context.

Pupil size (A) and heart rate (B) during the fixation period in all three monkeys, shown separately for three groups of environments: S/P, D/R, and S/R. The physiological measures (ordinate) are plotted against the predicted reward value (abscissa), defined as reward volume × success rate (see S2D Fig). Same format as Fig 6A and 6B. Asterisk (*) indicates statistically significant contrasts at P < 0.05 (environment context: one-way ANOVA, post hoc: Tukey–Kramer [see S2 Table]; predicted versus actual in D/R: one-sample t test, pupil size [PA]: P < 0.001, t = 14.962, pupil size [PI]: P < 0.001, t = 10.639, pupil size [SO]: P < 0.001, t = 39.775; heart rate [PA]: P = 0.001, t = 2.571; heart rate [PI]: P = 0.986, t = 0.017; heart rate [SO]: P = 0.018, t = 2.377). Data used to generate these plots can be found at https://osf.io/2yq8p/?view_only=97c4b290514348bb91cdbb9ec1c85e09. D/R, dangerous and rich; n.s., not significant; S/P, safe and poor; S/R, safe and rich.


https://doi.org/10.1371/journal.pbio.2005339.g007

The mere presence of dangerous scenes affected SacRT to the first object as well as amygdala neuronal activity preceding the saccade in monkeys PA and SO (Fig 6). However, the appearance of an actual robber was a more threatening event. We refer to the presence or absence of a robber object as “object context.” Additionally, some trials with safe scenes contained a distractor object that stayed on the screen but never robbed a good object (S1 Fig). We defined “object context” as the presence or absence of robber or distractor objects. We then examined the effect of object context over and above effects of danger and richness considered thus far (S7 Fig). The data suggest that amygdala neurons could facilitate saccades based on the object context, in addition to the scene context. This is explained in detail in the legend of S7 Fig.

Finally, we estimated the locations of these neurons by the 3D coordinates of the recording sites that were aligned on magnetic resonance (MR) images (Fig 8). Neurons responding to the visual environments (scenes) were located in various areas in the amygdala, presumably including the central (CE), lateral (L), and basolateral (BL) nuclei (Fig 8B). Neurons that were sensitive to the dangerous–safe dimension of context (D > S; S > D) were located mainly in CE and sparsely in BL and L (Fig 8C). Neurons that were sensitive to the rich–poor dimension of context (R > P; P > R) seem localized in CE (Fig 8D). Compared with neurons in BL and L, neurons in CE had more variable features, including the background firing rate (S8C and S8D Fig) and scene selectivity (S8E and S8F Fig).

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Fig 8. Estimated positions of recorded neurons.

Recording sites are shown in five coronal MR images spanning 0–4 mm posterior to the AC. (A) Amygdala and surrounding brain areas. (B–D) The neurons’ positions are shown, based on different features. (B) Neurons excited and inhibited by visual scenes during the fixation period, and neurons showing no response to the scenes (Visual −). (C) Excited-type neurons showing significantly different activity between dangerous (D) and safe (S) contexts (D > S; S > D), and others (D = S). (D) Excited-type neurons showing significantly different activity between rich (R) and poor (P) contexts (R > P; P > R), and others (R = P). AC, anterior commissure; BL, basolateral complex of the amygdala; CE, central nucleus of the amygdala; D, dangerous; HP, hippocampus; L, lateral nucleus of the amygdala; LV, lateral ventricle; MR, magnetic resonance; OC, optic chiasm; OT, optic tract; P, poor; PU, putamen; R, rich; S, safe.


https://doi.org/10.1371/journal.pbio.2005339.g008

Supporting information

S2 Fig. Fast learning of environmental contexts and object values.

(A) Learning across 4-d sessions in monkey PI during the foraging task (4 scenes with 8 objects). Before this experiment the subject had learned the task rule completely, but all scenes and objects were completely new on Day 1. Each session consisted of 52 trials (13 trials for each scene). In the early stage of learning on Day 1, the subject’s choice was sometimes wrong (i.e., bad object) or invalid (i.e., fixation break) but became nearly perfect toward the end of the session. The speed of learning is shown as the change in the correct response rate. The good performance was well retained across days. These example scene images were derived from OpenAerialMap (https://openaerialmap.org). (B–D) Learning in three monkeys (PA, PI, SO). (B) Time course of object choice learning for many scenes (56 scenes for monkey PA and PI, 32 scenes for monkey SO). (C) Change in saccade reaction time to the good object across learning. For each subject, data are shown separately for three scene types: D/R, S/R, and S/P. Trial number was measured for individual scenes, not the subject’s task career. (D) Performance after learning (>200 trials for each scene) in the three groups of scenes. “Failure” indicates the D/R context trials in which the robber beat the monkey’s saccade. Dangerous trials featured the appearance of the robber; those that did are indicated in light gray. D/R, dangerous and rich; S/P, safe and poor; S/R, safe and rich.

https://doi.org/10.1371/journal.pbio.2005339.s002

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S5 Fig. (Related to Fig 4D). Scene selectivity of individual and population neuronal activity.

(A) Scene selectivity of two example neurons in monkey PA. Left: the neuron’s activities in the three groups of scenes: D/R, S/R, S/P. Right: the neuron’s responses to individual scenes in the dangerous (D/R) context (in the fixation period). Same format as in Fig 4B and 4D. Shaded gray area indicates the tested scenes for each neuron. Scene selectivity (SI): 0.473 (#73), 0.775 (#83). (B) Scene selectivity of the population neuronal activity (excited-type neurons, n = 33) in D/R context in monkey PA (SI: 0.234). Same format as in (A). (C) SIs in individual neuronal activity (small dots) and the population neuronal activity (gray squares) in three contexts in each monkey (PA, PI, SO). Mean SI: 0.386 (PA[D/R]), 0.404 (PA[S/R]), 0.345 (PA[S/P]), 0.299 (PI[D/R]), 0.298 (PI[S/R]), 0.264 (PI[S/P]), 0.245 (SO[D/R]), 0.305 (SO[S/R]), 0.311 (SO[S/P]). Population SI: 0.234 (PA[D/R]), 0.444 (PA[S/R]), 0.346 (PA[S/P]), 0.261 (PI[D/R]), 0.248 (PI[S/R]), 0.308 (PI[S/P]), 0.203 (SO[D/R]), 0.180 (SO[S/R]), 0.145 (SO[S/P]). On the right, the probabilistic distribution of SIs is shown by kernel density estimation (width = 0.03) for each context and in each monkey. D/R, dangerous and rich; S/P, safe and poor; S/R, safe and rich; SI, selectivity index.

https://doi.org/10.1371/journal.pbio.2005339.s005

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S7 Fig. Effects of dangerous or distracting objects on amygdala neuronal activity and SacRT.

(A) Distributions of the SacRT to good objects in three monkeys (PA, PI, SO) in three groups of scenes: S/P, D/R, S/R. In each panel, data from two groups of object context are superimposed: Robber(+) and Robber(−) in D/R, and Distractor(+) and Distractor(−) in S/R and S/P. SacRT distribution is shown using reciprobit plot. (B) Relation between the neuronal activity (abscissa) and SacRT (ordinate) in two dimensions of context: environment contexts (D/R, S/R, S/P) and object context (Robber/Distractor[+], Robber/Distractor[−]). The object contexts shown here correspond to data shown in (A). (C) Relation between the neuronal activity (abscissa) and SacRT (ordinate) for individual scenes. Some scenes provide two data points: Robber/Distractor(+) and Robber/Distractor(−). Findings are presented in further detail below. In the dangerous scene (D/R), SacRT was shorter when a robber object was present (Robber+) than when absent (Robber−) (B). The distribution of SacRT became curved, indicating that the saccade preparation process became non-Gaussian by including extremely short SacRTs (A, D/R). Notably, these effects occurred in the all monkeys. Taken together with data shown in B, these results show that all monkeys were sensitive to danger, in both scene and object (i.e., robber present or absent) contexts. Monkey PI was primarily sensitive to the object context (faster when a robber object was present), in tandem with amygdala neurons tending to be more active on Robber(+) trials than on Robber(−) (B), although the statistical significance was shown only in monkey SO. Results of two-way ANOVA tests with environments and object+/− with Tukey–Kramer post hoc tests were as follows. Monkey PA: F[2, 1882] = 0.025, P = 0.975, post hoc, P = 0.811. Monkey PI: F[2, 1773] = 1.149, P = 0.317, post hoc, P = 0.882. Monkey SO: F[2, 1958] = 0.425, P = 0.654, post hoc, P = 0.0422. In the safe scenes (S/R, S/P), SacRT was shorter when a distractor object was present than when absent in monkeys PA and SO (B). Because the success rate (i.e., probability of rewarded trials) was not significantly different between the distractor present and distracter absent trials (PA[S/R]: chi-squared = 0.286, P = 0.593; PA[S/P]: chi-squared = 0.225, P = 0.636; PI[S/R]: chi-squared = 0.205, P = 0.651; PI[S/P]: chi-squared = 0.020, P = 0.887; SO[S/R] chi-squared = 3.274, P = 0.070; SO[S/P]: chi-squared = 2.954, P = 0.086), the change in SacRT is unlikely to be caused by the dangerous context. Instead, it may be related to a higher demand of attention when two objects are present simultaneously [19]. Amygdala neurons tended to be more active when a distractor object was present (Distractor+) than when absent (Distractor−) in monkeys PA and SO (B), although this trend did not reach statistical significance (two-way ANOVA with environments and object+/−, post hoc: Tukey–Kramer; PA[S/R]: P = 0.965; PA[S/P]: P = 0.890; PI[S/R]: P = 0.930; PI[S/P]: P = 0.998; SO[S/R]: P = 0.483; SO[S/P]: P = 0.716). The integration of neuron-behavior data in the object context (i.e., presence and absence) (C) shows that the activity of amygdala neurons was significantly correlated with SacRT in monkeys PA and SO (PA: r = −0.288, P = 0.005; PI: r = 0.022, P = 0.833; SO: r = −0.660, P < 0.001). These data suggest that amygdala neurons could facilitate saccades based on the object context, in addition to the scene context. D/R, dangerous and rich; S/P, safe and poor; S/R, safe and rich; SacRT, saccade reaction time.

https://doi.org/10.1371/journal.pbio.2005339.s007

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S8 Fig. (Related to Fig 8). Recording sites.

(A) Amygdala and surrounding brain areas. Same format as in Fig 8A. A horizontal black line indicates the dorsoventral border of neurons based on their background firing rate (data shown in D). (B) Neurons with visual responses (Visual +) and with no visual responses (Visual −), shown separately for three monkeys. (C) Visual scene-sensitive neurons (excited type) are classified based on their background FRs. (D) Background FRs of individual neurons (abscissa) plotted against their recorded depths from the AC (ordinate). The horizontal line (i.e., 8 mm below AC) indicates the dorsoventral border, by which the variance of Background FR was maximally higher in the dorsal area than the ventral area. This was determined by the lowest P value (two-sample F-test) while moving the border line (0.2 mm step) (as shown in colored bars on the right) (F[108, 48] = 3.363, P < 0.001, dorsal [355.856], ventral [105.812]). Note that the border line roughly corresponds to the border between CE and BL/L. (E-F) SIs of individual neurons (abscissa) plotted against their recorded depths (ordinate) during the free-viewing period (E) and the fixation period (F). With the border based on Background FR (D), the variance of SIs was significantly higher in the dorsal area than the ventral areas (SI in free-viewing: F[324,146] = 1.958, P < 0.001, dorsal [0.030], ventral [0.015]; SI in fixation: F[326,146] = 1.476, P = 0.008, dorsal [0.033], ventral [0.023]). AC, anterior commissure; BL/L, basolateral complex and lateral nucleus of the amygdala; CE, central nucleus of the amygdala; FR, firing rate; SI, Selectivity index.

https://doi.org/10.1371/journal.pbio.2005339.s008

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