Animals can draw from a large repertoire of innate and learned actions to generate behavioral output (Whishaw and Kolb, 2005). Even well-trained rodents sometimes engage in sleep, grooming, exploration, or other behaviors during laboratory tasks. When hungry, however, food rewards usually have sufficient value to motivate task engagement in lieu of these other options. Effort, reward, and other factors pertinent for decisions about resource collection are often formalized within the concept of utility (Phillips et al., 2007; Glimcher et al., 2009). Options requiring low effort and yielding a large food reward have high utility to hungry animals, whereas options requiring high effort or yielding little/unwanted food have low utility. Animals often seek to maximize utility, even if it requires exerting additional effort. For instance, rodents and primates typically choose to exert increased effort if the associated reward is of considerably higher value than that of lower-effort options (Salamone et al., 1994; Hosokawa et al., 2013). This preference is plastic. Moderate doses of d-amphetamine (AMPH) increases the amount of work rodents will exert for reward (Floresco et al., 2008b; Bardgett et al., 2009). It also increases engagement in learned food-seeking behaviors (Foltin, 2001; Odum and Shahan, 2004). The most obvious explanation for these effects according to utility theory is that AMPH increases the perceived value of reward. Behavioral data, however, suggest that reward valuation is decreased. AMPH-treated rats are less motivated to eat; their latency to first consumption is longer, they consume less than usual, and they spend less time eating (Blundell et al., 1976; Blundell et al., 1979; Leibowitz et al., 1986). Suppression of food-intake by AMPH is also evident in primates (Foltin, 2001). Why do animals work harder for food they appear less motivated to consume? It is possible that AMPH somehow affects the perceived cost of effort, or that increased task engagement is a by-product of motoric hyperactivity. But neither of these provide a robust explanation for the full suite of effects, including the drastic shift of responding to grooming and stereotyped outputs at high doses (Randrup et al., 1963; Randrup and Munkvad, 1967). The behavioral data, therefore, do not support a clear prediction about how AMPH may influence the neural encoding of utility.

Cost-benefit decisions engage a network of brain structures, and the anterior cingulate cortex (ACC) appears particularly important for those involving physical effort (Rudebeck et al., 2006; Floresco et al., 2008a). The preference of rats for high-effort, high-reward options is reduced or eliminated by ACC lesions (Walton et al., 2002; Walton et al., 2003; Schweimer and Hauber, 2005; Holec et al., 2014). Electrophysiological recordings indicate that rat ACC and nearby regions in the medial prefrontal cortex (mPFC) encode a variety of signals related to choice and task execution. These include the position of the animal (Euston and McNaughton, 2006; Fujisawa et al., 2008; Mashhoori et al., 2018), task phase (Lapish et al., 2008; Balaguer-Ballester et al., 2011), reward (Gruber et al., 2010; Cowen et al., 2012), choice (Cowen et al., 2012), effort (Cowen et al., 2012; Hashemniayetorshizi et al., 2015), and other features (Cowen and McNaughton, 2007; Gruber et al., 2009; Durstewitz et al., 2010; Sul et al., 2010). Some ACC neurons also jointly encode costs-benefit information, which is well suited to utility signaling (Hillman and Bilkey, 2010; Cowen et al., 2012). These data are consistent with findings in monkeys and humans (Isomura et al., 2003; Kennerley et al., 2006; Croxson et al., 2009; Skvortsova et al., 2014; Blanchard et al., 2015; Klein-Flügge et al., 2016). Although AMPH clearly modulates ACC activity (Lapish et al., 2015), its effect on the encoding of effort-reward utility and other task variables in ACC has not been explicitly shown. Here, we attempt to link these independent observations to better understand how AMPH affects ACC encoding and dynamics pertinent to task engagement and outcome valuation.

We used high-density electrophysiology to record ensembles of single neuron activity from the ACC of well-trained rats performing a continuous version of the classic T-maze (Figure 1A). Rats ran from a starting feeder to one of two target feeders accessible after turning to the right or left at the choice point. Rats were forced to alternate to the left and right sides on subsequent trials, and trials were organized into blocks in which either the reward volume delivered at the target feeders, or the effort (barrier climb) required to reach each target feeder, was different (Figure 1B). Rats performed five blocks of trials, then received an injection of either saline or AMPH, and performed the five blocks again. We quantified changes in behavior and neural signaling after the injection, with respect to the pre-injection phase. We recorded a total of 1209 putative pyramidal neurons from 22 session (55 ± 6.7 simultaneous cells per session) in four rats.

Figure 1

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AMPH increases running speed and decreases reward consumption time

AMPH administration in our animals evoked typical locomotor effects in a dose-dependent manner. It increased the median running speed (Figure 2A; Kruskal-Wallis, χ²(3)>109; p<0.0001). This effect peaked at 1.0 mg/kg because rats became more frequently disengaged in the task at higher doses. Indeed, the amount of off-task behavior (circling, pausing, backtracking) increased with dose (Figure 2B; main effect ANOVA, F_3,18 = 3.36; p=0.042; power = 0.76). Off-task behaviors became so prominent at 2.0 mg/kg that animals did not complete a sufficient number of trials for analysis. Data at this concentration is therefore excluded from the present report.

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AMPH administration also produced typical effects on reward-related behaviors. The rats’ occupancy time at the reward feeders decreased with increasing dose (Kruskal-Wallis median test, χ²(3)>136, p<0.0001 for all feeders). This occurred at the start feeder (Figure 2C) and target feeders (Figure 2—figure supplement 1). Such decreased feeder engagement after AMPH is consistent with previous reports (Randrup et al., 1963; Randrup and Munkvad, 1967; Blundell et al., 1976; Blundell et al., 1979; Leibowitz et al., 1986; Floresco et al., 2008b). In sum, moderate doses of AMPH in this task maintains animals’ engagement in the task, even though they appear less motivated to consume the reward.

We next investigated effects of AMPH on running path. The reasons are twofold. First, ACC activity is highly sensitive to the running path, so any gross changes in path trajectory or variance may confound the decoding of other information (Euston and McNaughton, 2006). Second, it provides an additional indicator of task engagement. We therefore use the distribution of running path smoothness as an additional measure of task engagement and/or psychomotor effects. When analyzed over the entire track, the running path of rats became more variable after AMPH, and this effect increased as the dose of AMPH increased (Figure 2E; ANOVA, F_3,18 = 3.47; p=0.038; power = 0.77). This is particularly evident on the return arms of the track from the target feeders back to the starting feeder. The inter-trial path variance was not significantly affected in the segment from the starting feeder to target feeders (Figure 2F; ANOVA, F_3,18 = 1.83; p=0.178), although it trended to be higher at 1.5 mg/kg. These data indicate that task performance is not disrupted by lower doses, but begins to deteriorate at 1.5 mg/kg.

The behavioral data suggest that AMPH’s effect in this study is typical of past studies, in which lower doses facilitate task engagement, but high doses disrupt it. Furthermore, measures of reward consumption decrease with increasing AMPH dose.

ACC signals effort and reward

The ACC is well known to signal effort and reward. In order to visualize where in the present task ACC neurons signaled these variables, we segmented the track into 36 spatial bins and tested if the mean firing rate of each cell in each bin was significantly different during high-effort versus low-effort trials (t-test, p<0.05). Nearly 20% of cells signaled upcoming effort while on the approach to the barrier, and this proportion ramped up to nearly 35% during the climb/jump to the reward platform (Figure 3A, solid line). Conversely, about 10–15% of ACC cells in our sample encoded reward, and this proportion did not vary much during the task (Figure 3B, solid line). This is consistent with previous reports (Cowen et al., 2012). Administration of vehicle (saline) did not grossly affect the relative proportions of effort-related or reward-related cells (Figure 3A–B, dotted lines). This suggests that the relative proportion of cells signaling these task variables is consistent throughout the session. This is important because we use a within-session task design in order to observe AMPH-related changes in signaling of the same set of neurons, and some neural correlates may change during the session. Indeed, we found that the mean firing rate is lower after vehicle administration (Figure 3C), which almost certainly reflects the typical decrease in firing rate as sessions progress. This decrease is attenuated as AMPH dose increases, and the firing rate is increased at the highest dose relative to the first half of the session (Figure 3C; F_3,1262 = 7.05; p=0.0001). This increase of ACC firing rate by AMPH is consistent with previous reports (Lapish et al., 2015), and again suggests that AMPH is having a typical effect in the present study.

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AMPH compresses the encoding of utility by single-units

The analysis of costs and benefits is typically formalized through the concept of utility, which can include many features (Glimcher et al., 2009). Here, we consider the joint encoding of effort and reward by individual ACC neurons. We use a linear regression approach, which is a standard method for discriminating when a continuous or binary predictor variable (reward or effort here) is informative of another variable (firing rate). We first computed the correlation of each neuron’s firing rate with effort, and independently computed its correlation with reward volume, in each spatial bin from the starting feeder to the target feeders. We then analyzed the distribution of all cells in the effort-reward space for each spatial bin using principle component analysis (PCA). The first principle component (PC) reveals the primary axis of variance.

The distribution of points in the effort-reward space prior to drug reveals two interesting features (Figure 4). First, the distribution of points has a downward diagonal trend (i.e. the slope of the first PC is negative). This indicates that the joint encoding of reward and effort by individual ACC neurons is typically anti-correlated, which is expected of a utility signal. Further, the maximum variance of data is explained by neurons located in both the second (Q_II) and fourth (Q_IV) quadrants (Quadrants are numbered as in geometry, starting from the upper right corner and progressing in an anticlockwise direction). Neurons of quadrants Q_II and Q_IV have opposing signaling of utility. Neurons of Q_IV tend to generate more action potentials for high-utility conditions (i.e. when the effort is low or the reward volume is large), and fire less in low-utility conditions (i.e. high-effort or small-reward). Neurons in Q_II exhibit the inverse relationship among firing and value. The second revealing feature is that ACC cells are correlated with future reward and effort contingencies. This is evident by the non-uniform distribution of points in the spatial bin immediately following departure from the start feeder (Figure 4A, explained variance is 63, 62, 61, 61% for pre-inj, and 62, 63, 59, 57% for post-inj with increasing dose). This is nearly 100 cm in advance of the barrier and target feeder that are the basis of the correlation.

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The slope of the first PC becomes steeper with increasing AMPH (Figure 4A-B), indicating a loss of neural firing correlation with reward. This relationship holds for the remainder of the spatial bins (Figure 4—figure supplement 1). Circular statistical analysis of the PCs from each spatial bin (first pooled over all sessions with similar treatment) reveals that the post-injection PCs are significantly more vertical than the pre-drug condition for AMPH, whereas saline injection has no effect (Figure 4B; Kuiper two-sample test, k = 160; p=0.028 for 0.5 mg/kg, k = 176; p=0.007 for 1 mg/kg, k = 240; p<0.001 for 1.5 mg/kg AMPH). To ensure that this is not an effect of heterogeneous sample sizes among conditions, we ran a bootstrap analysis (100 repetitions) in which we randomly sub-sampled the data to obtain the same number of neurons for each condition. The PCs were highly stable, and the results did not change with down sampling. Moreover, the percentage of total variance explained by the first PCs remains above 60% for all conditions, which further indicates that the results of the PCA are reliable. The rotation of neural tuning toward the vertical effort axis indicates that the encoding of reward is ‘compressed’ more than is the encoding of effort. Furthermore, the explained variance by the first principal component is significantly decreased at 1.0 and 1.5 mg/kg, which further indicates an overall loss of utility signaling at higher doses of AMPH (Figure 4C.; ANOVA F_3,60 = 17.3454; p=3 x 10⁻⁸). This effect might be through a breakdown in the encoding of effort or reward (e.g. points moving toward the origin), or an increased dispersion in effort-reward coding by shifting some neurons to the first quadrant in which the neurons respond positively to both effort and reward. In either case, these neurons fail to encode utility. Note that this analysis includes all neurons so as to eliminate any selection bias that may occur by first categorizing cells as utility or other classes based on statistical thresholds. We next analyze statistics of cells first identified as encoding reward or effort.

The preceding analysis suggests that neurons jointly encoding both effort and reward tend to lose sensitivity to reward more so than effort. We next sought to determine changes in the signaling of these variables independently, by computing the proportion of cells with significantly different firing rate for different levels of effort, or reward (ANOVA; p<0.05, and moderatley large effect size of partial η² >0.138). The proportion of units encoding effort shows a weak ‘inverted U’ shape with increasing AMPH, driven by the increase in the proportion of effort cells at 0.5 mg/kg (Figure 4D; ANOVA, F_3,60 = 3.73; p=0.016). No dose of AMPH caused a significantly different proportion than vehicle. Conversely, the proportion of units encoding reward strongly decreases with increasing AMPH (Figure 4E; ANOVA, F_3,60 = 46.04; p=14 x 10⁻¹⁶). In sum, high doses of AMPH reduce reward signaling in ACC, but do not appear to reduce signaling of effort. This is consistent with our behavioral observations that AMPH-treated rats appear less interested in consuming the reward. These results therefore suggest that reward is devalued by AMPH to a greater extent than effort.

The reduced number of reward signaling cells following AMPH could occur because the mean difference of firing rate in the two conditions diminishes (i.e. attenuated signal), or because the variation of firing increases (i.e. increased noise). We therefore computed signal amplitude and variation before and after AMPH, in all cells significantly discriminating reward value prior to drug. The difference of firing rates for large and small rewards decreased as AMPH increased (Figure 4F; F_3,1935 = 4.1; p=0.006). Note that the difference is negative after vehicle, likely reflecting reduced motivation for the reward as the animal becomes sated. AMPH appears to accelerate this process. Conversely, the standard deviation of firing from trial to trial was not significantly different under AMPH (Figure 4G; F_3,1935 = 2.06; p=0.104). Nonetheless, it shows a trend in which variability of firing is reduced by 1.0 mg/kg, but increased by 1.5 mg/kg. This is consistent with changes in ensemble variance shown later in this report. Note that the standard deviation is computed independently for small and large rewards before averaging, so the reduction is not a consequence of reduced difference of means among large and small reward trials. In sum, these data indicate that the loss of reward signaling for intermediate doses of AMPH (0.5 and 1.0 mg/kg) is primarily due to the signal reduction, rather than an increase in noise. At higher doses, both may play a role.

Ensemble ACC activity encodes task epoch, task features, and past/present/future events

We next conducted a state-space analysis of simultaneously recorded neurons in order to assess how AMPH affects temporally evolving patterns of neural ensembles. We used a method termed Gaussian Process Factor Analysis (GPFA) to reduce the dimensionality of the data. This algorithm is particularly advantageous for producing smooth trajectories in low-dimensional space from high-dimensional processes with discrete events, such as action potentials (Yu et al., 2009). Similar to PCA, GPFA serves to capture as much variance as possible and does not optimize for any particular information present in the data (e.g. reward, location). In the 3D space of the first three GPFA factors, our ACC data form trajectories that move smoothly in the reduced space as the trial progresses (Figure 5). The trajectories discriminate task epochs, but are highly similar across trials of the same type. The trajectories diverge for trials of different effort or reward (Figure 5, right panel). For instance, the trajectory at the barrier climb deviates in high-effort trials as compared to low-effort ones. This is expected from the single-unit analysis, which showed that approximately 1/3 of ACC neurons discriminated effort during this epoch. Note that the ACC trajectory diverges widely on the approach to the turn (red shading), which is well before the barrier climb. This is consistent with the single unit analysis showing correlation of units on the middle segment, and further suggests that the ACC is sensitive to upcoming events.

Figure 5

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The state-space analysis reveals another interesting novel phenomenon that cannot be discriminated directly from individual units. We analyzed trials in which the animal approached the barrier, then turned around and backtracked to the center feeder, and finally completed the trial by approaching and climbing the barrier. During this second approach and trial completion, the ACC diverged greatly from its typical trajectory during this task epoch, even though the path and velocity of the rat was very similar during both approaches (Figure 6). It thus appears that the ACC is sensitive to the recent history (or context) in which the task-related activity occurs. In sum, these data indicate that the trajectory of ACC activity in the reduced space is highly sensitive to the task epoch, task features (e.g. barrier climb), as well as the sequence of past and future events. Moreover, it shows that deviations from the trajectory are concomitant with off-task behavior. Changes in the trajectories, therefore, likely reveal changes in the neural processes directing task engagement and execution.

Figure 6

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AMPH contracts ACC state-space occupancy at low doses, and expands it at high doses

We next examined the effect of AMPH on neural trajectories. Low-dose AMPH caused a contraction of trajectories in state space for both large-reward trials (Figure 7B; ANOVA, F_3,371 = 28.28, p=2 x 10⁻¹⁶) and small-reward trials (Figure 7C; ANOVA, F_3,325 = 19.78, p=8 x 10⁻¹²). This is not likely explained by differences in running paths. This is because reduced state-space volumes suggest less variance of neural firing. This would require less variance in running paths, which does not occur (Figure 2D-F). It is also possible the first three latent factors capture less variance of the neural activity after AMPH, in which case the reduction in volume is likely an artifact of the methodology. We therefore compared the explained variance of the first three factors and found that it was not reduced by administration of 0.5–1.0 mg/kg AMPH (Figure 7D; confidence intervals do not deviate from 0). The contraction therefore appears to be a neural phenomenon, rather than a methodological artefact. As opposed to the reduction of state-space volume under low-dose AMPH, we instead found that 1.5 mg/kg caused an expansion (Figure 7B-C). Because the roughness of the running path increased at this dose, it is possible that these phenomena are related.

Figure 7

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It has been suggested that the neuromodulators elevated by AMPH could increase the dynamical stability of cortical activity (Durstewitz et al., 1999; Compte et al., 2000; Durstewitz et al., 2000; Brunel and Wang, 2001; Gruber et al., 2006; Cano-Colino et al., 2013). If so, we would expect a reduction in the variation of neural trajectories across trials of the same type. We found that the mean variation of trajectories showed a trend to vary under AMPH but were not significantly different with the present sample (ANOVA; large-rew: F_3,62 = 2.11; p=0.108, and small-reward: F_3,62 = 2.37; p=0.079). Nonetheless, the variation tended to decreases relative to baseline following 0.5 and 1.0 mg/kg, but then tended to increase under 1.5 mg/kg, for both small reward trials and large reward trials (Figure 7E–F). The pattern of reduced variation under low AMPH and increased variation under high AMPH is generally consistent across task epochs but is particularly evident when animals were at the target feeder (Figure 7.G). The pattern of dose-dependent modulation of variance described above is statistically significant at the target feeder (ANOVA; F_3,62 = 3.93, p=0.012). These data support the hypothesis that neural dynamics are stabilized by low-to-moderate AMPH, but become unstable under high doses.

We investigated the effects of systemic AMPH administration on ACC encoding and dynamics in rats performing a task with variable effort and reward. Our aim in this study was to use neural recordings to better understand why AMPH typically causes animals to become more engaged in tasks and to increase effort for food rewards, while also decreasing their motivation to consume the reward. We observed these typical behavioral features in the present study. Rats ran faster, but spent less time consuming rewards as AMPH increased. We observed several neural correlates of task performance that were sensitive to AMPH. Notably, many individual ACC neurons jointly encoded effort and reward, which is expected of cells involved in computing utility. Many units signaled positive utility; they positively correlated with the reward amount and negatively with the effort level. AMPH injection increased the dispersion of effort-reward coding in the population, but the rotation of the principle axis of the population toward the effort axis reveals that the suppression of reward signaling by single units is the primary effect. This effect was mirrored by all neurons encoding reward or effort. AMPH injection thus decreased reward signaling in ACC while having little effect on effort signaling, and this coincided with reduced time at the feeder. Note that the reduced reward signaling emerged well before rats reached the target feeder, and so is indicative of altered anticipation, rather than changes in sensory aspects of reward consumption. Therefore, it is possible that the reduced reward signaling is a causal factor in the reduced motivation for reward that is a hallmark of AMPH across species (Blundell et al., 1976; Blundell et al., 1979; Leibowitz et al., 1986).

The state-space analysis of ensemble activity (mean of 55 ± 6.7 putative pyramidal neurons per session) revealed smoothly varying trajectories specific to task epochs and conditions. These trajectories were highly similar across trials of the same type, suggesting they reflect task-specific information. Besides encoding the present state of the animal on the track, the trajectories also reflected future events. This is evident in the deviation of trajectories prior to turning to reach the barrier in high vs low effort trials and is consistent with our finding that many single units were correlated with future reward and effort. Trajectories were also sensitive to past events. In particular, the trajectories deviated extensively following off-task behaviors, even as the rats re-engaged in the task and performed in a typical manner. Thus, the trajectories are task-specific, and sensitive to past, present, and future states/actions. AMPH had dose-dependent effects on the trajectories. Their modulation during the task (volume in state space) and trial-by-trial variance contracted under low-doses, but expanded under high doses. This coincided with increased task engagement at the lower doses, but frequent disengagement at high doses. Note that we excluded trials with off-task behavior from the neural analysis of volume, suggesting that the off-task behavior is triggered by processes related to the expansion of state-space occupancy and/or variance, rather than the inverse relationship.

AMPH increases extracellular concentrations of dopamine, norepinephrine, and other neuromodulators in the prefrontal cortex and other regions (Chiueh and Moore, 1973; Pum et al., 2007). The idea that such neuromodulators affect brain dynamics in the neocortex by altering the stability of attractor states has a long history in computational studies (Durstewitz et al., 1999; Compte et al., 2000; Durstewitz et al., 2000; Brunel and Wang, 2001; Gruber et al., 2006; Cano-Colino et al., 2013). Attractors are stable states of activity produced by particular configurations of synaptic connectivity and neural excitability. Stability in this context indicates that the network resists small perturbations (inputs that drive ensemble activity to patterns different than the attractor state) so as to re-settle in the stable pattern. Increased stability means that the state can resist larger perturbations. Attractors can take different forms. The simplest is point attractors, which form a static pattern of activity in time. Another form is line attractors, in which activity propagates smoothly along a ‘valley’ of stable points, but resists perturbations that push the system up the ‘hills’ on either side. Computational studies have predicted that dopamine can stabilize these types of attractors as well (Gruber et al., 2006).

Recently, Lapish and colleagues tested the hypothesis that low-dose AMPH stabilizes attractor states in dorsal medial prefrontal cortex, whereas high doses destabilizes them, during a working memory task (Lapish et al., 2015). They found that relative to saline, 1.0 mg/kg AMPH reduced variation of ensemble states after dimensionality reduction, whereas 3.3 mg/kg expanded it. This is consistent with the dose-dependent effects on state-space variation we report here (Figure 7). Interestingly, these authors report that neural states tend to form distinct clusters separated in space for different task epochs (Balaguer-Ballester et al., 2011; Lapish et al., 2015). In contrast, we found that ACC states do not cluster, but rather form smooth trajectories that evolve as the trial progresses. This difference may be due to the difference in dimensionality reduction methodology, task demands, and/or the number of units recorded. Here, we have a larger number of neurons in each ensemble, but use a dimensionality reduction method that is not optimized for anything other than capturing maximal variance in each successive factor, similar to principle component analysis (PCA). Nonetheless, the low-dimensional representation was highly specific for task epoch and task condition (effort, etc.) and was remarkably consistent across trials, suggesting that it does capture meaningful variance. Furthermore, it was sensitive to future events, as well as past off-task behavior. Based on these properties, we suggest that ACC may be executing some form of a continuous mental ‘script’ for task events and execution, which includes not only the present state, but also what is about to happen. By ‘continuous’, we mean that the ACC is not only encoding information about the sequence of events, but also their relative proximity in time or space. This is consistent with the smooth ramping up of the proportion of effort-related cells as rats approach the barrier (Figure 3A). It is also consistent with the ramping up of primate ACC activity in anticipation of stimuli and reward (Amiez et al., 2006).

The contraction of state space occupancy and trend of smaller variance under 0.5 and 1.0 mg/kg AMPH may reflect increased stability of a line attractor. Further, we propose that the increased excitability under AMPH (Figure 3C; Lapish et al., 2015) could account for the increased speed of task execution. That is, it propels the activity state along the valley of the line attractor more quickly. Increased excitability can cause intrinsic perturbations that destabilize attractor states, but we speculate that the increased dynamical stability by AMPH counteracts this effect. We also speculate that the further increased excitability at higher doses may exceed the increased stability. This would result in higher variance and state-space occupancy, and more frequently generate perturbations sufficiently large to escape the attractor basin, so that the task-related trajectory cannot recover. The result would be a break in the behavioral script and frequent interruptions of task performance. These predictions are consistent with the neural and behavioral data observed here and elsewhere, as described next.

Our data provide a possible explanation for the apparently paradoxical effect of AMPH on task engagement and reward valuation. The reduced motivation for reward may follow from the reduced reward encoding. They continue to engage in the task, however, because of the increased stability of task-related neural trajectories. In short, the stability is sufficiently strong that it ‘captures’ the neural dynamics to an extent that it is more difficult for the brain to shift to other behavioural scripts. The result is that rats run through the behavioural script of the task, but are then not motivated to consume the reward. This provides an explanatory mechanism for many of the known effects of AMPH. As described above, AMPH has been shown to decrease feeding (Foltin, 2001; Shoblock et al., 2003; Cannon et al., 2004; Wellman et al., 2009) and decrease the sensitivity to reward omission (Wong et al., 2017), suggesting a devaluation of food rewards. Again, this may follow from the attenuation of reward encoding. Low doses of AMPH increase task engagement and execution speed in a variety of tasks (Wilkinson et al., 1993; Foltin, 2001; Odum and Shahan, 2004). This can be explained as a consequence of increased excitability balanced by increased stability. Similarly, it provides a possible explanation for the ability of low-dose AMPH to enhance attention, vigilance, and working memory in a variety of task settings and species (Sostek et al., 1980; Ridley et al., 1982; Koelega, 1993; Solanto, 1998; Grilly, 2000; Shoblock et al., 2003; Silber et al., 2006; Sagvolden, 2011). Specifically, these can be described as increasing robustness against perturbations unrelated to the task (Gruber et al., 2006). AMPH also consistently increases motoric activity (Randrup and Munkvad, 1967; Groves and Rebec, 1976; Robinson and Berridge, 1993; Wilkinson et al., 1993; Sams-Dodd, 1998), an effect we observed here. Our explanation is that it is a consequence of increased neural excitability constrained by increased attractor stability. This is not likely an effect mediated largely by ACC, because ACC-lesioned animals have little deficits in engaging motoric output (Holec et al., 2014; Brockett et al., 2020). This suggests that AMPH acting in other brain regions also influences task performance in our animals.

The control of behavioral output depends on sets of interconnected brain structures (Balleine and O'Doherty, 2010; Gruber and McDonald, 2012), and AMPH influences processing in several of them (Chiueh and Moore, 1973; Pum et al., 2007). Therefore, we cannot infer from our data where in the brain AMPH may be affecting task-related processing. Dopaminergic tone in the nucleus accumbens appears to be critical for effort-based decision-making (Salamone et al., 1994), but it is less clear if dopamine in the ACC also is needed. Walton et al., 2005 reported that depleting dopamine innervation with 6-OHDA in the ACC did not alter the likelihood of effort-based responses, whereas Schweimer and Hauber, 2005 found that such lesions reduced this type of responding. This discrepancy may be related to methodological differences between the two studies. Because the mPFC and striatum form functional circuits with other structures (Middleton and Strick, 1997; Voorn et al., 2004), we can use neural activity in ACC as a window into network processing. Previous studies have reported ACC activity in anticipation of effort and reward (Sul et al., 2010; Cowen et al., 2012; Hashemniayetorshizi et al., 2015). Our results show that ACC neurons primarily encode utility in the portion of the maze from the starting feeder to target feeders. It is likely that the animals anticipate the effort and reward of the upcoming trial because of the task design; the animal was directed to alternate between left and right options, and the effort and rewards were invariant over blocks of 20 trials. Indeed, we found that many neurons encoded future utility when the animal had just left the starting feeder, and ensemble dynamics deviate among trials of high/low effort at this point. It therefore seems that animals track upcoming effort and reward, even though they are not able to choose among the target feeders. Their choice in the present experiment is whether to engage in the task or not. Our data are therefore more revealing of mechanisms of task engagement than choice. Indeed, the present data do not provide conclusive evidence as to why animals shift their preference to high-effort high-reward options under low-dose AMPH (Floresco et al., 2008b; Bardgett et al., 2009). Effort encoding was not affected by AMPH, so it does not appear that the brain discounts effort or cannot discriminate future effort levels.

One caveat to our study is that we used repeated injections of AMPH over several days to provide escalating doses in the same set of animals. We used this design because (1) drug naive rats will often not perform tasks if their first exposure is a high dose, and (2) the expense and effort needed to record neural ensembles prohibits us from collecting only one dose per rat. It is likely that AMPH had lasting effects on the brain. Repeated administration of high-doses AMPH (5 weeks of escalating daily dose up to 8 mg/kg) changes synaptic and dendritic density in the mPFC of untrained rats (Robinson and Kolb, 1997). Furthermore, withdrawal from methamphetamine following weeks of repeated exposure has been shown to reduce the preference of rats for high-effort, high-reward options (Hart et al., 2018). Our animals had far lower exposure (3–5 injections of 0.5–1.5 mg/kg). Nonetheless, we attempted to minimize confounds of AMPH-driven changes. We used an escalating dose to minimize total prior exposure on each recording session. We also intermixed control sessions (saline injections) between the days of AMPH. Lastly, we used within session contrasts prior to between-session comparisons. These latter two controls serve to capture accumulating changes in signaling that may occur as the experiment progressed. Furthermore, we used a before/after design in which the drug was always given in the second half of the experiment. This allowed us to observe the effect of AMPH on signaling of an identified set of cells. However, any consistent changes that occur during the session, such as satiation, may therefore confound the results. The between-session contrasts help mitigate this confound. The pattern of results we observed, particularly the fact that responses often had different polarity for high vs low doses, suggests that this potential confound is not strongly influencing our results or interpretations.

In conclusion, the data in the present study suggest that AMPH decreases reward signaling in circuits involving ACC, which may play a role in the well-known effect of this drug to reduce reward consumption behaviours. Further, the data support previous proposals that AMPH has a dose-dependent effect on the stability of neural dynamics in the prefrontal cortex, which could explain increased task engagement at low doses, but increased task disruption at high doses. Future studies are needed to identify which of the several neuromodulatory systems affected by AMPH play a role in these effects.

Data and analysis code are available online (https://github.com/SaeedehUleth/AMPH-and-utility-encoding; copy archived at https://github.com/elifesciences-publications/AMPH-and-utility-encoding).

1. 1. Amiez C
   2. Joseph JP
   3. Procyk E

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Author details

1. Saeedeh Hashemnia

   Canadian Center for Behavioral Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing - original draft

   Competing interests

   No competing interests declared

2. David R Euston

   Canadian Center for Behavioral Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Conceptualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Aaron J Gruber

   Canadian Center for Behavioral Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing

   For correspondence

   aaron.gruber@uleth.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2700-5429

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