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

Download asset Open asset

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.

Figure 2 with 1 supplement see all

Download asset Open asset

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.

Figure 3

Download asset Open asset

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.

Figure 4 with 1 supplement see all

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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

   (2006) Reward encoding in the monkey anterior cingulate cortex

   Cerebral Cortex 16:1040–1055.

   https://doi.org/10.1093/cercor/bhj046

   • PubMed
   • Google Scholar
2. 1. Balaguer-Ballester E
   2. Lapish CC
   3. Seamans JK
   4. Durstewitz D

   (2011) Attracting dynamics of frontal cortex ensembles during memory-guided decision-making

   PLOS Computational Biology 7:e1002057.

   https://doi.org/10.1371/journal.pcbi.1002057

   • PubMed
   • Google Scholar
3. 1. Balleine BW
   2. O'Doherty JP

   (2010) Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action

   Neuropsychopharmacology 35:48–69.

   https://doi.org/10.1038/npp.2009.131

   • PubMed
   • Google Scholar
4. 1. Bardgett ME
   2. Depenbrock M
   3. Downs N
   4. Points M
   5. Green L

   (2009) Dopamine modulates effort-based decision making in rats

   Behavioral Neuroscience 123:242–251.

   https://doi.org/10.1037/a0014625

   • PubMed
   • Google Scholar
5. 1. Barthó P
   2. Hirase H
   3. Monconduit L
   4. Zugaro M
   5. Harris KD
   6. Buzsáki G

   (2004) Characterization of neocortical principal cells and interneurons by network interactions and extracellular features

   Journal of Neurophysiology 92:600–608.

   https://doi.org/10.1152/jn.01170.2003

   • PubMed
   • Google Scholar
6. 1. Blanchard TC
   2. Strait CE
   3. Hayden BY

   (2015) Ramping ensemble activity in dorsal anterior cingulate neurons during persistent commitment to a decision

   Journal of Neurophysiology 114:2439–2449.

   https://doi.org/10.1152/jn.00711.2015

   • PubMed
   • Google Scholar
7. 1. Blundell JE
   2. Latham CJ
   3. Leshem MB

   (1976) Differences between the anorexic actions of amphetamine and fenfluramine--possible effects on hunger and satiety

   Journal of Pharmacy and Pharmacology 28:471–477.

   https://doi.org/10.1111/j.2042-7158.1976.tb02768.x

   • PubMed
   • Google Scholar
8. 1. Blundell JE
   2. Latham CJ
   3. Moniz E
   4. McArthur RA
   5. Rogers PJ

   (1979) Structural analysis of the actions of amphetamine and fenfluramine on food intake and feeding behaviour in animals and in man

   Current Medical Research and Opinion 6:34–54.

   https://doi.org/10.1185/03007997909117490

   • Google Scholar
9. 1. Brockett AT
   2. Tennyson SS
   3. deBettencourt CA
   4. Gaye F
   5. Roesch MR

   (2020) Anterior cingulate cortex is necessary for adaptation of action plans

   PNAS 117:6196–6204.

   https://doi.org/10.1073/pnas.1919303117

   • PubMed
   • Google Scholar
10. 1. Brunel N
    2. Wang XJ

    (2001) Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition

    Journal of Computational Neuroscience 11:63–85.

    https://doi.org/10.1023/a:1011204814320

    • PubMed
    • Google Scholar
11. 1. Cannon CM
    2. Abdallah L
    3. Tecott LH
    4. During MJ
    5. Palmiter RD

    (2004) Dysregulation of striatal dopamine signaling by amphetamine inhibits feeding by hungry mice

    Neuron 44:509–520.

    https://doi.org/10.1016/j.neuron.2004.10.009

    • PubMed
    • Google Scholar
12. 1. Cano-Colino M
    2. Almeida R
    3. Compte A

    (2013) P.2.013 modelling serotonergic neuromodulation of behavioural performance in spatial working memory

    European Neuropsychopharmacology 23:S37.

    https://doi.org/10.1016/S0924-977X(13)70041-5

    • Google Scholar
13. 1. Chiueh CC
    2. Moore KE

    (1973) Release of endogenously synthesized catechols from the caudate nucleus by stimulation of the nigro-striatal pathway and by the administration of d-amphetamine

    Brain Research 50:221–225.

    https://doi.org/10.1016/0006-8993(73)90612-4

    • PubMed
    • Google Scholar
14. 1. Compte A
    2. Brunel N
    3. Goldman-Rakic PS
    4. Wang XJ

    (2000) Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model

    Cerebral Cortex 10:910–923.

    https://doi.org/10.1093/cercor/10.9.910

    • PubMed
    • Google Scholar
15. 1. Cowen SL
    2. Davis GA
    3. Nitz DA

    (2012) Anterior cingulate neurons in the rat map anticipated effort and reward to their associated action sequences

    Journal of Neurophysiology 107:2393–2407.

    https://doi.org/10.1152/jn.01012.2011

    • PubMed
    • Google Scholar
16. 1. Cowen SL
    2. McNaughton BL

    (2007) Selective delay activity in the medial prefrontal cortex of the rat: contribution of sensorimotor information and contingency

    Journal of Neurophysiology 98:303–316.

    https://doi.org/10.1152/jn.00150.2007

    • PubMed
    • Google Scholar
17. 1. Croxson PL
    2. Walton ME
    3. O'Reilly JX
    4. Behrens TE
    5. Rushworth MF

    (2009) Effort-based cost-benefit valuation and the human brain

    Journal of Neuroscience 29:4531–4541.

    https://doi.org/10.1523/JNEUROSCI.4515-08.2009

    • PubMed
    • Google Scholar
18. 1. Durstewitz D
    2. Kelc M
    3. Güntürkün O

    (1999) A neurocomputational theory of the dopaminergic modulation of working memory functions

    The Journal of Neuroscience 19:2807–2822.

    https://doi.org/10.1523/JNEUROSCI.19-07-02807.1999

    • PubMed
    • Google Scholar
19. 1. Durstewitz D
    2. Seamans JK
    3. Sejnowski TJ

    (2000) Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex

    Journal of Neurophysiology 83:1733–1750.

    https://doi.org/10.1152/jn.2000.83.3.1733

    • PubMed
    • Google Scholar
20. 1. Durstewitz D
    2. Vittoz NM
    3. Floresco SB
    4. Seamans JK

    (2010) Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning

    Neuron 66:438–448.

    https://doi.org/10.1016/j.neuron.2010.03.029

    • PubMed
    • Google Scholar
21. 1. Euston DR
    2. McNaughton BL

    (2006) Apparent encoding of sequential context in rat medial prefrontal cortex is accounted for by behavioral variability

    Journal of Neuroscience 26:13143–13155.

    https://doi.org/10.1523/JNEUROSCI.3803-06.2006

    • PubMed
    • Google Scholar
22. 1. Faul F
    2. Erdfelder E
    3. Lang AG
    4. Buchner A

    (2007) G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behavior Research Methods 39:175–191.

    https://doi.org/10.3758/BF03193146

    • PubMed
    • Google Scholar
23. 1. Floresco SB
    2. St Onge JR
    3. Ghods-Sharifi S
    4. Winstanley CA

    (2008a) Cortico-limbic-striatal circuits subserving different forms of cost-benefit decision making

    Cognitive, Affective, & Behavioral Neuroscience 8:375–389.

    https://doi.org/10.3758/CABN.8.4.375

    • PubMed
    • Google Scholar
24. 1. Floresco SB
    2. Tse MT
    3. Ghods-Sharifi S

    (2008b) Dopaminergic and glutamatergic regulation of effort- and delay-based decision making

    Neuropsychopharmacology 33:1966–1979.

    https://doi.org/10.1038/sj.npp.1301565

    • PubMed
    • Google Scholar
25. 1. Foltin RW

    (2001) Effects of amphetamine, dexfenfluramine, diazepam, and other pharmacological and dietary manipulations on food "seeking" and "taking" behavior in non-human primates

    Psychopharmacology 158:28–38.

    https://doi.org/10.1007/s002130100865

    • PubMed
    • Google Scholar
26. Book

    1. Freedman D

    (2009) Statistical Models: Theory and Practice

    Cambridge; New York: Cambridge University Press.

    https://doi.org/10.1017/CBO9780511815867

    • Google Scholar
27. 1. Fujisawa S
    2. Amarasingham A
    3. Harrison MT
    4. Buzsáki G

    (2008) Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

    Nature Neuroscience 11:823–833.

    https://doi.org/10.1038/nn.2134

    • PubMed
    • Google Scholar
28. Book

    1. Glimcher PW
    2. Camerer CF
    3. Fehr E
    4. Poldrack RA

    (2009) Neuroeconomics Decision Making and the Brain Preface

    Elsevier.

    https://doi.org/10.1016/C2011-0-05512-6

    • Google Scholar
29. 1. Gneiting T
    2. Ševčíková H
    3. Percival DB

    (2012) Estimators of fractal dimension: assessing the roughness of time series and spatial data

    Statistical Science 27:247–277.

    https://doi.org/10.1214/11-STS370

    • Google Scholar
30. 1. Grilly DM

    (2000) A verification of psychostimulant-induced improvement in sustained attention in rats: effects of d-amphetamine, nicotine, and pemoline

    Experimental and Clinical Psychopharmacology 8:14–21.

    https://doi.org/10.1037/1064-1297.8.1.14

    • PubMed
    • Google Scholar
31. 1. Groves PM
    2. Rebec GV

    (1976) Biochemistry and behavior: some central actions of amphetamine and antipsychotic drugs

    Annual Review of Psychology 27:91–127.

    https://doi.org/10.1146/annurev.ps.27.020176.000515

    • PubMed
    • Google Scholar
32. 1. Gruber AJ
    2. Dayan P
    3. Gutkin BS
    4. Solla SA

    (2006) Dopamine modulation in the basal ganglia locks the gate to working memory

    Journal of Computational Neuroscience 20:153–166.

    https://doi.org/10.1007/s10827-005-5705-x

    • PubMed
    • Google Scholar
33. 1. Gruber AJ
    2. Hussain RJ
    3. O'Donnell P

    (2009) The nucleus accumbens: a switchboard for goal-directed behaviors

    PLOS ONE 4:e5062.

    https://doi.org/10.1371/journal.pone.0005062

    • PubMed
    • Google Scholar
34. 1. Gruber AJ
    2. Calhoon GG
    3. Shusterman I
    4. Schoenbaum G
    5. Roesch MR
    6. O'Donnell P

    (2010) More is less: a disinhibited prefrontal cortex impairs cognitive flexibility

    Journal of Neuroscience 30:17102–17110.

    https://doi.org/10.1523/JNEUROSCI.4623-10.2010

    • PubMed
    • Google Scholar
35. 1. Gruber AJ
    2. McDonald RJ

    (2012) Context, emotion, and the strategic pursuit of goals: interactions among multiple brain systems controlling motivated behavior

    Frontiers in Behavioral Neuroscience 6:50.

    https://doi.org/10.3389/fnbeh.2012.00050

    • PubMed
    • Google Scholar
36. 1. Hart EE
    2. Gerson JO
    3. Izquierdo A

    (2018) Persistent effect of withdrawal from intravenous methamphetamine self-administration on brain activation and behavioral economic indices involving an effort cost

    Neuropharmacology 140:130–138.

    https://doi.org/10.1016/j.neuropharm.2018.07.023

    • PubMed
    • Google Scholar
37. Conference

    1. Hashemniayetorshizi S
    2. Sessford D
    3. Gruber AJ
    4. Euston DR

    (2015)

    Modulation of cellular activity by effort and reward in anterior cingulate cortex

    Program No. 633.20. 2015 Neuroscience Meeting Planner.

    • Google Scholar
38. 1. Hausdorff F

    (1919) Dimension und äußeres Maß

    Mathematische Annalen 79:157–179.

    https://doi.org/10.1007/BF01457179

    • Google Scholar
39. 1. Hillman KL
    2. Bilkey DK

    (2010) Neurons in the rat anterior cingulate cortex dynamically encode cost-benefit in a spatial decision-making task

    Journal of Neuroscience 30:7705–7713.

    https://doi.org/10.1523/JNEUROSCI.1273-10.2010

    • PubMed
    • Google Scholar
40. 1. Holec V
    2. Pirot HL
    3. Euston DR

    (2014) Not all effort is equal: the role of the anterior cingulate cortex in different forms of effort-reward decisions

    Frontiers in Behavioral Neuroscience 8:12.

    https://doi.org/10.3389/fnbeh.2014.00012

    • PubMed
    • Google Scholar
41. 1. Hosokawa T
    2. Kennerley SW
    3. Sloan J
    4. Wallis JD

    (2013) Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex

    Journal of Neuroscience 33:17385–17397.

    https://doi.org/10.1523/JNEUROSCI.2221-13.2013

    • PubMed
    • Google Scholar
42. 1. Isomura Y
    2. Ito Y
    3. Akazawa T
    4. Nambu A
    5. Takada M

    (2003) Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex

    The Journal of Neuroscience 23:8002–8012.

    https://doi.org/10.1523/JNEUROSCI.23-22-08002.2003

    • PubMed
    • Google Scholar
43. 1. Kennerley SW
    2. Walton ME
    3. Behrens TE
    4. Buckley MJ
    5. Rushworth MF

    (2006) Optimal decision making and the anterior cingulate cortex

    Nature Neuroscience 9:940–947.

    https://doi.org/10.1038/nn1724

    • PubMed
    • Google Scholar
44. 1. Klein-Flügge MC
    2. Kennerley SW
    3. Friston K
    4. Bestmann S

    (2016) Neural signatures of value comparison in human cingulate cortex during decisions requiring an Effort-Reward Trade-off

    Journal of Neuroscience 36:10002–10015.

    https://doi.org/10.1523/JNEUROSCI.0292-16.2016

    • PubMed
    • Google Scholar
45. 1. Koelega HS

    (1993) Stimulant drugs and vigilance performance: a review

    Psychopharmacology 111:1–16.

    https://doi.org/10.1007/BF02257400

    • PubMed
    • Google Scholar
46. 1. Lapish CC
    2. Durstewitz D
    3. Chandler LJ
    4. Seamans JK

    (2008) Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex

    PNAS 105:11963–11968.

    https://doi.org/10.1073/pnas.0804045105

    • PubMed
    • Google Scholar
47. 1. Lapish CC
    2. Balaguer-Ballester E
    3. Seamans JK
    4. Phillips AG
    5. Durstewitz D

    (2015) Amphetamine exerts Dose-Dependent changes in prefrontal cortex attractor dynamics during working memory

    Journal of Neuroscience 35:10172–10187.

    https://doi.org/10.1523/JNEUROSCI.2421-14.2015

    • PubMed
    • Google Scholar
48. 1. Leibowitz SF
    2. Shor-Posner G
    3. Maclow C
    4. Grinker JA

    (1986) Amphetamine: effects on meal patterns and macronutrient selection

    Brain Research Bulletin 17:681–689.

    https://doi.org/10.1016/0361-9230(86)90200-5

    • PubMed
    • Google Scholar
49. 1. Mashhoori A
    2. Hashemnia S
    3. McNaughton BL
    4. Euston DR
    5. Gruber AJ

    (2018) Rat anterior cingulate cortex recalls features of remote reward locations after disfavoured reinforcements

    eLife 7:e29793.

    https://doi.org/10.7554/eLife.29793

    • PubMed
    • Google Scholar
50. 1. McNaughton BL
    2. O'Keefe J
    3. Barnes CA

    (1983) The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records

    Journal of Neuroscience Methods 8:391–397.

    https://doi.org/10.1016/0165-0270(83)90097-3

    • PubMed
    • Google Scholar
51. 1. Middleton FA
    2. Strick PL

    (1997)

    New concepts about the organization of basal ganglia output

    Advances in Neurology 74:57–68.

    • PubMed
    • Google Scholar
52. 1. Odum AL
    2. Shahan TA

    (2004) D-Amphetamine reinstates behavior previously maintained by food: importance of context

    Behavioural Pharmacology 15:513–516.

    https://doi.org/10.1097/00008877-200411000-00007

    • PubMed
    • Google Scholar
53. Book

    1. Paxinos G
    2. Watson C

    (2014) Paxino's and Watson's the Rat Brain in Stereotaxic Coordinates

    Academic Press.

    https://doi.org/10.1016/c2009-0-63235-9

    • Google Scholar
54. 1. Phillips PE
    2. Walton ME
    3. Jhou TC

    (2007) Calculating utility: preclinical evidence for cost-benefit analysis by mesolimbic dopamine

    Psychopharmacology 191:483–495.

    https://doi.org/10.1007/s00213-006-0626-6

    • PubMed
    • Google Scholar
55. 1. Pum M
    2. Carey RJ
    3. Huston JP
    4. Müller CP

    (2007) Dissociating effects of cocaine and d-amphetamine on dopamine and serotonin in the perirhinal, entorhinal, and prefrontal cortex of freely moving rats

    Psychopharmacology 193:375–390.

    https://doi.org/10.1007/s00213-007-0791-2

    • PubMed
    • Google Scholar
56. 1. Randrup A
    2. Munkvad I
    3. Udsen P

    (1963) Adrenergic mechanisms and amphetamine induced abnormal behaviour

    Acta Pharmacologica Et Toxicologica 20:145–157.

    https://doi.org/10.1111/j.1600-0773.1963.tb01731.x

    • PubMed
    • Google Scholar
57. 1. Randrup A
    2. Munkvad I

    (1967) Stereotyped activities produced by amphetamine in several animal species and man

    Psychopharmacologia 11:300–310.

    https://doi.org/10.1007/BF00404607

    • PubMed
    • Google Scholar
58. 1. Ridley RM
    2. Baker HF
    3. Owen F
    4. Cross AJ
    5. Crow TJ

    (1982) Behavioural and biochemical effects of chronic amphetamine treatment in the vervet monkey

    Psychopharmacology 78:245–251.

    https://doi.org/10.1007/BF00428159

    • PubMed
    • Google Scholar
59. 1. Robinson TE
    2. Berridge KC

    (1993) The neural basis of drug craving: an incentive-sensitization theory of addiction

    Brain Research Reviews 18:247–291.

    https://doi.org/10.1016/0165-0173(93)90013-P

    • PubMed
    • Google Scholar
60. 1. Robinson TE
    2. Kolb B

    (1997) Persistent structural modifications in nucleus accumbens and prefrontal cortex neurons produced by previous experience with amphetamine

    The Journal of Neuroscience 17:8491–8497.

    https://doi.org/10.1523/JNEUROSCI.17-21-08491.1997

    • PubMed
    • Google Scholar
61. 1. Rudebeck PH
    2. Walton ME
    3. Smyth AN
    4. Bannerman DM
    5. Rushworth MF

    (2006) Separate neural pathways process different decision costs

    Nature Neuroscience 9:1161–1168.

    https://doi.org/10.1038/nn1756

    • PubMed
    • Google Scholar
62. 1. Sagvolden T

    (2011) Impulsiveness, overactivity, and poorer sustained attention improve by chronic treatment with low doses of l-amphetamine in an animal model of Attention-Deficit/Hyperactivity disorder (ADHD)

    Behavioral and Brain Functions 7:6.

    https://doi.org/10.1186/1744-9081-7-6

    • PubMed
    • Google Scholar
63. 1. Salamone JD
    2. Cousins MS
    3. Bucher S

    (1994) Anhedonia or anergia? effects of haloperidol and nucleus accumbens dopamine depletion on instrumental response selection in a T-maze cost/benefit procedure

    Behavioural Brain Research 65:221–229.

    https://doi.org/10.1016/0166-4328(94)90108-2

    • PubMed
    • Google Scholar
64. 1. Sams-Dodd F

    (1998) Effects of continuous D-amphetamine and phencyclidine administration on social behaviour, stereotyped behaviour, and locomotor activity in rats

    Neuropsychopharmacology 19:18–25.

    https://doi.org/10.1016/S0893-133X(97)00200-5

    • PubMed
    • Google Scholar
65. 1. Schweimer J
    2. Hauber W

    (2005) Involvement of the rat anterior cingulate cortex in control of instrumental responses guided by reward expectancy

    Learning & Memory 12:334–342.

    https://doi.org/10.1101/lm.90605

    • PubMed
    • Google Scholar
66. 1. Shoblock JR
    2. Sullivan EB
    3. Maisonneuve IM
    4. Glick SD

    (2003) Neurochemical and behavioral differences between d-methamphetamine and d-amphetamine in rats

    Psychopharmacology 165:359–369.

    https://doi.org/10.1007/s00213-002-1288-7

    • PubMed
    • Google Scholar
67. 1. Silber BY
    2. Croft RJ
    3. Papafotiou K
    4. Stough C

    (2006) The acute effects of d-amphetamine and methamphetamine on attention and psychomotor performance

    Psychopharmacology 187:154–169.

    https://doi.org/10.1007/s00213-006-0410-7

    • PubMed
    • Google Scholar
68. 1. Skvortsova V
    2. Palminteri S
    3. Pessiglione M

    (2014) Learning to minimize efforts versus maximizing rewards: computational principles and neural correlates

    Journal of Neuroscience 34:15621–15630.

    https://doi.org/10.1523/JNEUROSCI.1350-14.2014

    • PubMed
    • Google Scholar
69. 1. Solanto MV

    (1998) Neuropsychopharmacological mechanisms of stimulant drug action in attention-deficit hyperactivity disorder: a review and integration

    Behavioural Brain Research 94:127–152.

    https://doi.org/10.1016/S0166-4328(97)00175-7

    • PubMed
    • Google Scholar
70. 1. Sostek AJ
    2. Buchsbaum MS
    3. Rapoport JL

    (1980) Effects of amphetamine on vigilance performance in normal and hyperactive children

    Journal of Abnormal Child Psychology 8:491–500.

    https://doi.org/10.1007/BF00916502

    • PubMed
    • Google Scholar
71. 1. Sul JH
    2. Kim H
    3. Huh N
    4. Lee D
    5. Jung MW

    (2010) Distinct roles of rodent orbitofrontal and medial prefrontal cortex in decision making

    Neuron 66:449–460.

    https://doi.org/10.1016/j.neuron.2010.03.033

    • PubMed
    • Google Scholar
72. 1. Voorn P
    2. Vanderschuren LJ
    3. Groenewegen HJ
    4. Robbins TW
    5. Pennartz CM

    (2004) Putting a spin on the dorsal-ventral divide of the striatum

    Trends in Neurosciences 27:468–474.

    https://doi.org/10.1016/j.tins.2004.06.006

    • PubMed
    • Google Scholar
73. 1. Walton ME
    2. Bannerman DM
    3. Rushworth MF

    (2002) The role of rat medial frontal cortex in effort-based decision making

    The Journal of Neuroscience 22:10996–11003.

    https://doi.org/10.1523/JNEUROSCI.22-24-10996.2002

    • PubMed
    • Google Scholar
74. 1. Walton ME
    2. Bannerman DM
    3. Alterescu K
    4. Rushworth MF

    (2003) Functional specialization within medial frontal cortex of the anterior cingulate for evaluating effort-related decisions

    The Journal of Neuroscience 23:6475–6479.

    https://doi.org/10.1523/JNEUROSCI.23-16-06475.2003

    • PubMed
    • Google Scholar
75. 1. Walton ME
    2. Croxson PL
    3. Rushworth MF
    4. Bannerman DM

    (2005) The mesocortical dopamine projection to anterior cingulate cortex plays no role in guiding effort-related decisions

    Behavioral Neuroscience 119:323–328.

    https://doi.org/10.1037/0735-7044.119.1.323

    • PubMed
    • Google Scholar
76. 1. Wellman PJ
    2. Davis KW
    3. Clifford PS
    4. Rothman RB
    5. Blough BE

    (2009) Changes in feeding and locomotion induced by amphetamine analogs in rats

    Drug and Alcohol Dependence 100:234–239.

    https://doi.org/10.1016/j.drugalcdep.2008.10.005

    • PubMed
    • Google Scholar
77. Book

    1. Whishaw IQ
    2. Kolb B

    (2005) The Behavior of the Laboratory Rat: A Handbook with Tests

    Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780195162851.001.0001

    • Google Scholar
78. 1. Wilkinson LS
    2. Mittleman G
    3. Torres E
    4. Humby T
    5. Hall FS
    6. Robbins TW

    (1993) Enhancement of amphetamine-induced locomotor activity and dopamine release in nucleus accumbens following excitotoxic lesions of the Hippocampus

    Behavioural Brain Research 55:143–150.

    https://doi.org/10.1016/0166-4328(93)90110-C

    • PubMed
    • Google Scholar
79. 1. Wilson MA
    2. McNaughton BL

    (1993) Dynamics of the hippocampal ensemble code for space

    Science 261:1055–1058.

    https://doi.org/10.1126/science.8351520

    • PubMed
    • Google Scholar
80. 1. Wong SA
    2. Thapa R
    3. Badenhorst CA
    4. Briggs AR
    5. Sawada JA
    6. Gruber AJ

    (2017) Opposing effects of acute and chronic d-amphetamine on decision-making in rats

    Neuroscience 345:218–228.

    https://doi.org/10.1016/j.neuroscience.2016.04.021

    • PubMed
    • Google Scholar
81. 1. Yu BM
    2. Cunningham JP
    3. Santhanam G
    4. Ryu SI
    5. Shenoy KV
    6. Sahani M

    (2009) Gaussian-Process factor analysis for Low-Dimensional Single-Trial analysis of neural population activity

    Journal of Neurophysiology 102:2008.

    https://doi.org/10.1152/jn.90941.2008

    • Google Scholar

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

• 1,826

  views

• 149

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.