Dopamine Projection Neurons in the VTA Have Distinct Roles in Reward Association and Motivation

Post by Amanda McFarlan 

What's the science?

The ventral tegmental area (VTA) is a midbrain structure with a large population of dopaminergic neurons that innervate the two major regions of the nucleus accumbens (NAc): the core and the shell. It is well known that dopamine projection neurons from the VTA to the NAc facilitate reward association and motivation. However, how dopamine release in the two regions of the NAc acts to facilitate these distinct functions remains unclear. This week in Neuron, Heymann and colleagues investigated the role of dopamine release in the NAc core versus the NAc shell in reward association and motivation.

How did they do it?

The authors used patterns of expression of different neuropeptide-associated genes in the VTA to identify distinct populations of dopamine neurons in the VTA that project to either the core or the shell of the NAc. Then, to understand the importance of these VTA to NAc connections during reward learning, they optogenetically inhibited projection neurons from the VTA to either the NAc shell or core in mice that were being trained in a Pavlovian conditioning paradigm. In this paradigm, mice were conditioned to expect a reward following the presentation of a lever, and had to press the lever and enter their head into an area with food to receive a reward. Next, the authors determined whether optogenetic activation of dopamine projection neurons from the VTA to the NAc would be sufficient to promote intracranial optical self-stimulation (i.e. a rewarding stimulation) in mice. To do this, they targeted the expression of Channelrhodopsin-2 (an excitatory light-gated ion channel) to VTA neurons that project to either the NAc shell or core and allowed mice to lever press for optical stimulation of these neurons. Additionally, the authors investigated the role of VTA projection neurons in reward-seeking behaviour: they trained calorie-restricted mice on a fixed-ratio schedule of food reinforcement (where food is delivered after a set number of responses) for 5 days and then switched the mice to 5 days of intracranial optical self-stimulation. Finally, the authors assessed the effect of simultaneous activation of VTA projection neurons to the NAc core and shell on reward seeking behaviours. 

What did they find?

The authors found that the dopamine neurons in the VTA expressing corticotropin-releasing hormone receptor 1, preferentially innervated neurons in the NAc core, while dopamine neurons in the VTA expressing cholecystokinin, preferentially innervated neurons in the NAc shell. Then, they revealed that inhibiting VTA projection neurons that target the NAc core, but not the shell, during the Pavlovian conditioning paradigm significantly reduced the number of head entries in response to the conditioned stimulus. Similarly, they determined that optogenetic activation of VTA neurons innervating the NAc core, but not the shell, was sufficient to promote intracranial optical self-stimulation in mice, suggesting that VTA to NAc core connections are important for reward association. 

Next, the authors showed that the switch from food reinforcement to intracranial optical self-stimulation in calorie-restricted mice resulted in an acute increase in lever pressing for optogenetic activation of VTA neurons innervating the NAc core on day 1 that decreased over time. However, there was an increase in lever pressing for optogenetic activation of VTA neurons innervating the NAc shell that persisted all 5 days, suggesting that the VTA to NAc shell connections may be important for the motivation involved in maintaining reward-seeking behavior. Finally, the authors revealed that simultaneous activation of the VTA neurons that innervate the NAc core and shell resulted in robust self-stimulation in mice, suggesting that robust behavioural responses emerge from coincident activation of pathways involved in reward association and motivation.

What’s the impact?

This is the first study to show that dopamine neurons in the VTA that preferentially innervate either the NAc core or shell can be isolated using neuropeptide-associated genes. The authors revealed that dopamine neurons in the VTA that project to the NAc core are important for reward association, while dopamine neurons in the VTA that project to the NAc shell are involved in motivation. Altogether, these findings highlight how the coincident activation of both VTA to NAc pathways leads to robust behavioural changes in response to a reward. 

Heymann et al. Synergy of Distinct Dopamine Projection Populations in Behavioral Reinforcement. Neuron (2019). Access the original scientific publication here. 

The Role of the Posterior Medial Prefrontal Cortex in Confirmation Bias

Post by Shireen Parimoo 

What's the science?

Our beliefs can be influenced in many ways. For example, people often exhibit confirmation bias, which is the tendency to ignore information that is inconsistent with their existing beliefs while giving greater weight to information that confirms their beliefs. Similarly, people are more likely to be influenced by strongly expressed views than by weakly expressed views. It is important to understand how new information that could affect our decisions is processed. Activity in the posterior medial prefrontal cortex (pmPFC) has been implicated in monitoring and evaluating decisions, such as changing behavior after making a mistake. However, it is not known whether representations of beliefs in the pmPFC are sensitive to the strength (strong or weak) or type (consistent or inconsistent) of new evidence. This week in Nature Neuroscience, Kappes and colleagues used functional magnetic resonance imaging (fMRI) to investigate people’s sensitivity to the strength of new evidence and how this influences their judgments when the evidence confirms or contradicts existing beliefs.

How did they do it?

Participants completed the study in pairs across two testing sessions. In the first session, two participants individually played a real estate investment game, in which they were shown a property with a price and asked to (i) make a judgment about whether the true price was higher or lower than the price that was shown, and (ii) place a wager on their judgment (between 1 and 60 cents). They were informed that if they were correct, they would receive the amount of money that they wagered and if they were wrong, they would lose that amount. In the second session, the pairs of participants underwent fMRI scanning in adjacent rooms. They were shown the same properties as before along with their judgments and wagers. Importantly, they were also shown what were ostensibly their partner’s evaluations, which could be consistent or inconsistent with their own judgments, as well as the amount of money that their partner seemingly wagered on their judgment, which could be high (strong evidence) or low (weak evidence). Participants then had to decide whether they would change the amount of money that they wagered (but not the judgment itself) based on their partner’s evaluations. Thus, the partner’s judgment indicated whether participants saw confirmatory or contradictory evidence, and the partner’s wager amount indicated the strength of the evidence.

The authors first assessed whether participants showed confirmation bias. That is, were participants more likely to change their final wager when presented with consistent compared to contradictory evidence? Then, they determined whether participants were influenced by the strength of new evidence when it was consistent or inconsistent with their judgments. Finally, they performed a moderated mediation analysis to examine the sensitivity of the pmPFC to the type and strength of evidence. This allowed them to determine whether pmPFC activity mediated the effect of strong and weak evidence on participants’ final wagers, and if this varied based on whether the evidence was consistent or inconsistent with their own judgments.

What did they find?

Participants exhibited a confirmation bias, as they were more likely to increase their wager when their partner agreed with them. The strength of new evidence only affected behavior when the evidence was consistent with their existing beliefs. That is, participants were more likely to increase their wager when the evidence was strong (their partner made a large wager as well) than when the evidence was weak (their partner made a small wager). However, when their partner’s judgment did not align with their own judgments, the strength of the new evidence had no effect on their final wager.

The strength of new evidence was negatively correlated with pmPFC activity, but only for confirmatory evidence. Specifically, activity in the pmPFC was lower when the participant’s partner placed a higher wager on the same judgment, but pmPFC activation was not related to the partner’s wager when their judgment was inconsistent with the participants’ own judgments. Similarly, activity in the pmPFC mediated the effect of the strength of the new evidence and the participants’ final wager, but only when their partner’s judgments were consistent with their own. In other words, there was a reduction in pmPFC activity when participants were presented with strong confirmatory evidence, which was related to an increase in their final wager. Thus, the strength of new evidence only affects behavioral and neural responses when the evidence confirms existing beliefs.

What's the impact?

This study is the first to show that the pmPFC only tracks the strength of new evidence when it is consistent with prior beliefs, and that regardless of how strong the new evidence is, it does not influence behavior when it contradicts prior beliefs. These findings have important implications in numerous contexts, ranging from personal lifestyle to advocacy and policy making.

Kappes et al. Confirmation bias in the utilization of others’ opinion strength. Nature Neuroscience (2019). Access the original scientific publication here.

Frontal Cortex Neuron Types Categorically Encode Single Decision Variables

Post by Stephanie Williams

What's the science?

Individual neurons in cortical regions respond to specific features relevant to the function of the region. For example, individual neurons in primary visual cortex will fire robustly in response to  viewing a line or a bar at a particular orientation. It is not currently known whether individual neurons across all cortical regions — especially in the frontal cortex which is involved in complex behavior — respond to specific sensory or task-related features, or whether information can only be read out from populations of neurons. This week in Nature, Hirokawa and colleagues show that the activity of individual neurons in rat orbitofrontal cortex covaries with individual decision variables.

How did they do it?                             

The authors examine the relationship between single neuron activity in rat orbitofrontal cortex and a set of individual choice variables by 1) recording behavioral and neural data from cohorts of rats performing a complex decision task and 2) developing a computational model of behavior for the decision task. They use the variables defined in their behavioral model to interpret the trends they observe in their neural data.

The decision task was a reward-biased olfactory decision task that required rats to make both perceptual and value-guided decisions. In the task, the rats sampled a mixture of two odors, and then had to choose between either the left or right port to receive a water reward. To make a correct choice, the rats needed to choose the port that corresponded to the dominant odor in the mixture. The structure of the task allowed it to be broken into several different epochs, which the authors analyzed separately. For example, the “reward anticipation epoch” consisted of the time period after the rat had entered a port to make the left or right choice, and before the rat received feedback on its choice. To vary perceptual uncertainty, the authors varied the concentration ratio of the odors in the mixture (5% to 95%) across trials. To vary reward expectations, the authors changed the amount of water given to the rats in the left or right ports across blocks. The authors recorded from hundreds of neurons in the orbitofrontal cortex of 9 rats while they performed the task.

The computational model the authors developed to represent decision choices incorporated variables that represented the reward context (eg. the size of the potential reward and the presence of a reward, previous rewards), variables that represented the sensory evidence that the rats used to represent the expected reward, and the amount of confidence in their decision. The authors first identified individual orbitofrontal neurons that covaried with particular variables in their decision model. Then, to ensure the neurons they identified were representative of the orbitofrontal neural population, the authors used a model-free approach to identify clusters of neurons that showed similar responses. The authors conducted their first set of analyses on a particular epoch of the task, the reward anticipation epoch, and then repeated their clustering procedure on two other epochs (the stimulus epoch and the feedback epoch). To understand whether the orbitofrontal neuron clusters represented individual task variables, decision variables, or mixtures of variables, the authors fit several regression models to the cluster-averaged response of neurons. Some of the models they used consisted of variables that were randomly mixed, which they compared to the models consisting of unmixed elementary decision variables.

To understand whether there was anatomical organization underlying the response profiles of the neural clusters, the authors examined neurons projecting to the striatum using a retrograde virus to target striatal-projecting neurons (this virus traces neurons back to where they originate from - the orbitofrontal cortex). They analyzed the activity of the tagged neurons in the context of the decision variables in their behavior model. The authors then repeated all of their analyses on an independent cohort of rats to confirm the reproducibility of their results. 

What did they find?

When the authors analyzed the activity of neurons during the reward anticipation epoch of the task, they found individual neurons encoded specific variables in their behavioral choice model. For example, the authors identified individual neurons that were specifically active in proportion to the amount of evidence that supported their choice (eg. their confidence in their choice), and were not influenced by the amount of expected reward. They identified similar neurons for the decision variables in their model, including the anticipated reward size and the choice value, defined as probability of a reward multiplied by the reward size. When the authors used the model-free approach to cluster OFC neurons according to their response profiles, they found that there were nine primary clusters for the reward anticipation epoch. Each of the clusters they identified using their model-free approach resembled a variable in their computational model of behavior. These variables included decision confidence, integrated value, previous outcomes, and reward size. The authors confirmed that each cluster corresponded to a decision variable in several ways. Using regression analysis, they showed that the orbitofrontal neurons could be represented in a space made up of task-relevant variables, and that the average cluster neural response was best described by the individual variables in their decision model, rather than random mixtures of those variables. When the authors analyzed the activity of orbitofrontal neurons that projected to striatum, they found that a similar pattern across all of the striatum-projecting neurons, which matched one of the clusters they had previously identified, and encoded information about the trial outcome. This result suggests that the decision-variable-specific clusters of orbitofrontal neuron responses may be supported by cell-type specific circuit organization.

What's the impact?

The authors show that despite the complexity of frontal cortex computations, individual orbitofrontal neurons in rats do not encode random mixtures of multiple task variables, and instead encode information about individual decision variables. These findings provide new insights into the architectural logic of frontal cortex, with each neuron encoding distinct internal variables that have specific computational functions supporting behavior.

Hirokawa et al. Frontal cortex neuron types categorically encode single decision variables. Nature. (2019). Access the original scientific publication here.