The Role of Cocaine and Dopamine D2 Receptors in Conditioned Behaviors

Post by Andrew Vo

What's the science?

Addictive drugs are known to alter brain circuits — specifically the midbrain dopamine system that innervates the dorsal striatum (DSt) and nucleus accumbens (NAc). Dopamine signaling can have different effects in these distinct brain regions depending on the class of dopamine receptor the dopamine reaches: D1 receptors excite medium spiny neurons (MSNs), while D2 receptors have inhibitory effects on behaviour. Although the role of D2 receptors (D2Rs) in motivated behavior is understood and the impact of D2R dysregulation following long-term drug use in addiction is well-established, it remains unknown how initial cocaine exposure regulates D2R signaling in a region-specific manner to alter conditioned behaviors. This week in Neuron, Gong et al. investigated the cellular mechanisms underlying the effect of repeated cocaine exposure on drug-seeking behaviors in mice.

How did they do it?

The authors began by examining a) the effect of repeated cocaine exposure on D2R sensitivity in D2-MSNs and b) whether this effect differed between the DSt and NAc. To measure D2R signaling in D2-MSNs, electrophysiological activity from D2-MSNs was recorded during cocaine exposure while modulating the concentration of dopamine stimulation, generating a dose-response curve. Mice were repeatedly exposed to cocaine for 7 days, followed by a 14-day withdrawal period, before a single injection challenge of cocaine. Response curves were generated after each of these phases.

Next, the authors set out to determine the exact mechanism underlying changes in D2R sensitivity following cocaine exposure. To test whether these changes were caused by alterations in D2R levels, they sampled tissues from the DSt and NAc of mice treated with either cocaine or a saline control and measured the relative density of D2Rs using immunoblotting (a technique for analyzing proteins in a sample using antibody staining). They also generated mice in which D2R levels were either (1) knocked down or (2) overexpressed and observed any changes in the effects of cocaine exposure on D2R sensitivity. To test whether these changes were instead caused by regulation of G proteins, which tightly couple with D2Rs to facilitate dopamine signaling, they sampled tissues from the DSt and NAc of mice following acute, chronic, withdrawn, and cocaine-challenge compared to saline controls and measured G protein levels using western blotting (which detects the type and amount of a specific protein in a mixture). Further, they measured the effects of cocaine exposure on D2R sensitivity in G protein knockdown mice and again after G protein re-expression via a viral rescue procedure.

The authors were also interested in the behavioral effects of cocaine-mediated changes in D2R sensitivity. Using a conditioned place preference paradigm, in which mice learn to associate a previously neutral chamber with a drug, they compared behavior of G protein knockdown mice to controls, as well as following re-expression of G protein levels. In a self-administration task that assessed reinforcement learning, G protein knockdown and control mice were trained to self-administer cocaine in response to a cue, followed by an abstinence period, before a final relapse test.

Finally, the authors explored potential mechanisms that could capture the effect of cocaine exposure on D2R sensitivity changes. They tested the effects of increasing (i.e., administering a dopamine precursor or D2R-specific agonist) or decreasing (i.e., administering D2R or D1R antagonists) dopamine stimulation. They also examined the effects of chemogenetic inactivation of D1Rs in either D1-MSNs or the prefrontal cortex. Last, they tested the role of NMDA plasticity by administering an NMDA-receptor antagonist.

What did they find?

Repeated exposure to cocaine caused a rightward shift in the dose-response curve in the NAc but not DSt, indicating a region-specific reduction in D2R sensitivity. This reduced D2R sensitivity returned to baseline levels after a drug withdrawal period but was immediately reinstated following a single challenge injection of cocaine.

Immunoblotting revealed similar levels of D2R expression in both DSt and NAc in cocaine-treated mice compared to controls. Reducing or enhancing D2R levels, via knockdown or overexpression respectively, did not prevent cocaine-associated decreases in D2R sensitivity in the NAc. Western blotting showed that cocaine exposure reduced G protein levels in the NAc but not DSt. These altered levels returned to baseline after a withdrawal period but were reinstated following a single injection challenge of cocaine. Decreasing G protein levels via knockdown mice successfully reduced D2R sensitivity in the NAc and blocked the effect of chronic cocaine exposure. Viral rescue of G protein levels in these mice recovered the cocaine-associated reduction in D2R sensitivity in NAc. Collectively, these findings indicate that D2R sensitivity changes following cocaine exposure occur independently of changes in D2R levels and are instead related to the regulation of G proteins.

Cocaine conditioned place preference caused a reduction in G protein levels in the NAc. G protein knock-down mice were found to spend more time in the drug-associated chamber. This preference was eliminated after re-expression of G protein levels via viral rescue. In the self-administration task, knock-down mice were unaffected in their ability to self-administer cocaine in response to a cue. Following an abstinence period and subsequent relapse test, these knock-down mice were unaffected in the reinstatement of drug-seeking. Taken together, these results suggest that cocaine-induced changes in G protein expression leading to a reduction in D2R sensitivity in the NAc underlie conditioned drug-seeking behaviors.  

Increasing extracellular dopamine levels did not affect D2R sensitivity, unlike the result of cocaine exposure. D2R antagonism before cocaine exposure did not block reductions in D2R sensitivity in the NAc. In contrast, D1R antagonism could successfully block this cocaine-mediated effect. Further examining the role of D1R regulation of D2R-MSN sensitivity, inactivation of D1Rs in PFC but not D1-MSNs blocked the effect of cocaine on D2R sensitivity in NAc. A similar effect could be achieved by blocking NMDA receptors. These findings illustrate a regulatory role of D1R inputs from the PFC on D2R sensitivity.

What's the impact?

In summary, this study demonstrated that initial chronic exposure to cocaine reduced the sensitivity—but not the level—of D2Rs specifically in the NAc of mice. Reduced D2R sensitivity was caused by decreased expression of G protein in D2-MSNs following cocaine exposure. Together, these changes promoted conditioned drug-seeking behaviors. Uncovering the neural mechanisms through which initial drug exposure regulates drug-seeking behavior has important implications for the treatment of addiction and relapse.

Gong et al. Cocaine shifts dopamine D2 receptor sensitivity to gate conditioned behaviors. Neuron (2021). Access the original scientific publication here.

The Impact of Social Learners on Collective Decision-Making

Post by Lani Cupo

What's the science?

In democratic societies, collective decisions, such as who should hold power, or what action should be taken to address climate change, can drastically impact society. But when people make decisions in groups, the most popular option is sometimes chosen even though it does not have the most merit. This phenomenon is due in part to the presence of those identified here as social learners who adopt the opinions of others instead of critically assessing options for themselves. This week in PNAS, Yang and colleagues developed a mathematical framework for investigating whether there is a critical threshold of social learners that can be present in a collective decision, after which one option may prevail because of popularity, rather than merit. 

How did they do it?

The authors created a dynamical system model which integrated two options (X and Y) with relative merit (m) associated with each option, where m was a number between zero and one. The model incorporated differing proportions of social and independent learners (s) in the population. Finally, it included one parameter as a function that is hypothesized to model two types of conformity, normative (engaging in a behavior because others do it), and informational (engaging in a behavior because it is the right thing to do). The authors derived transition rates between the options for the different types of learners, where social learners will transition between the options based on the popularity of the option, but independent learners transition based on the merit of the option. This allowed the authors to examine the fixed points of the equation, where the proportion of people favoring a given option stops changing. They also investigated how stable these fixed points are when the model is perturbed. Their conclusions remained the same when they changed the model to account for opinion on a spectrum from independent to social, when they only allowed individuals to be impacted only by their local environment, and when they introduced statistical noise to the model. Finally, the researchers simulated a model incorporating the strength of opinion weighting towards option X or Y. 

What did they find?

When X and Y are options with equal merit, there is a critical threshold for the proportion of social learners after which the model bifurcates into two branches, implying either option X or Y could be selected. In the case that the model is not equal between groups, the majority will favor the more meritorious option up to a critical point, but if the proportion of social learners is too high, instability is introduced into the model, meaning there can be cases in collective decision making where the less meritorious option is still chosen. The critical threshold is determined both by the discrepancy in merit between the two options and the conformity function. The threshold that these parameters identify predicts the threshold above which the proportion of social learners harms the decision. Notably, the model is flexible to adapt to different behaviors modelled through the conformity function or to allow parameters, such as the strength of opinions, meaning it represents a flexible tool that can be used to model different situations. 

What's the impact?

This study investigated the impact of social learners on collective decision-making, demonstrating there is a threshold above which social learners may negatively impact the outcome of collective decisions. The outcome of collective decisions can drastically impact daily life—not only in small communities but on a national and global scale as well. The mathematical framework presented provides future studies with the ability to examine social learning in varied and complex scenarios. 

Yan et al. Dynamical system model predicts when social learners impair collective performance. PNAS (2021). Access the original scientific publication here

Virtual Reality-Based Cognitive and Behavioral Therapy for Anxiety and Depression

Post by Leanna Kalinowski

A need for anxiety and depression treatment

Anxiety and depression are the most commonly diagnosed mental disorders, impacting the lives of millions of adults across the globe. While they are distinct entities, up to 60% of adults with an anxiety disorder also suffer from depressive symptoms and vice versa. Cognitive-behavioral therapy is an effective treatment for both — either alone or in combination with pharmacotherapy.  

What is Cognitive-Behavioral Therapy?

Cognitive-behavioral therapy (CBT) is a form of psychotherapy comprising a wide range of cognitive and behavioral interventions. CBT is based on the idea that behavioral changes lead to changes in emotion and cognition, and that cognitive changes lead to changes in emotion and behavior. With both strategies combined, CBT teaches patients how to identify and change their thought patterns related to the behavioral and emotional reactions that cause harm.

There are a handful of CBT techniques commonly used for people with anxiety and depression, including:

1) Psychoeducation. This is often the first step in CBT, where patients are taught how CBT works and provided the rationale for why it will be beneficial to them. Once patients learn how CBT works, they are typically able to easily apply it to their own lives in the absence of any future intervention.

2) Behavioral Activation. People with depression often withdraw from activities that previously provided natural reinforcement, leading to a cycle in which they remain inactive and do not experience the reinforcement from these activities. Behavioral activation aims to work with patients to set goals to engage in these rewarding activities (e.g., exercising or hanging out with friends) and break them out of this cycle of inactivity. 

3) Cognitive Restructuring. People with anxiety and depression often engage in negative thought patterns, including overgeneralizing and catastrophizing. Cognitive restructuring aims to help patients notice when they are engaging in these negative thought patterns and empower them with the tools needed to change these thought patterns.  

4) Exposure-based Therapy, which is especially common for people with anxiety. This therapy is based on emotional processing theory, which states that fear is represented through associative networks that maintain information about the feared stimulus (e.g., public speaking), the response to the feared stimulus (e.g., escape, avoidance, physiological responses), and the meaning of the stimulus and response (e.g., public speaking = increased heart rate = danger). In this example, exposure to public speaking in a controlled environment provides new information to indicate that public speaking isn’t that scary or dangerous, leading to a decrease in fear. 

While CBT is an effective treatment for anxiety and depression, a variety of therapy gaps exist. CBT often helps with initial treatment but is not as effective at preventing relapse of symptoms. Furthermore, CBT typically requires 10 to 20 sessions, each up to an hour-long, with guidance from a mental health professional. These gaps pose a problem for many adults, which has led to a push from mental health professionals to develop accessible ways in which CBT can be delivered, such as through smartphone apps and virtual reality technology. 

What is virtual reality technology? 

Virtual reality (VR) is a wearable technology that allows the user to feel fully immersed in a virtual world. VR commonly consists of a head-mounted display that blocks out the outside world while displaying a computer-generated, yet realistic world. This technology allows users to look around, move around, and interact with objects within the virtual world. It is often supplemented with auditory and tactile (e.g., vibrations) stimuli to mimic the real world. VR has been used in video games, military training, and business meetings is relatively affordable, and is compatible with most modern smartphones. These features make it an attractive option to enhance CBT.

How can virtual reality be used for CBT?

VR therapy is a promising avenue for implementing CBT, and Lindner and colleagues provide a comprehensive review into how some of the common approaches to CBT outlined above can be tapped into using the VR world. For example, several VR games available on the market allow users to engage in physical activity (e.g., VR boxing and tennis) and social gatherings (e.g., VR karaoke and concerts). Engaging in these activities can help patients experience the natural reinforcement required for behavioral activation therapy. 

Another example of how CBT can be adapted for a VR world is seen with virtual cognitive restructuring therapy. When this type of CBT is utilized in person, the patient must imagine an example situation to practice these cognitive restructuring techniques. However, with VR, the patient can be placed directly into these situations, without needing to imagine them, report negative thoughts by placing them into speech bubbles, and manipulate those thoughts using a virtual eraser. Engaging in these activities can help patients practice cognitive restructuring techniques in a more realistic setting.

Exposure therapy can also easily be accomplished using VR by exposing the patient to stimuli that lead to anxiety. For example, someone with a fear of spiders can undergo a VR task in which they are placed in a room with virtual spiders. Like in-person exposure therapy, this empowers the patient to change the associative networks that associate spiders with fear, leading to a decrease in anxiety. 

However, not all approaches to in-person CBT are as easily transferable to a VR context. For example, it is difficult to simulate psychoeducation using VR; evidence suggests that this method is better suited to be administered in-person or through a smartphone app that does not utilize VR. 

What’s the bottom line?

Many of the approaches to treating anxiety and depression with CBT are easily achievable using VR technology. This, coupled with its cost and accessibility, makes VR a promising avenue for enhancing CBT. Further research is needed to determine the effectiveness of VR-based CBT (1) in contrast to in-person CBT, (2) in combination with pharmacotherapy, and (3) when treating initial symptoms in addition to relapse of symptoms.

References

Ballenger. Anxiety and depression: Optimizing treatments. 2000. The Primary Care Companion to the Journal of Clinical Psychiatry. Access the publication here. 

Hundt et al. The relationship between use of CBT and depression treatment outcome: A theoretical and methodological review of the literature. 2013. Behavior Therapy. Access the publication here. 

Ioannou et al. Virtual reality and symptoms management of anxiety, depression, fatigue, and pain: A systematic review. 2020. SAGE Open Nursing. Access the publication here.

Kaczkurkin & Foa. Cognitive-behavioral therapy for anxiety disorders: An update on the empirical evidence. 2015. Dialogues in Clinical Neuroscience. Access the publication here. 

Lindner et al. How to treat depression with low-intensity virtual reality interventions: Perspectives on translating cognitive behavioral techniques into the virtual reality modality and how to make anti-depressive use of virtual reality-unique experiences. 2019. Frontiers in Psychiatry. Access the publication here.

Maples-Keller et al. The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. 2017. Harvard Reviews Psychiatry. Access the publication here. 

Roshanaei-Moghaddam et al. Relative effects of CBT and pharmacotherapy in depression versus anxiety: Is medication somewhat better for depression, and CBT somewhat better for anxiety? 2011. Depression and Anxiety. Access the publication here.