Effects of dopamine on reinforcement learning and consolidation in Parkinson's disease

Valence-dependent dopaminergic modulation during reversal learning in Parkinson's disease: A neurocomputational approach

Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system's influence on learning, particularly in Parkinson's disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms. In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects. In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al. As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks. Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks.

Monoaminergic Modulation of Learning and Cognitive Function in the Prefrontal Cortex

Extensive research has shed light on the cellular and functional underpinnings of higher cognition as influenced by the prefrontal cortex. Neurotransmitters act as key regulatory molecules within the PFC to assist with synchronizing cognitive state and arousal levels. The monoamine family of neurotransmitters, including dopamine, serotonin, and norepinephrine, play multifaceted roles in the cognitive processes behind learning and memory. The present review explores the organization and signaling patterns of monoamines within the PFC, as well as elucidates the numerous roles played by monoamines in learning and higher cognitive function.

Lasting dynamic effects of the psychedelic 2,5-dimethoxy-4-iodoamphetamine ((±)-DOI) on cognitive flexibility

Psychedelic drugs can aid fast and lasting remission from various neuropsychiatric disorders, though the underlying mechanisms remain unclear. Preclinical studies suggest serotonergic psychedelics enhance neuronal plasticity, but whether neuroplastic changes can also be seen at cognitive and behavioural levels is unexplored. Here we show that a single dose of the psychedelic 2,5-dimethoxy-4-iodoamphetamine ((±)-DOI) affects structural brain plasticity and cognitive flexibility in young adult mice beyond the acute drug experience. Using ex vivo magnetic resonance imaging, we show increased volumes of several sensory and association areas one day after systemic administration of 2 mgkg-1 (±)-DOI. We then demonstrate lasting effects of (±)-DOI on cognitive flexibility in a two-step probabilistic reversal learning task where 2 mgkg-1 (±)-DOI improved the rate of adaptation to a novel reversal in task structure occurring one-week post-treatment. Strikingly, (±)-DOI-treated mice started learning from reward omissions, a unique strategy not typically seen in mice in this task, suggesting heightened sensitivity to previously overlooked cues. Crucially, further experiments revealed that (±)-DOI's effects on cognitive flexibility were contingent on the timing between drug treatment and the novel reversal, as well as on the nature of the intervening experience. (±)-DOI's facilitation of both cognitive adaptation and novel thinking strategies may contribute to the clinical benefits of psychedelic-assisted therapy, particularly in cases of perseverative behaviours and a resistance to change seen in depression, anxiety, or addiction. Furthermore, our findings highlight the crucial role of time-dependent neuroplasticity and the influence of experiential factors in shaping the therapeutic potential of psychedelic interventions for impaired cognitive flexibility.

Non-motor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease

Parkinson's disease (PD) is characterized by three main motor symptoms: bradykinesia, rigidity and tremor. PD is also associated with diverse non-motor symptoms that may develop in parallel or precede motor dysfunctions, ranging from autonomic system dysfunctions and impaired sensory perception to cognitive deficits and depression. Here, we examine the role of the progressive loss of dopaminergic transmission in behaviors related to the non-motor symptoms of PD in a mouse model of the disease (the TIF-IADATCreERT2 strain). We found that in the period from 5 to 12 weeks after the induction of a gradual loss of dopaminergic neurons, mild motor symptoms became detectable, including changes in the distance between paws while standing as well as the swing speed and step sequence. Male mutant mice showed no apparent changes in olfactory acuity, no anhedonia-like behaviors, and normal learning in an instrumental task; however, a pronounced increase in the number of operant responses performed was noted. Similarly, female mice with progressive dopaminergic neuron degeneration showed normal learning in the probabilistic reversal learning task and no loss of sweet-taste preference, but again, a robustly higher number of choices were performed in the task. In both males and females, the higher number of instrumental responses did not affect the accuracy or the fraction of rewarded responses. Taken together, these data reveal discrete, dopamine-dependent non-motor symptoms that emerge in the early stages of dopaminergic neuron degeneration.

Evaluating the Influence of Musical and Monetary Rewards on Decision Making through Computational Modelling

A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised by probabilistic associations between stimuli and rewards, with probabilities changing over time to reflect environmental volatility. In each task, choices were reinforced either by monetary outcomes (win/lose) or by the endings of musical melodies (consonant/dissonant). We applied the Hierarchical Gaussian Filter, a validated hierarchical Bayesian framework, to model learning under these two conditions. Bayesian statistics provided evidence for similar learning patterns across both reward types, suggesting individuals' similar adaptability. However, within the musical task, individual preferences for consonance over dissonance explained some aspects of learning. Specifically, correlation analyses indicated that participants more tolerant of dissonance behaved more stochastically in their belief-to-response mappings and were less likely to choose the response associated with the current prediction for a consonant ending, driven by higher volatility estimates. By contrast, participants averse to dissonance showed increased tonic volatility, leading to larger updates in reward tendency beliefs.

Dopamine-independent effect of rewards on choices through hidden-state inference

Dopamine is implicated in adaptive behavior through reward prediction error (RPE) signals that update value estimates. There is also accumulating evidence that animals in structured environments can use inference processes to facilitate behavioral flexibility. However, it is unclear how these two accounts of reward-guided decision-making should be integrated. Using a two-step task for mice, we show that dopamine reports RPEs using value information inferred from task structure knowledge, alongside information about reward rate and movement. Nonetheless, although rewards strongly influenced choices and dopamine activity, neither activating nor inhibiting dopamine neurons at trial outcome affected future choice. These data were recapitulated by a neural network model where cortex learned to track hidden task states by predicting observations, while basal ganglia learned values and actions via RPEs. This shows that the influence of rewards on choices can stem from dopamine-independent information they convey about the world's state, not the dopaminergic RPEs they produce.

Dopamine Increases Accuracy and Lengthens Deliberation Time in Explicit Motor Skill Learning

Although animal research implicates a central role for dopamine in motor skill learning, a direct causal link has yet to be established in neurotypical humans. Here, we tested if a pharmacological manipulation of dopamine alters motor learning, using a paradigm which engaged explicit, goal-directed strategies. Participants (27 females; 11 males; aged 18-29 years) first consumed either 100 mg of levodopa (n = 19), a dopamine precursor that increases dopamine availability, or placebo (n = 19). Then, during training, participants learnt the explicit strategy of aiming away from presented targets by instructed angles of varying sizes. Targets jumped mid-movement by the instructed aiming angle. Task success was thus contingent upon aiming accuracy and not speed. The effect of the dopamine manipulations on skill learning was assessed during training and after an overnight follow-up. Increasing dopamine availability at training improved aiming accuracy and lengthened reaction times, particularly for larger, more difficult aiming angles, both at training and, importantly, at follow-up, despite prominent session-by-session performance improvements in both accuracy and speed. Exogenous dopamine thus seems to result in a learnt, persistent propensity to better adhere to task goals. Results support the proposal that dopamine is important in engagement of instrumental motivation to optimize adherence to task goals, particularly when learning to execute goal-directed strategies in motor skill learning.

Impulse control disorder in Parkinson's disease is associated with abnormal frontal value signalling

Dopaminergic medication is well established to boost reward- versus punishment-based learning in Parkinson's disease. However, there is tremendous variability in dopaminergic medication effects across different individuals, with some patients exhibiting much greater cognitive sensitivity to medication than others. We aimed to unravel the mechanisms underlying this individual variability in a large heterogeneous sample of early-stage patients with Parkinson's disease as a function of comorbid neuropsychiatric symptomatology, in particular impulse control disorders and depression. One hundred and ninety-nine patients with Parkinson's disease (138 ON medication and 61 OFF medication) and 59 healthy controls were scanned with functional MRI while they performed an established probabilistic instrumental learning task. Reinforcement learning model-based analyses revealed medication group differences in learning from gains versus losses, but only in patients with impulse control disorders. Furthermore, expected-value related brain signalling in the ventromedial prefrontal cortex was increased in patients with impulse control disorders ON medication compared with those OFF medication, while striatal reward prediction error signalling remained unaltered. These data substantiate the hypothesis that dopamine's effects on reinforcement learning in Parkinson's disease vary with individual differences in comorbid impulse control disorder and suggest they reflect deficient computation of value in medial frontal cortex, rather than deficient reward prediction error signalling in striatum. See Michael Browning (https://doi.org/10.1093/brain/awad248) for a scientific commentary on this article.

Apathy in Parkinson's Disease: Clinical Patterns and Neurobiological Basis

Apathy is commonly defined as a loss of motivation leading to a reduction in goal-directed behaviors. This multidimensional syndrome, which includes cognitive, emotional and behavioral components, is one of the most prevalent neuropsychiatric features of Parkinson's disease (PD). It has been established that the prevalence of apathy increases as PD progresses. However, the pathophysiology and anatomic substrate of this syndrome remain unclear. Apathy seems to be underpinned by impaired anatomical structures that link the prefrontal cortex with the limbic system. It can be encountered in the prodromal stage of the disease and in fluctuating PD patients receiving bilateral chronic subthalamic nucleus stimulation. In these stages, apathy may be considered as a disorder of motivation that embodies amotivational behavioral syndrome, is underpinned by combined dopaminergic and serotonergic denervation and is dopa-responsive. In contrast, in advanced PD patients, apathy may be considered as cognitive apathy that announces cognitive decline and PD dementia, is underpinned by diffuse neurotransmitter system dysfunction and Lewy pathology spreading and is no longer dopa-responsive. In this review, we discuss the clinical patterns of apathy and their treatment, the neurobiological basis of apathy, the potential role of the anatomical structures involved and the pathways in motivational and cognitive apathy.

Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

Angiotensin blockade enhances motivational reward learning via enhancing striatal prediction error signaling and frontostriatal communication

Adaptive human learning utilizes reward prediction errors (RPEs) that scale the differences between expected and actual outcomes to optimize future choices. Depression has been linked with biased RPE signaling and an exaggerated impact of negative outcomes on learning which may promote amotivation and anhedonia. The present proof-of-concept study combined computational modeling and multivariate decoding with neuroimaging to determine the influence of the selective competitive angiotensin II type 1 receptor antagonist losartan on learning from positive or negative outcomes and the underlying neural mechanisms in healthy humans. In a double-blind, between-subjects, placebo-controlled pharmaco-fMRI experiment, 61 healthy male participants (losartan, n = 30; placebo, n = 31) underwent a probabilistic selection reinforcement learning task incorporating a learning and transfer phase. Losartan improved choice accuracy for the hardest stimulus pair via increasing expected value sensitivity towards the rewarding stimulus relative to the placebo group during learning. Computational modeling revealed that losartan reduced the learning rate for negative outcomes and increased exploitatory choice behaviors while preserving learning for positive outcomes. These behavioral patterns were paralleled on the neural level by increased RPE signaling in orbitofrontal-striatal regions and enhanced positive outcome representations in the ventral striatum (VS) following losartan. In the transfer phase, losartan accelerated response times and enhanced VS functional connectivity with left dorsolateral prefrontal cortex when approaching maximum rewards. These findings elucidate the potential of losartan to reduce the impact of negative outcomes during learning and subsequently facilitate motivational approach towards maximum rewards in the transfer of learning. This may indicate a promising therapeutic mechanism to normalize distorted reward learning and fronto-striatal functioning in depression.

A failure of sleep-dependent consolidation of visuoperceptual procedural learning in young adults with ADHD

ADHD has been associated with cortico-striatal dysfunction that may lead to procedural memory abnormalities. Sleep plays a critical role in consolidating procedural memories, and sleep problems are an integral part of the psychopathology of ADHD. This raises the possibility that altered sleep processes characterizing those with ADHD could contribute to their skill-learning impairments. On this basis, the present study tested the hypothesis that young adults with ADHD have altered sleep-dependent procedural memory consolidation. Participants with ADHD and neurotypicals were trained on a visual discrimination task that has been shown to benefit from sleep. Half of the participants were tested after a 12-h break that included nocturnal sleep (sleep condition), whereas the other half were tested after a 12-h daytime break that did not include sleep (wakefulness condition) to assess the specific contribution of sleep to improvement in task performance. Despite having a similar degree of initial learning, participants with ADHD did not improve in the visual discrimination task following a sleep interval compared to neurotypicals, while they were on par with neurotypicals during the wakefulness condition. These findings represent the first demonstration of a failure in sleep-dependent consolidation of procedural learning in young adults with ADHD. Such a failure is likely to disrupt automatic control routines that are normally provided by the non-declarative memory system, thereby increasing the load on attentional resources of individuals with ADHD.

Dopaminergic medication increases motivation to exert cognitive control by reducing subjective effort costs in Parkinson's patients

Engaging in demanding mental activities requires the allocation of cognitive control, which can be effortful and aversive. Individuals thus tend to avoid exerting cognitive effort if less demanding behavioral options are available. Recent accounts propose a key role for dopamine in motivating behavior by increasing the sensitivity to rewards associated with effort exertion. Whether dopamine additionally plays a specific role in modulating the sensitivity to the costs of cognitive effort, even in the absence of any incentives, is much less clear. To address this question, we assessed cognitive effort avoidance in patients (n = 38) with Parkinson's disease, a condition characterized by loss of midbrain dopaminergic neurons, both ON and OFF dopaminergic medication and compared them to healthy controls (n = 24). Effort avoidance was assessed using the Demand Selection Task (DST), in which participants could freely choose between performing a high-demand or a low-demand version of a task-switching paradigm. Critically, participants were not offered any incentives to choose the more effortful option, nor for good performance. While healthy controls and patients OFF their dopaminergic medications consistently preferred the low-demand option, effort avoidance in patients ON dopaminergic medications was reduced compared to patients OFF, a difference which seems to lessen over trials. These differences in preference could not be explained by altered task-switching performance. Although patients ON were less accurate at detecting the different effort levels, as measured during instructed forced-choice blocks, their detection ability was not associated with effort avoidance, unlike in the healthy controls and the patients OFF. Our findings provide evidence that dopamine replacement in Parkinson's patients increases the willingness to engage in cognitively demanding behavior, and that this cannot be explained by possible effects of dopamine replacement on performance nor on the ability to detect effort demands. These results suggest that dopamine plays a role in reducing the sensitivity to effort costs that is independent of its role in enhancing the sensitivity to the benefits of effort exertion.

Role of dopamine and clinical heterogeneity in cognitive dysfunction in Parkinson's disease

Parkinson's disease (PD) is commonly treated with dopaminergic medication, which enhances some, while impairing other cognitive functions. It can even contribute to impulse control disorder and addiction. We describe the history of research supporting the dopamine overdose hypothesis, which accounts for the large within-patient variability in dopaminergic medication effects across different tasks by referring to the spatially non-uniform pattern of dopamine depletion in dorsal versus ventral striatum. However, there is tremendous variability in dopaminergic medication effects not just within patients across distinct tasks, but also across different patients. In the second part of this chapter we review recent studies addressing the large individual variability in the negative side effects of dopaminergic medication on functions that implicate dopamine, such as value-based learning and choice. These studies begin to unravel the mechanisms of dopamine overdosing, thus revising the strict version of the overdose hypothesis. For example, the work shows that the canonical boosting of reward-versus punishment-based choice by medication is greater in patients with depression and a non-tremor phenotype, which both implicate, among other pathology, more rather than less severe dysregulation of the mesolimbic dopamine system. Future longitudinal cohort studies are needed to identify how to optimally combine different clinical, personality, cognitive, neural, genetic and molecular predictors of detrimental medication effects in order to account for as much of the relevant variability as possible. This will provide a useful tool for precision neurology, allowing individual and contextual tailoring of (the dose of) dopaminergic medication in order to maximize its cognitive benefits, yet minimize its side effects.

Investigation of reward learning and feedback sensitivity in non-clinical participants with a history of early life stress

Early life stress (ELS) is an important risk factor for the development of depression. Impairments in reward learning and feedback sensitivity are suggested to be an intermediate phenotype in depression aetiology therefore we hypothesised that healthy adults with a history of ELS would exhibit reward processing deficits independent of any current depressive symptoms. We recruited 64 adults with high levels of ELS and no diagnosis of a current mental health disorder and 65 controls. Participants completed the probabilistic reversal learning task and probabilistic reward task followed by depression, anhedonia, social status, and stress scales. Participants with high levels of ELS showed decreased positive feedback sensitivity in the probabilistic reversal learning task compared to controls. High ELS participants also trended towards possessing a decreased model-free learning rate. This was coupled with a decreased learning ability in the acquisition phase of block 1 following the practice session. Neither group showed a reward induced response bias in the probabilistic reward task however high ELS participants exhibited decreased stimuli discrimination. Overall, these data suggest that healthy participants without a current mental health diagnosis but with high levels of ELS show deficits in positive feedback sensitivity and reward learning in the probabilistic reversal learning task that are distinct from depressed patients. These deficits may be relevant to increased depression vulnerability.

Acute stress blunts prediction error signals in the dorsal striatum during reinforcement learning

Acute stress is pervasive in everyday modern life and is thought to affect how people make choices and learn from them. Reinforcement learning, which implicates learning from the unexpected rewarding and punishing outcomes of our choices (i.e., prediction errors), is critical for adjusted behaviour and seems to be affected by acute stress. However, the neural mechanisms by which acute stress disrupts this type of learning are still poorly understood. Here, we investigate whether and how acute stress blunts neural signalling of prediction errors during reinforcement learning using model-based functional magnetic resonance imaging. Male participants completed a well-established reinforcement-learning task involving monetary gains and losses whilst under stress and control conditions. Acute stress impaired participants’ (n = 23) behavioural performance towards obtaining monetary gains (p < 0.001), but not towards avoiding losses (p = 0.57). Importantly, acute stress blunted signalling of prediction errors during gain and loss trials in the dorsal striatum (p = 0.040) — with subsidiary analyses suggesting that acute stress preferentially blunted signalling of positive prediction errors. Our results thus reveal a neurocomputational mechanism by which acute stress may impair reward learning.

Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period

Neuroscience research has illuminated the mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and midbrain dopamine system. However, in humans, short-term working memory that is dependent on frontal and parietal cortices can also play an important role, particularly in commonly used paradigms in which learning is relatively condensed in time. Given the growing use of reward-based learning tasks in translational studies in computational psychiatry, it is important to understand the extent of the influence of working memory and also how core gradual learning mechanisms can be better isolated. In our experiments, we manipulated the spacing between repetitions along with a post-learning delay preceding a test phase. We found that learning was slower for stimuli repeated after a long delay (spaced-trained) compared to those repeated immediately (massed-trained), likely reflecting the remaining contribution of feedback learning mechanisms when working memory is not available. For massed learning, brief interruptions led to drops in subsequent performance, and individual differences in working memory capacity positively correlated with overall performance. Interestingly, when tested after a delay period but not immediately, relative preferences decayed in the massed condition and increased in the spaced condition. Our results provide additional support for a large role of working memory in reward-based learning in temporally condensed designs. We suggest that spacing training within or between sessions is a promising approach to better isolate and understand mechanisms supporting gradual reward-based learning, with particular importance for understanding potential learning dysfunctions in addiction and psychiatric disorders.

Learning offline: memory replay in biological and artificial reinforcement learning

Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory.

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant’s sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.

Alterations in neurotransmitter levels and transcription factor expression following intranasal buprenorphine administration

Buprenorphine is an opioid drug used in the management of pain and the treatment opioid addiction. Like other opioids, it is believed that it achieves these effects by altering functional neurotransmitter pathways and the expression of important transcription factors; cyclic AMP response element-binding protein (CREB) and brain-derived neurotrophic factor (BDNF) in the brain. However, there is a lack of scientific evidence to support these theories. This study investigated the pharmacodynamic effects of BUP administration by assessing neurotransmitter and molecular changes in the healthy rodent brain. Sprague-Dawley rats (150-200 g) were intranasally administered buprenorphine (0.3 mg/mL) and sacrificed at different time points: 0.25, 0.5, 1, 2, 4, 6, 8 and 24 h post drug administration. LC-MS was used to quantify BUP and neurotransmitters (GABA, GLUT, DA, NE and 5-HT) in the brain, while CREB and BDNF gene expression was determined using qPCR. Results showed that BUP reached a Cmax of 1.21 ± 0.0523 ng/mL after 2 h, with all neurotransmitters showing an increase in their concentration over time, with GABA, GLUT and NE reaching their maximum concentration after 8 h. DA and 5-HT reached their maximum concentrations at 1 h and 24 h, respectively post drug administration. Treatment with BUP resulted in significant upregulation in BDNF expression throughout the treatment period while CREB showed patterns of significant upregulation at 2 and 8 h, and downregulation at 1 and 6 h. This study contributes to the understanding of the pharmacodynamic effects of BUP in opioid addiction by proving that the drug significantly influences NT pathways that are implicated in opioid addiction.

Tonic dopamine, uncertainty and basal ganglia action selection

To make optimal decisions in uncertain circumstances flexible adaption of behaviour is required; exploring alternatives when the best choice is unknown, exploiting what is known when that is best. Using a computational model of the basal ganglia, we propose that switches between exploratory and exploitative decisions are mediated by the interaction between tonic dopamine and cortical input to the basal ganglia. We show that a biologically detailed action selection circuit model, endowed with dopamine dependant striatal plasticity, can optimally solve the explore-exploit problem, estimating the true underlying state of a noisy Gaussian diffusion process. Critical to the model's performance was a fluctuating level of tonic dopamine which increased under conditions of uncertainty. With an optimal range of tonic dopamine, explore-exploit decisions were mediated by the effects of tonic dopamine on the precision of the model action selection mechanism. Under conditions of uncertain reward pay-out, the model's reduced selectivity allowed disinhibition of multiple alternative actions to be explored at random. Conversely, when uncertainly about reward pay-out was low, enhanced selectivity of the action selection circuit facilitated exploitation of the high value choice. Model performance was at the level of a Kalman filter which provides an optimal solution for the task. These simulations support the idea that this subcortical neural circuit may have evolved to facilitate decision making in non-stationary reward environments. The model generates several experimental predictions with relevance to abnormal decision making in neuropsychiatric and neurological disease.

Retention of Value Representations Across Time in People With Schizophrenia and Healthy Control Subjects

BACKGROUND

The current study aimed to further etiological understanding of the psychological mechanisms underlying negative symptoms in people with schizophrenia. Specifically, we tested whether negative symptom severity is associated with reduced retention of reward-related information over time and thus a degraded ability to utilize such information to guide future action selection.

METHODS

Forty-four patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy control volunteers performed a probabilistic reinforcement-learning task involving stimulus pairs in which choices resulted in reward or in loss avoidance. Following training, participants indicated their valuation of learned stimuli in a test/transfer phase. The test/transfer phase was administered immediately following training and 1 week later. Percent retention was defined as accuracy at week-long delay divided by accuracy at immediate delay.

RESULTS

Healthy control subjects and people with schizophrenia showed similarly robust retention of reinforcement learning over a 1-week delay interval. However, in the schizophrenia group, negative symptom severity was associated with reduced retention of information regarding the value of actions across a week-long interval. This pattern was particularly notable for stimuli associated with reward compared with loss avoidance.

CONCLUSIONS

Our results show that although individuals with schizophrenia may initially learn about rewarding aspects of their environment, such learning decays at a more rapid rate in patients with severe negative symptoms. Thus, previously learned reward-related information may be more difficult to access to guide future decision making and to motivate action selection.

Acute stress impairs reward learning in men

Acute stress is ubiquitous in everyday life, but the extent to which acute stress affects how people learn from the outcomes of their choices is still poorly understood. Here, we investigate how acute stress impacts reward and punishment learning in men using a reinforcement-learning task. Sixty-two male participants performed the task whilst under stress and control conditions. We observed that acute stress impaired participants' choice performance towards monetary gains, but not losses. To unravel the mechanism(s) underlying such impairment, we fitted a reinforcement-learning model to participants' trial-by-trial choices. Computational modeling indicated that under acute stress participants learned more slowly from positive prediction errors - when the outcomes were better than expected - consistent with stress-induced dopamine disruptions. Such mechanistic understanding of how acute stress impairs reward learning is particularly important given the pervasiveness of stress in our daily life and the impact that stress can have on our wellbeing and mental health.

Tremor in Parkinson's disease inverts the effect of dopamine on reinforcement

This scientific commentary refers to ‘Effects of dopamine on reinforcement learning in Parkinson’s disease depend on motor phenotype’ by van Nuland et al. (doi:10.1093/brain/awaa335).

Effects of dopamine on reinforcement learning in Parkinson's disease depend on motor phenotype

Parkinson's disease is clinically defined by bradykinesia, along with rigidity and tremor. However, the severity of these motor signs is greatly variable between individuals, particularly the presence or absence of tremor. This variability in tremor relates to variation in cognitive/motivational impairment, as well as the spatial distribution of neurodegeneration in the midbrain and dopamine depletion in the striatum. Here we ask whether interindividual heterogeneity in tremor symptoms could account for the puzzlingly large variability in the effects of dopaminergic medication on reinforcement learning, a fundamental cognitive function known to rely on dopamine. Given that tremor-dominant and non-tremor Parkinson's disease patients have different dopaminergic phenotypes, we hypothesized that effects of dopaminergic medication on reinforcement learning differ between tremor-dominant and non-tremor patients. Forty-three tremor-dominant and 20 non-tremor patients with Parkinson's disease were recruited to be tested both OFF and ON dopaminergic medication (200/50 mg levodopa-benserazide), while 22 age-matched control subjects were recruited to be tested twice OFF medication. Participants performed a reinforcement learning task designed to dissociate effects on learning rate from effects on motivational choice (i.e. the tendency to 'Go/NoGo' in the face of reward/threat of punishment). In non-tremor patients, dopaminergic medication improved reward-based choice, replicating previous studies. In contrast, in tremor-dominant patients, dopaminergic medication improved learning from punishment. Formal modelling showed divergent computational effects of dopaminergic medication as a function of Parkinson's disease motor phenotype, with a modulation of motivational choice bias and learning rate in non-tremor and tremor patients, respectively. This finding establishes a novel cognitive/motivational difference between tremor and non-tremor Parkinson's disease patients, and highlights the importance of considering motor phenotype in future work.

Dopamine is associated with prioritization of reward-associated memories in Parkinson's disease

Patients with Parkinson's disease have reduced reward sensitivity related to dopaminergic neuron loss, which is associated with impairments in reinforcement learning. Increasingly, however, dopamine-dependent reward signals are recognized to play an important role beyond reinforcement learning. In particular, it has been shown that reward signals mediated by dopamine help guide the prioritization of events for long-term memory consolidation. Meanwhile, studies of memory in patients with Parkinson's disease have focused on overall memory capacity rather than what is versus what isn't remembered, leaving open questions about the effect of dopamine replacement on the prioritization of memories by reward and the time-dependence of this effect. The current study sought to fill this gap by testing the effect of reward and dopamine on memory in patients with Parkinson's disease. We tested the effect of dopamine modulation and reward on two forms of long-term memory: episodic memory for neutral objects and memory for stimulus-value associations. We measured both forms of memory in a single task, adapting a standard task of reinforcement learning with incidental episodic encoding events of trial-unique objects. Objects were presented on each trial at the time of feedback, which was either rewarding or not. Memory for the trial-unique images and for the stimulus-value associations, and the influence of reward on both, was tested immediately after learning and 2 days later. We measured performance in Parkinson's disease patients tested either ON or OFF their dopaminergic medications and in healthy older control subjects. We found that dopamine was associated with a selective enhancement of memory for reward-associated images, but that it did not influence overall memory capacity. Contrary to predictions, this effect did not differ between the immediate and delayed memory tests. We also found that while dopamine had an effect on reward-modulated episodic memory, there was no effect of dopamine on memory for stimulus-value associations. Our results suggest that impaired prioritization of cognitive resource allocation may contribute to the early cognitive deficits of Parkinson's disease.

Comparison of conventional and rapid-acting antidepressants in a rodent probabilistic reversal learning task

Deficits in reward processing are a central feature of major depressive disorder with patients exhibiting decreased reward learning and altered feedback sensitivity in probabilistic reversal learning tasks. Methods to quantify probabilistic learning in both rodents and humans have been developed, providing translational paradigms for depression research. We have utilised a probabilistic reversal learning task to investigate potential differences between conventional and rapid-acting antidepressants on reward learning and feedback sensitivity. We trained 12 rats in a touchscreen probabilistic reversal learning task before investigating the effect of acute administration of citalopram, venlafaxine, reboxetine, ketamine or scopolamine. Data were also analysed using a Q-learning reinforcement learning model to understand the effects of antidepressant treatment on underlying reward processing parameters. Citalopram administration decreased trials taken to learn the first rule and increased win-stay probability. Reboxetine decreased win-stay behaviour while also decreasing the number of rule changes animals performed in a session. Venlafaxine had no effect. Ketamine and scopolamine both decreased win-stay probability, number of rule changes performed and motivation in the task. Insights from the reinforcement learning model suggested that reboxetine led animals to choose a less optimal strategy, while ketamine decreased the model-free learning rate. These results suggest that reward learning and feedback sensitivity are not differentially modulated by conventional and rapid-acting antidepressant treatment in the probabilistic reversal learning task.

Spontaneous eye blink rate predicts individual differences in exploration and exploitation during reinforcement learning

Spontaneous eye blink rate (sEBR) has been linked to striatal dopamine function and to how individuals make value-based choices after a period of reinforcement learning (RL). While sEBR is thought to reflect how individuals learn from the negative outcomes of their choices, this idea has not been tested explicitly. This study assessed how individual differences in sEBR relate to learning by focusing on the cognitive processes that drive RL. Using Bayesian latent mixture modelling to quantify the mapping between RL behaviour and its underlying cognitive processes, we were able to differentiate low and high sEBR individuals at the level of these cognitive processes. Further inspection of these cognitive processes indicated that sEBR uniquely indexed explore-exploit tendencies during RL: lower sEBR predicted exploitative choices for high valued options, whereas higher sEBR predicted exploration of lower value options. This relationship was additionally supported by a network analysis where, notably, no link was observed between sEBR and how individuals learned from negative outcomes. Our findings challenge the notion that sEBR predicts learning from negative outcomes during RL, and suggest that sEBR predicts individual explore-exploit tendencies. These then influence value sensitivity during choices to support successful performance when facing uncertain reward.

Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson's disease

Reduced levels of dopamine in Parkinson's disease contribute to changes in learning, resulting from the loss of midbrain neurons that transmit a dopaminergic teaching signal to the striatum. Dopamine medication used by patients with Parkinson's disease has previously been linked to behavioural changes during learning as well as to adjustments in value-based decision-making after learning. To date, however, little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. Twenty-four Parkinson's disease patients ON and OFF dopaminergic medication and 24 healthy controls subjects underwent functional MRI while performing a probabilistic reinforcement learning experiment. During learning, dopaminergic medication reduced an overemphasis on negative outcomes. Medication reduced negative (but not positive) outcome learning rates, while concurrent striatal blood oxygen level-dependent responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by systematic striatal blood oxygen level-dependent response alterations. These findings elucidate the role of dopamine-driven learning differences in Parkinson's disease, and show how these changes during learning impact subsequent value-based decision-making.

Levodopa does not affect expression of reinforcement learning in older adults

Dopamine has been implicated in learning from rewards and punishment, and in the expression of this learning. However, many studies do not fully separate retrieval and decision mechanisms from learning and consolidation. Here, we investigated the effects of levodopa (dopamine precursor) on choice performance (isolated from learning or consolidation). We gave 31 healthy older adults 150 mg of levodopa or placebo (double-blinded, randomised) 1 hour before testing them on stimuli they had learned the value of the previous day. We found that levodopa did not affect the overall accuracy of choices, nor the relative expression of positively or negatively reinforced values. This contradicts several studies and suggests that overall dopamine levels may not play a role in the choice performance for values learned through reinforcement learning in older adults.

Optogenetic Stimulation of the M2 Cortex Reverts Motor Dysfunction in a Mouse Model of Parkinson's Disease

Neuromodulation of deep brain structures (deep brain stimulation) is the current surgical procedure for treatment of Parkinson's disease (PD). Less studied is the stimulation of cortical motor areas to treat PD symptoms, although also known to alleviate motor disturbances in PD. We were able to show that optogenetic activation of secondary (M2) motor cortex improves motor functions in dopamine-depleted male mice. The stimulated M2 cortex harbors glutamatergic pyramidal neurons that project to subcortical structures, critically involved in motor control, and makes synaptic contacts with dopaminergic neurons. Strikingly, optogenetic activation of M2 neurons or axons into the dorsomedial striatum increases striatal levels of dopamine and evokes locomotor activity. We found that dopamine neurotransmission sensitizes the locomotor behavior elicited by activation of M2 neurons. Furthermore, combination of intranigral infusion of glutamatergic antagonists and circuit specific optogenetic stimulation revealed that behavioral response depended on the activity of M2 neurons projecting to SNc. Interestingly, repeated M2 stimulation combined with l-DOPA treatment produced an unanticipated improvement in working memory performance, which was absent in control mice under l-DOPA treatment only. Therefore, the M2-basal ganglia circuit is critical for the assembly of the motor and cognitive function, and this study demonstrates a therapeutic mechanism for cortical stimulation in PD that involves recruitment of long-range glutamatergic projection neurons.SIGNIFICANCE STATEMENT Some patients with Parkinson's disease are offered treatment through surgery, which consists of delivering electrical current to regions deep within the brain. This study shows that stimulation of an area located on the brain surface, known as the secondary motor cortex, can also reverse movement disorders in mice. Authors have used a brain stimulation technique called optogenetics, which allowed targeting a specific type of surface neuron that communicates with the deep part of the brain involved in movement control. The study also shows that a combination of this stimulation with drug treatment might be useful to treat memory impairment, a kind of cognitive problem in Parkinson's disease.

Meditation experience predicts negative reinforcement learning and is associated with attenuated FRN amplitude

Focused attention meditation (FAM) practices are cognitive control exercises where meditators learn to maintain focus and attention in the face of distracting stimuli. Previous studies have shown that FAM is both activating and causing plastic changes to the mesolimbic dopamine system and some of its target structures, particularly the anterior cingulate cortex (ACC) and striatum. Feedback-based learning also depends on these systems and is known to be modulated by tonic dopamine levels. Capitalizing on previous findings that FAM practices seem to cause dopamine release, the present study shows that FAM experience predicts learning from negative feedback on a probabilistic selection task. Furthermore, meditators exhibited attenuated feedback-related negativity (FRN) as compared with nonmeditators and this effect scales with meditation experience. Given that reinforcement learning and FRN are modulated by dopamine levels, a possible explanation for our findings is that FAM practice causes persistent increases in tonic dopamine levels which scale with amount of practice, thus altering feedback processing.

Translating Insights From Optogenetics To Therapies For Parkinson's Disease

Movement disorders including Parkinson’s disease and dystonia are caused by neurological dysfunction, typically resulting from the loss of a neuronal input within a circuit. Neuromodulation, specifically deep brain stimulation (DBS), has proven to be a critical development in the treatment of movement disorders. Continuing efforts aim to improve DBS techniques, both in how they exert their effects and in the efficacy of the mechanism involved in eliciting those effects. While optogenetic stimulation is currently infeasible in human patients, opto-DBS research provides an indispensible avenue to understand the mechanisms of DBS therapeutic and adverse effects. We review the benefits of cell-type specific manipulations in understanding the root cause of movement disorders and how DBS might optimally combat those causes. We also explore new circuit-inspired applications of DBS suggested by thorough, high-throughput optogenetic techniques. Maximizing the efficacy and outcome of DBS requires a multi-tiered approach; research employing optogenetics provides the specificity and feasibility to uncover the mechanisms that will help realize these gains in patient care.

Paradoxical Decision-Making: A Framework for Understanding Cognition in Parkinson's Disease

People with Parkinson's disease (PD) show impaired decision-making when sensory and memory information must be combined. This recently identified impairment results from an inability to accumulate the proper amount of information needed to make a decision and appears to be independent of dopamine tone and reinforcement learning mechanisms. Although considerable work focuses on PD and decisions involving risk and reward, in this Opinion article we propose that the emerging findings in perceptual decision-making highlight the multisystem nature of PD, and that unraveling the neuronal circuits underlying perceptual decision-making impairment may help in understanding other cognitive impairments in people with PD. We also discuss how a decision-making framework may be extended to gain insights into mechanisms of motor impairments in PD.

Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain

Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback. Learning paradigms are increasingly being extended to study mood and psychiatric disorders as well as addiction. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are usually conducted in a single rapidly paced session, whereas learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. In our experiments, we examined reward learning in short condensed sessions distributed across weeks versus learning completed in a single "massed" session in male and female participants. As expected, we found that after equal amounts of training, accuracy was matched between the spaced and massed conditions. However, in a 3-week follow-up, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. Supporting a role for short-term memory in massed learning, we found a significant positive correlation between initial learning and working memory capacity. Neurally, we found that patterns of activity in the medial temporal lobe and prefrontal cortex showed stronger discrimination of spaced- versus massed-trained reward values. Further, patterns in the striatum discriminated between spaced- and massed-trained stimuli overall. Our results indicate that single-session learning tasks engage partially distinct learning mechanisms from distributed training. Our studies begin to address a large gap in our knowledge of human learning from reinforcement, with potential implications for our understanding of mood disorders and addiction.SIGNIFICANCE STATEMENT Humans and animals learn to associate predictive value with stimuli and actions, and these values then guide future behavior. Such reinforcement-based learning often happens over long time periods, in contrast to most studies of reward-based learning in humans. In experiments that tested the effect of spacing on learning, we found that associations learned in a single massed session were correlated with short-term memory and significantly decayed over time, whereas associations learned in short massed sessions over weeks were well maintained. Additionally, patterns of activity in the medial temporal lobe and prefrontal cortex discriminated the values of stimuli learned over weeks but not minutes. These results highlight the importance of studying learning over time, with potential applications to drug addiction and psychiatry.

Selective Effects of the Loss of NMDA or mGluR5 Receptors in the Reward System on Adaptive Decision-Making

Selecting the most advantageous actions in a changing environment is a central feature of adaptive behavior. The midbrain dopamine (DA) neurons along with the major targets of their projections, including dopaminoceptive neurons in the frontal cortex and basal ganglia, play a key role in this process. Here, we investigate the consequences of a selective genetic disruption of NMDA receptor and metabotropic glutamate receptor 5 (mGluR5) in the DA system on adaptive choice behavior in mice. We tested the effects of the mutation on performance in the probabilistic reinforcement learning and probability-discounting tasks. In case of the probabilistic choice, both the loss of NMDA receptors in dopaminergic neurons or the loss mGluR5 receptors in D1 receptor-expressing dopaminoceptive neurons reduced the probability of selecting the more rewarded alternative and lowered the likelihood of returning to the previously rewarded alternative (win-stay). When observed behavior was fitted to reinforcement learning models, we found that these two mutations were associated with a reduced effect of the expected outcome on choice (i.e., more random choices). None of the mutations affected probability discounting, which indicates that all animals had a normal ability to assess probability. However, in both behavioral tasks animals with targeted loss of NMDA receptors in dopaminergic neurons or mGluR5 receptors in D1 neurons were significantly slower to perform choices. In conclusion, these results show that glutamate receptor-dependent signaling in the DA system is essential for the speed and accuracy of choices, but at the same time probably is not critical for correct estimation of probable outcomes.

Protective effect of carnosine on white matter damage in corpus striatum induced by chronic cerebral hypoperfusion

Subcortical ischemic vascular dementia caused by chronic cerebral hypoperfusion due to small-artery disease is a common subtype of vascular dementia, which is recognized as the second most prevalent type of dementia. The aim of this study was to determine the effect of carnosine on white matter damage in corpus striatum. Adult male mice (C57BL/6 strain) were subjected to right unilateral common carotid arteries occlusion (rUCCAO), and treated with carnosine or saline. Klüver-Barrera staining, immunohistochemical analyses, Western blots and neurochemical analysis were performed after rUCCAO. The white matter in corpus striatum was damaged at day 37 after rUCCAO, which was largely rescued by carnosine (200, 500 mg/kg). Carnosine (200, 500 mg/kg) significantly recovered the expression of myelin basic protein, suppressed the activation of microglia and reversed the decrease of 5-hydroxytryptamine and dopamine levels in corpus striatum. Moreover, carnosine (200, 500 mg/kg) significantly inhibited the apoptosis in corpus striatum. These data suggest that carnosine has the neuroprotective effect in corpus striatum on rUCCAO in mice, may be due to its protection of neurotransmitters and inhibition of apoptosis.

Perceptual decisions based on previously learned information are independent of dopaminergic tone

Both cognitive and motor symptoms in people with Parkinson's disease (PD) arise from either too little or too much dopamine (DA). Akinesia stems from DA neuronal cell loss, and dyskinesia often stems from an overdose of DA medication. Cognitive behaviors typically associated with frontal cortical function, such as working memory and task switching, are also affected by too little or too much DA in PD. Whether motor and cognitive circuits overlap in PD is unknown. In this article, we show that whereas motor performance improves in people with PD when on dopaminergic medication compared with off medication, perceptual decision-making based on previously learned information (priors) remains impaired whether on or off medications. To rule out effects of long-term DA treatment and dopaminergic neuronal loss such as occur in PD, we also tested a group of people with dopa-unresponsive focal dystonia, a disease that involves the basal ganglia, like PD, but has motor symptoms that are insensitive to dopamine treatment and is not thought to involve frontal cortical DA circuits, unlike PD. We found that people with focal dystonia showed intact perceptual decision-making performance but impaired use of priors in perceptual decision-making, similar to people with PD. Together, the results show a dissociation between motor and cognitive performance in people with PD and reveal a novel cognitive impairment, independent of sensory and motor impairment, in people with focal dystonia. The combined results from people with PD and people with focal dystonia provide mechanistic insights into the role of basal ganglia non-dopaminergic circuits in perceptual decision-making based on priors.