What Can Reinforcement Learning Models of Dopamine and Serotonin Tell Us about the Action of Antidepressants?

Direct serotonin release in humans shapes aversive learning and inhibition

The role of serotonin in human behaviour is informed by approaches which allow in vivo modification of synaptic serotonin. However, characterising the effects of increased serotonin signalling in human models of behaviour is challenging given the limitations of available experimental probes, notably selective serotonin reuptake inhibitors. Here we use a now-accessible approach to directly increase synaptic serotonin in humans (a selective serotonin releasing agent) and examine its influence on domains of behaviour historically considered core functions of serotonin. Computational techniques, including reinforcement learning and drift diffusion modelling, explain participant behaviour at baseline and after week-long intervention. Reinforcement learning models reveal that increasing synaptic serotonin reduces sensitivity for outcomes in aversive contexts. Furthermore, increasing synaptic serotonin enhances behavioural inhibition, and shifts bias towards impulse control during exposure to aversive emotional probes. These effects are seen in the context of overall improvements in memory for neutral verbal information. Our findings highlight the direct effects of increasing synaptic serotonin on human behaviour, underlining its role in guiding decision-making within aversive and more neutral contexts, and offering implications for longstanding theories of central serotonin function.

Unveiling serotonergic dysfunction of obsessive-compulsive disorder on prefrontal network dynamics: a computational perspective

Serotonin (5-HT) regulates working memory within the prefrontal cortex network, which is crucial for understanding obsessive-compulsive disorder. However, the mechanisms how network dynamics and serotonin interact in obsessive-compulsive disorder remain elusive. Here, we incorporate 5-HT receptors (5-HT1A, 5-HT2A) and dopamine receptors into a multistable prefrontal cortex network model, replicating the experimentally observed inverted U-curve phenomenon. We show how the two 5-HT receptors antagonize neuronal activity and modulate network multistability. Reduced binding of 5-HT1A receptors increases global firing, while reduced binding of 5-HT2A receptors deepens attractors. The obtained results suggest reward-dependent synaptic plasticity mechanisms may attenuate 5-HT related network impairments. Integrating serotonin-mediated dopamine release into circuit, we observe that decreased serotonin concentration triggers the network into a deep attractor state, expanding the domain of attraction of stable nodes with high firing rate, potentially causing aberrant reverse learning. This suggests a hypothesis wherein elevated dopamine concentrations in obsessive-compulsive disorder might result from primary deficits in serotonin levels. Findings of this work underscore the pivotal role of serotonergic dysregulation in modulating synaptic plasticity through dopamine pathways, potentially contributing to learned obsessions. Interestingly, serotonin reuptake inhibitors and antidopaminergic potentiators can counteract the over-stable state of high-firing stable points, providing new insights into obsessive-compulsive disorder treatment.

Leveraging Decision Science to Characterize Depression

This brief review examines the potential to use decision science to objectively characterize depression. We provide a brief overview of the existing literature examining different domains of decision-making in depression. Because this overview highlights the specific role of reinforcement learning as an important decision process affected in the disorder, we then introduce reinforcement learning modeling and explain how this approach has identified specific reinforcement learning deficits in depression. We conclude with ideas for future research at the intersection of decision science and depression, emphasizing the potential for decision science to help uncover underlying mechanisms and targets for the treatment of depression.

New living evidence resource of human and non-human studies for early intervention and research prioritisation in anxiety, depression and psychosis

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will work for whom and in what contexts. To intervene early in the process and deliver optimal care to patients, we need to understand the underlying mechanisms of mental health conditions, develop safe and effective interventions that target these mechanisms, and improve our capabilities in timely diagnosis and reliable prediction of symptom trajectories. Better synthesis of existing evidence is one way to reduce waste and improve efficiency in research towards these ends. Living systematic reviews produce rigorous, up-to-date and informative evidence summaries that are particularly important where research is emerging rapidly, current evidence is uncertain and new findings might change policy or practice. Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) aims to tackle the challenges of mental health science research by cataloguing and evaluating the full spectrum of relevant scientific research including both human and preclinical studies. GALENOS will also allow the mental health community-including patients, carers, clinicians, researchers and funders-to better identify the research questions that most urgently need to be answered. By creating open-access datasets and outputs in a state-of-the-art online resource, GALENOS will help identify promising signals early in the research process. This will accelerate translation from discovery science into effective new interventions for anxiety, depression and psychosis, ready to be translated in clinical practice across the world.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.