Effects of serotonin and dopamine depletion on neural prediction computations during social learning

The dual roles of serotonin in antitumor immunity

Research has shown that a significant portion of cancer patients experience depressive symptoms, often accompanied by neuroendocrine hormone imbalances. Depression is frequently associated with decreased levels of serotonin with the alternate name 5-hydroxytryptamine (5-HT), leading to the common use of selective serotonin reuptake inhibitors (SSRIs) as antidepressants. However, the role of serotonin in tumor regulation remains unclear, with its expression levels displaying varied effects across different types of tumors. Tumor initiation and progression are closely intertwined with the immune function of the human body. Neuroimmunity, as an interdisciplinary subject, has played a unique role in the study of the relationship between psychosocial factors and tumors and their mechanisms in recent years. This article offers a comprehensive review of serotonin's regulatory roles in tumor onset and progression, as well as its impacts on immune cells in the tumor microenvironment. The aim is to stimulate further interdisciplinary research and discover novel targets for tumor treatment.

Hydroalcoholic Extract of Centella asiatica and Madecassic Acid Reverse Depressive-Like Behaviors, Inflammation and Oxidative Stress in Adult Rats Submitted to Stress in Early Life

Major depressive disorder (MDD) is a severe disorder that causes enormous loss of quality of life, and among the factors underlying MDD is stress in maternal deprivation (MD). In addition, classic pharmacotherapy has presented severe adverse effects. Centella asiatica (C. asiatica) demonstrates a potential neuroprotective effect but has not yet been evaluated in MD models. This study aimed to evaluate the effect of C. asiatica extract and the active compound madecassic acid on possible depressive-like behavior, inflammation, and oxidative stress in the hippocampus and serum of young rats submitted to MD in the first days of life. Rats (after the first day of birth) were separated from the mother for 3 h a day for 10 days. When adults, these animals were divided into groups and submitted to treatment for 14 days. After subjecting the animals to protocols of locomotor activity in the open field and behavioral despair in the forced swimming test, researchers then euthanized the animals. The hippocampus and serum were collected and analyzed for the inflammatory cytokines and oxidative markers. The C. asiatica extract and active compound reversed or reduced depressive-like behaviors, inflammation in the hippocampus, and oxidative stress in serum and hippocampus. These results suggest that C. asiatica and madecassic acid have potential antidepressant action, at least partially, through anti-inflammatory and antioxidant profiles.

65 years of research on dopamine's role in classical fear conditioning and extinction: A systematic review

Dopamine, a catecholamine neurotransmitter, has historically been associated with the encoding of reward, whereas its role in aversion has received less attention. Here, we systematically gathered the vast evidence of the role of dopamine in the simplest forms of aversive learning: classical fear conditioning and extinction. In the past, crude methods were used to augment or inhibit dopamine to study its relationship with fear conditioning and extinction. More advanced techniques such as conditional genetic, chemogenic and optogenetic approaches now provide causal evidence for dopamine's role in these learning processes. Dopamine neurons encode conditioned stimuli during fear conditioning and extinction and convey the signal via activation of D1-4 receptor sites particularly in the amygdala, prefrontal cortex and striatum. The coordinated activation of dopamine receptors allows for the continuous formation, consolidation, retrieval and updating of fear and extinction memory in a dynamic and reciprocal manner. Based on the reviewed literature, we conclude that dopamine is crucial for the encoding of classical fear conditioning and extinction and contributes in a way that is comparable to its role in encoding reward.

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

Although evidence suggests that antidepressants are effective at treating depression, the mechanisms behind antidepressant action remain unclear, especially at the cognitive/computational level. In recent years, reinforcement learning (RL) models have increasingly been used to characterise the roles of neurotransmitters and to probe the computations that might be altered in psychiatric disorders like depression. Hence, RL models might present an opportunity for us to better understand the computational mechanisms underlying antidepressant effects. Moreover, RL models may also help us shed light on how these computations may be implemented in the brain (e.g., in midbrain, striatal, and prefrontal regions) and how these neural mechanisms may be altered in depression and remediated by antidepressant treatments. In this paper, we evaluate the ability of RL models to help us understand the processes underlying antidepressant action. To do this, we review the preclinical literature on the roles of dopamine and serotonin in RL, draw links between these findings and clinical work investigating computations altered in depression, and appraise the evidence linking modification of RL processes to antidepressant function. Overall, while there is no shortage of promising ideas about the computational mechanisms underlying antidepressant effects, there is insufficient evidence directly implicating these mechanisms in the response of depressed patients to antidepressant treatment. Consequently, future studies should investigate these mechanisms in samples of depressed patients and assess whether modifications in RL processes mediate the clinical effect of antidepressant treatments.

Dopaminergic challenge dissociates learning from primary versus secondary sources of information

Some theories of human cultural evolution posit that humans have social-specific learning mechanisms that are adaptive specialisations moulded by natural selection to cope with the pressures of group living. However, the existence of neurochemical pathways that are specialised for learning from social information and individual experience is widely debated. Cognitive neuroscientific studies present mixed evidence for social-specific learning mechanisms: some studies find dissociable neural correlates for social and individual learning, whereas others find the same brain areas and, dopamine-mediated, computations involved in both. Here, we demonstrate that, like individual learning, social learning is modulated by the dopamine D2 receptor antagonist haloperidol when social information is the primary learning source, but not when it comprises a secondary, additional element. Two groups (total N = 43) completed a decision-making task which required primary learning, from own experience, and secondary learning from an additional source. For one group, the primary source was social, and secondary was individual; for the other group this was reversed. Haloperidol affected primary learning irrespective of social/individual nature, with no effect on learning from the secondary source. Thus, we illustrate that dopaminergic mechanisms underpinning learning can be dissociated along a primary-secondary but not a social-individual axis. These results resolve conflict in the literature and support an expanding field showing that, rather than being specialised for particular inputs, neurochemical pathways in the human brain can process both social and non-social cues and arbitrate between the two depending upon which cue is primarily relevant for the task at hand.

A neurobiological perspective on social influence: Serotonin and social adaptation

Humans are inherently social beings. Being suggestible to each other's expectations enables pro-social skills that are crucial for social learning and adaptation. Despite its high relevance for psychiatry, the neurobiological mechanisms underlying social adaptation are still not well understood. This review therefore provides a conceptual framework covering various distinct mechanisms underlying social adaptation and explores the neuropharmacology - in particular the role of the serotonin (5-HT) system - modulating these mechanisms. This article therefore reviews empirical results on social influence processing and reconciles them with recent findings from psychedelic research on social processing to elucidate neurobiological and neuropharmacological underpinnings of social adaptation. Various computational, neurobiological, and neurochemical processes are involved in distinct mechanisms underlying social adaptation such as the multisensory process of social information integration that is crucial for the forming of self-representation and representations of social norms. This is again associated with self- and other-perception during social interactions as well as value-based decision making that guides our behaviour in daily interactions. We highlight the critical role of 5-HT in these processes and suggest that 5-HT can facilitate social learning and may represent an important target for treating psychiatric disorders characterized by impairments in social functioning. This framework also has important implications for psychedelic-assisted therapy as well as for the development of novel treatment approaches and future research directions.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.