Serotonin, inhibition, and negative mood

Erasing "bad memories": reversing aberrant synaptic plasticity as therapy for neurological and psychiatric disorders

Dopamine modulates corticostriatal plasticity in both the direct and indirect pathways of the cortico-striato-thalamo-cortical (CSTC) loops. These gradual changes in corticostriatal synaptic strengths produce long-lasting changes in behavioral responses. Under normal conditions, these mechanisms enable the selection of the most appropriate responses while inhibiting others. However, under dysregulated dopamine conditions, including a lack of dopamine release or dopamine signaling, these mechanisms could lead to the selection of maladaptive responses and/or the inhibition of appropriate responses in an experience-dependent and task-specific manner. In this review, we propose that preventing or reversing such maladaptive synaptic strengths and erasing such aberrant "memories" could be a disease-modifying therapeutic strategy for many neurological and psychiatric disorders. We review evidence from Parkinson's disease, drug-induced parkinsonism, L-DOPA-induced dyskinesia, obsessive-compulsive disorder, substance use disorders, and depression as well as research findings on animal disease models. Altogether, these studies allude to an emerging theme in translational neuroscience and promising new directions for therapy development. Specifically, we propose that combining pharmacotherapy with behavioral therapy or with deep brain stimulation (DBS) could potentially cause desired changes in specific neural circuits. If successful, one important advantage of correcting aberrant synaptic plasticity is long-lasting therapeutic effects even after treatment has ended. We will also discuss the potential molecular targets for these therapeutic approaches, including the cAMP pathway, proteins involved in synaptic plasticity as well as pathways involved in new protein synthesis. We place special emphasis on RNA binding proteins and epitranscriptomic mechanisms, as they represent a new frontier with the distinct advantage of rapidly and simultaneously altering the synthesis of many proteins locally.

Assessing acute effects of methylphenidate and modafinil on inhibitory capacity, time estimation, attentional lapses, and compulsive-like behavior in rats

Medications known as 'cognitive enhancers' are increasingly being consumed off-label by healthy people, raising concerns about their safety. The aim of our study was to profile behavioral performance upon oral administration of methylphenidate (2.5 mg/kg) and modafinil (64 mg/kg) - two popular cognitive enhancers - and upon their discontinuation. We modeled cognitively demanding challenges in neurotypical individuals using a behavioral task where Wistar - Lewis rats had to withhold responses for a specified time to obtain food rewards. This task allowed us to extract several measures of behavioral performance associated with clinically meaningful indices, such as compulsive-like responding, incapacity to wait (impulsivity), time estimation (precision and accuracy), and attentional lapses. Our study design involved examining these behavioral indices in subjects administered either methylphenidate, modafinil, or vehicle. We found that subjects administered modafinil obtained fewer rewards and were less efficient in reward pursuing than the vehicle group; this result was likely due to a drug-induced inability to wait. Upon modafinil discontinuation, subjects earned more rewards but did not entirely catch up with the vehicle group. As for methylphenidate, neither favorable nor unfavorable effects were found in our main analyses. However, an exploratory analysis of changes in behavioral performance within sessions suggested that methylphenidate fostered favorable, yet short-lived, effects. We discuss our results in terms of the risks and cost-benefits of doses above or below the effective dose of cognitive enhancement drugs.

Metabolomic profiles in serum uncover novel biomarkers in children with Williams-Beuren syndrome

Williams-Beuren syndrome (WBS, OMIM-no.194050) is a rare congenital genetic disorder primarily marked by developmental delays and cardiovascular anomalies, with potential involvement of metabolic dysregulation. Despite this, the metabolic features of WBS have not been extensively studied. Thus, our objective was to examine the serum metabolome profile in children with WBS, elucidating metabolic changes and associated pathways in the disorder. We recruited 25 children with WBS (mean age 5.0 ± 2.6 years, 40% female) from the Children's Hospital affiliated to Zhejiang University between 2020 and 2023. An age and sex matched healthy control group (N = 25) were recruited from the Health Management Center in the same hospital. Clinical information of WBS were extracted from the medical records. Blood samples were obtained for untargeted metabolomics analysis using UPLC-MS/MS. The metabolomic profiles of WBS patients were compared to those of healthy controls to identify metabolites with differential abundance. Enrichment analysis was conducted to identify potentially impacted KEGG pathways. Associations between metabolites and phenotypes were evaluated. Children with WBS exhibited a unique metabolic profile compared to healthy controls, as evidenced by the identification of 465 untargeted metabolites in serum. Of these metabolites, 169 showed differential abundance in WBS children. The top enriched KEGG pathways in WBS children included nicotine addiction, cholesterol metabolism, arginine biosynthesis, retrograde endocannabinoid signaling. Additionally, there were indications of potential metabolic alterations in the L-tryptophan pathway, with a shift from serotonin to L-kynurenine, as well as disruptions in bile acid metabolism. Metabolome data in children with WBS showed neurological and amino acid metabolism changes, indicating multisystem involvement and developmental delay. This data can help monitor and manage the disease, but further studies are needed to understand the underlying mechanisms and consequences.

Social isolation induces sexually aggressive behaviour in male Wistar rats

BACKGROUND

Sexual violence, a pervasive global issue, significantly impacts individuals and societies, necessitating a deeper understanding of its underlying biological mechanisms. This study aimed to elucidate the role of stress-induced dysregulation of the hypothalamus-pituitary-adrenocortical axis in sexual aggression in male Wistar rats. Employing a sexual aggression paradigm, we investigated the effects of social isolation on aggression, anxiety-like behaviour, and neurochemistry in virgin adult male Wistar rats.

RESULTS

The results showed that social isolation significantly escalated aggressive behaviours and induced anxiety-like responses in male rats. The sexual aggression test revealed that socially isolated males exhibited heightened aggression towards non-receptive females. Neurochemical analyses indicated significant alterations in key markers, such as corticotrophin-releasing hormone, oxytocin, and arginine vasopressin, correlating with the observed behavioural changes. Gene expression analyses revealed significant findings, particularly in the expression of the oxytocin receptor (OXTR) and vasopressin receptor 1 A (AVPR1A) genes. Social isolation and the duration of aggressive behaviour prior to the sexual aggression test significantly influenced OXTR expression in the hippocampus and AVPR1A expression in both the prefrontal cortex and hippocampus, highlighting the complex interplay between environmental stressors, neurochemical responses, and gene expression in the manifestation of sexual aggression behaviour.

CONCLUSIONS

This study underscores the critical impact of stress and social isolation on sexual aggression, providing valuable insights into possible neurobiological underpinnings of sexual violence. Understanding these mechanisms is crucial for developing effective interventions to mitigate the consequences of sexual aggression.

Improving brain health via the central executive network

Cognitive and physical stress have significant effects on brain health, particularly through their influence on the central executive network (CEN). The CEN, which includes regions such as the dorsolateral prefrontal cortex, anterior cingulate cortex and inferior parietal lobe, is central to managing the demands of cognitively challenging motor tasks. Acute stress can temporarily reduce connectivity within the CEN, leading to impaired cognitive function and emotional states. However a rebound in these states often follows, driven by motivational signals through the mesocortical and mesolimbic pathways, which help sustain inhibitory control and task execution. Chronic exposure to physical and cognitive challenges leads to long-term improvements in CEN functionality. These changes are supported by neurochemical, structural and systemic adaptations, including mechanisms of tissue crosstalk. Myokines, adipokines, anti-inflammatory cytokines and gut-derived metabolites contribute to a biochemical environment that enhances neuroplasticity, reduces neuroinflammation and supports neurotransmitters such as serotonin and dopamine. These processes strengthen CEN connectivity, improve self-regulation and enable individuals to adopt and sustain health-optimizing behaviours. Long-term physical activity not only enhances inhibitory control but also reduces the risk of age-related cognitive decline and neurodegenerative diseases. This review highlights the role of progressive physical stress through exercise as a practical approach to strengthening the CEN and promoting brain health, offering a strategy to improve cognitive resilience and emotional well-being across the lifespan.

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.

A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI

BACKGROUND

Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity.

METHODS

We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity.

RESULTS

MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems.

CONCLUSIONS

This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.

The selective serotonin reuptake inhibitor sertraline alters learning from aversive reinforcements in patients with depression: evidence from a randomized controlled trial

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological treatments for depression and anxiety. However, little is known about how pharmacological action is related to cognitive and affective processes. Here, we examine whether specific reinforcement learning processes mediate the treatment effects of SSRIs.

METHODS

The PANDA trial was a multicentre, double-blind, randomized clinical trial in UK primary care comparing the SSRI sertraline with placebo for depression and anxiety. Participants (N = 655) performed an affective Go/NoGo task three times during the trial and computational models were used to infer reinforcement learning processes.

RESULTS

There was poor task performance: only 54% of the task runs were informative, with more informative task runs in the placebo than in the active group. There was no evidence for the preregistered hypothesis that Pavlovian inhibition was affected by sertraline. Exploratory analyses revealed that in the sertraline group, early increases in Pavlovian inhibition were associated with improvements in depression after 12 weeks. Furthermore, sertraline increased how fast participants learned from losses and faster learning from losses was associated with more severe generalized anxiety symptoms.

CONCLUSIONS

The study findings indicate a relationship between aversive reinforcement learning mechanisms and aspects of depression, anxiety, and SSRI treatment, but these relationships did not align with the initial hypotheses. Poor task performance limits the interpretability and likely generalizability of the findings, and highlights the critical importance of developing acceptable and reliable tasks for use in clinical studies.

FUNDING

This article presents research supported by NIHR Program Grants for Applied Research (RP-PG-0610-10048), the NIHR BRC, and UCL, with additional support from IMPRS COMP2PSYCH (JM, QH) and a Wellcome Trust grant (QH).

From Tripping and Falling to Ruminating and Worrying: A Meta-Control Account of Repetitive Negative Thinking

Repetitive negative thinking (RNT) is a transdiagnostic construct that encompasses rumination and worry, yet what precisely is shared between rumination and worry is unclear. To clarify this, we develop a meta-control account of RNT. Meta-control refers to the reinforcement and control of mental behavior via similar computations as reinforce and control motor behavior. We propose rumination and worry are coarse terms for failure in meta-control, just as tripping and falling are coarse terms for failure in motor control. We delineate four meta-control stages and risk factors increasing the chance of failure at each, including open-ended thoughts (stage 1), individual differences influencing subgoal execution (stage 2) and switching (stage 3), and challenges inherent to learning adaptive mental behavior (stage 4). Distinguishing these stages therefore elucidates diverse processes that lead to the same behavior of excessive RNT. Our account also subsumes prominent clinical accounts of RNT into a computational cognitive neuroscience framework.

Techniques for Measurement of Serotonin: Implications in Neuropsychiatric Disorders and Advances in Absolute Value Recording Methods

Serotonin (5-HT) is a monoamine neurotransmitter in the peripheral, enteric, and central nervous systems (CNS). Within the CNS, serotonin is principally involved in mood regulation and reward-seeking behaviors. It is a critical regulator in CNS pathologies such as major depressive disorder, addiction, and schizophrenia. Consequently, in vivo serotonin measurements within the CNS have emerged as one of many promising approaches to investigating the pathogenesis, progression, and treatment of these and other neuropsychiatric conditions. These techniques vary in methods, ranging from analyte sampling with microdialysis to voltammetry. Provided this diversity in approach, inherent differences between techniques are inevitable. These include biosensor size, temporal/spatial resolution, and absolute value measurement capabilities, all of which must be considered to fit the prospective researcher's needs. In this review, we summarize currently available methods for the measurement of serotonin, including novel voltammetric absolute value measurement techniques. We also detail serotonin's role in various neuropsychiatric conditions, highlighting the role of phasic and tonic serotonergic neuronal firing within each where relevant. Lastly, we briefly review the present clinical application of these techniques and discuss the potential of a closed-loop monitoring and neuromodulation system utilizing deep brain stimulation (DBS).

Functional Magnetic Resonance Imaging Signatures of Pavlovian and Instrumental Valuation Systems during a Modified Orthogonalized Go/No-go Task

Motivational (i.e., Pavlovian) values interfere with instrumental responding and can lead to suboptimal decision-making. In humans, task-based neuroimaging studies have only recently started illuminating the functional neuroanatomy of Pavlovian biasing of instrumental control. To provide a mechanistic understanding of the neural dynamics underlying the Pavlovian and instrumental valuation systems, analysis of neuroimaging data has been informed by computational modeling of conditioned behavior. Nonetheless, because of collinearities in Pavlovian and instrumental predictions, previous research failed to tease out hemodynamic activity that is parametrically and dynamically modulated by coexistent Pavlovian and instrumental value expectations. Moreover, neural correlates of Pavlovian to instrumental transfer effects have so far only been identified in extinction (i.e., in the absence of learning). In this study, we devised a modified version of the orthogonalized go/no-go paradigm, which introduced Pavlovian-only catch trials to better disambiguate trial-by-trial Pavlovian and instrumental predictions in both sexes. We found that hemodynamic activity in the ventromedial pFC covaried uniquely with the model-derived Pavlovian value expectations. Notably, modulation of neural activity encoding for instrumental predictions in the supplementary motor cortex was linked to successful action selection in conflict conditions. Furthermore, hemodynamic activity in regions pertaining to the limbic system and medial pFC was correlated with synergistic Pavlovian and instrumental predictions and improved conditioned behavior during congruent trials. Altogether, our results provide new insights into the functional neuroanatomy of decision-making and corroborate the validity of our variant of the orthogonalized go/no-go task as a behavioral assay of the Pavlovian and instrumental valuation systems.

Serotonin neurons in mating female mice are activated by male ejaculation

Sexual stimulation triggers changes in female physiology and behavior, including sexual satiety and preparing the uterus for pregnancy. Serotonin (5-HT) is an important regulator of reproductive physiology and sexual receptivity, but the relationship between sexual stimulation and 5-HT neural activity in females is poorly understood. Here, we investigated dorsal raphe 5-HT neural activity in female mice during sexual behavior. We found that 5-HT neural activity in mating females peaked specifically upon male ejaculation and remained elevated above baseline until disengagement. Artificial intravaginal mechanical stimulation was sufficient to elicit increased 5-HT neural activity but the delivery of ejaculatory fluids was not. Distal penis expansion ("penile cupping") at ejaculation and forceful expulsion of ejaculatory fluid each provided sufficient mechanical stimulation to elicit 5-HT neuron activation. Our study identifies a female ejaculation-specific signal in a major neuromodulatory system and shows that intravaginal mechanosensory stimulation is necessary and sufficient to drive this signal.

Serotonin neurons in mating female mice are activated by male ejaculation

Sexual stimulation triggers changes in female physiology and behavior, including sexual satiety and preparing the uterus for pregnancy. Serotonin is an important regulator of reproductive physiology and sexual receptivity, but the relationship between sexual stimulation and serotonin neural activity in females is poorly understood. Here, we investigated dorsal raphe serotonin neural activity in females during sexual behavior. We found that serotonin neural activity in mating females peaked specifically upon male ejaculation, and remained elevated above baseline until disengagement. Artificial intravaginal mechanical stimulation was sufficient to elicit increased 5-HT neural activity but the delivery of ejaculatory fluids was not. Distal penis erectile enlargement ("penile cupping") at ejaculation and forceful expulsion of ejaculatory fluid each provided sufficient mechanical stimulation to elicit serotonin neuron activation. Our study identifies a female ejaculation-specific signal in a major neuromodulatory system and shows that intravaginal mechanosensory stimulation is necessary and sufficient to drive this signal.

Invasive Computational Psychiatry

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Risk taking for potential losses but not gains increases with time of day

Humans exhibit distinct risk preferences when facing choices involving potential gains and losses. These preferences are believed to be subject to neuromodulatory influence, particularly from dopamine and serotonin. As neuromodulators manifest circadian rhythms, this suggests decision making under risk might be affected by time of day. Here, in a large subject sample collected using a smartphone application, we found that risky options with potential losses were increasingly chosen over the course of the day. We observed this result in both a within-subjects design (N = 2599) comparing risky options chosen earlier and later in the day in the same individuals, and in a between-subjects design (N = 26,720) showing our effect generalizes across ages and genders. Using computational modelling, we show this diurnal change in risk preference reflects a decrease in sensitivity to increasing losses, but no change was observed in the relative impacts of gains and losses on choice (i.e., loss aversion). Thus, our findings reveal a striking diurnal modulation in human decision making, a pattern with potential importance for real-life decisions that include voting, medical decisions, and financial investments.

Fifty years on: Serotonin and depression

It has been over 50 years since the original serotonin hypothesis was proposed by the British Psychiatrist Alec Coppen. Recently, some authors have questioned the validity of the hypothesis. In this narrative review, we summarise the evidence for the serotonin hypothesis of depression, focusing on psychopharmacology and molecular imaging, as well as systems-level neuroscience.

Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks

Although we understand how serotonin receptors function at the single-cell level, what role different serotonin receptors play in regulating brain-wide activity and, in turn, human behavior, remains unknown. Here, we developed transcriptomic-neuroimaging mapping to characterize brain-wide functional signatures associated with specific serotonin receptors: serotonin receptor networks (SRNs). Probing SRNs with optogenetics-functional magnetic resonance imaging (MRI) and pharmacology in mice, we show that activation of dorsal raphe serotonin neurons differentially modulates the amplitude and functional connectivity of different SRNs, showing that receptors' spatial distributions can confer specificity not only at the local, but also at the brain-wide, network level. In humans, using resting-state functional MRI, SRNs replicate established divisions of serotonin effects on impulsivity and negative biases. These results provide compelling evidence that heterogeneous brain-wide distributions of different serotonin receptor types may underpin behaviorally distinct modes of serotonin regulation. This suggests that serotonin neurons may regulate multiple aspects of human behavior via modulation of large-scale receptor networks.

Clinical Study of Serum Serotonin as a Screening Marker for Anxiety and Depression in Patients with Type 2 Diabetes

Over time, studies have shown the importance of determining serotonin levels to diagnose somatic and psychiatric disorders. There are theoretical premises and practical ways to achieve a subtle correlation between the existence of comorbid psychiatric disorders and somatic diseases caused by the changes observed in serotonin levels. The present study, classified as retrospective and quantitative, provides evidence for determining the serotonin levels in patients with diabetes and anxiety or depression. A total of 48 patients with diabetes type 2 were enrolled in the study. Blood glucose level, glycated haemoglobin, and serum serotonin were noted, and they completed Hamilton A and Beck Depression Inventory questionnaires. We found robust correlations between serum serotonin and blood glucose (Sig. = 0.008), serum serotonin and HbA1c (Sig. = 0.007), serum serotonin and anxiety (Sig. = 0.000), and serum serotonin and depression (Sig. = 0.000). It is also noteworthy that women recorded extreme values higher than men for glycated haemoglobin (95% confidence interval: 6.92–7.79 in women and 6.30–7.23 in men). In conclusion, using serotonin as a marker of the mentioned diseases in clinical practice is of significant utility, considering the benefits in terms of the evolution and prognosis of comorbidities in patients with type 2 diabetes and anxiety and depressive symptoms.

A Role for Serotonin in Modulating Opposing Drive and Brake Circuits of Impulsivity

Impulsivity generally refers to a deficit in inhibition, with a focus on understanding the neural circuits which constitute the "brake" on actions and gratification. It is likely that increased impulsivity can arise not only from reduced inhibition, but also from a heightened or exaggerated excitatory "drive." For example, an action which has more vigor, or is fueled by either increased incentive salience or a stronger action-outcome association, may be harder to inhibit. From this perspective, this review focuses on impulse control as a competition over behavioral output between an initially learned response-reward outcome association, and a subsequently acquired opposing inhibitory association. Our goal is to present a synthesis of research from humans and animal models that supports this dual-systems approach to understanding the behavioral and neural substrates that contribute to impulsivity, with a focus on the neuromodulatory role of serotonin. We review evidence for the role of serotonin signaling in mediating the balance of the "drive" and "brake" circuits. Additionally, we consider parallels of these competing instrumental systems in impulsivity within classical conditioning processes (e.g., extinction) in order to point us to potential behavioral and neural mechanisms that may modulate the competing instrumental associations. Finally, we consider how the balance of these competing associations might contribute to, or be extracted from, our experimental assessments of impulsivity. A careful understanding of the underlying behavioral and circuit level contributions to impulsivity is important for understanding the pathogenesis of increased impulsivity present in a number of psychiatric disorders. Pathological levels of impulsivity in such disorders are likely subserved by deficits in the balance of motivational and inhibitory processes.

Aversive motivation and cognitive control

Aversive motivation plays a prominent role in driving individuals to exert cognitive control. However, the complexity of behavioral responses attributed to aversive incentives creates significant challenges for developing a clear understanding of the neural mechanisms of this motivation-control interaction. We review the animal learning, systems neuroscience, and computational literatures to highlight the importance of experimental paradigms that incorporate both motivational context manipulations and mixed motivational components (e.g., bundling of appetitive and aversive incentives). Specifically, we postulate that to understand aversive incentive effects on cognitive control allocation, a critical contextual factor is whether such incentives are associated with negative reinforcement or punishment. We further illustrate how the inclusion of mixed motivational components in experimental paradigms enables increased precision in the measurement of aversive influences on cognitive control. A sharpened experimental and theoretical focus regarding the manipulation and assessment of distinct motivational dimensions promises to advance understanding of the neural, monoaminergic, and computational mechanisms that underlie the interaction of motivation and cognitive control.

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.

Uncertainty alters the balance between incremental learning and episodic memory

A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

Value-based cognition and drug dependency

Value-based decision-making is thought to play an important role in drug dependency. Achieving elevated levels of euphoria or ameliorating dysphoria/pain may motivate goal-directed drug consumption in both drug-naïve and long-time users. In other words, drugs become viewed as the preferred means of attaining a desired internal state. The bias towards choosing drugs may affect one's cognition. Observed biases in learning, attention and memory systems within the brain gradually focus one's cognitive functions towards drugs and related cues to the exclusion of other stimuli. In this narrative review, the effects of drug use on learning, attention and memory are discussed with a particular focus on changes across brain-wide functional networks and the subsequent impact on behaviour. These cognitive changes are then incorporated into the cycle of addiction, an established model outlining the transition from casual drug use to chronic dependency. If drug use results in the elevated salience of drugs and their cues, the studies highlighted in this review strongly suggest that this salience biases cognitive systems towards the motivated pursuit of addictive drugs. This bias is observed throughout the cycle of addiction, possibly contributing to the persistent hold that addictive drugs have over the dependent. Taken together, the excessive valuation of drugs as the preferred means of achieving a desired internal state affects more than just decision-making, but also learning, attentional and mnemonic systems. This eventually narrows the focus of one's thoughts towards the pursuit and consumption of addictive drugs.

A Competition of Critics in Human Decision-Making

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about the mean and standard deviation of rewards, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin's hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.

The role of the gut microbiome in the development of schizophrenia

Schizophrenia is a heterogeneous neurodevelopmental disorder involving the convergence of a complex and dynamic bidirectional interaction of genetic expression and the accumulation of prenatal and postnatal environmental risk factors. The development of the neural circuitry underlying social, cognitive and emotional domains requires precise regulation from molecular signalling pathways, especially during critical periods or "windows", when the brain is particularly sensitive to the influence of environmental input signalling. Many of the brain regions involved, and the molecular substrates sub-serving these domains are responsive to life-long microbiota-gut-brain (MGB) axis signalling. This intricate microbial signalling system communicates with the brain via the vagus nerve, immune system, enteric nervous system, enteroendocrine signalling and production of microbial metabolites, such as short-chain fatty acids. Preclinical data has demonstrated that MGB axis signalling influences neurotransmission, neurogenesis, myelination, dendrite formation and blood brain barrier development, and modulates cognitive function and behaviour patterns, such as, social interaction, stress management and locomotor activity. Furthermore, preliminary clinical studies suggest altered gut microbiota profiles in schizophrenia. Unravelling MGB axis signalling in the context of an evolving dimensional framework in schizophrenia may provide a more complete understanding of the neurobiological architecture of this complex condition and offers the possibility of translational interventions.

Hidden Role of Gut Microbiome Dysbiosis in Schizophrenia: Antipsychotics or Psychobiotics as Therapeutics?

Schizophrenia is a chronic, heterogeneous neurodevelopmental disorder that has complex symptoms and uncertain etiology. Mounting evidence indicates the involvement of genetics and epigenetic disturbances, alteration in gut microbiome, immune system abnormalities, and environmental influence in the disease, but a single root cause and mechanism involved has yet to be conclusively determined. Consequently, the identification of diagnostic markers and the development of psychotic drugs for the treatment of schizophrenia faces a high failure rate. This article surveys the etiology of schizophrenia with a particular focus on gut microbiota regulation and the microbial signaling system that correlates with the brain through the vagus nerve, enteric nervous system, immune system, and production of postbiotics. Gut microbially produced molecules may lay the groundwork for further investigations into the role of gut microbiota dysbiosis and the pathophysiology of schizophrenia. Current treatment of schizophrenia is limited to psychotherapy and antipsychotic drugs that have significant side effects. Therefore, alternative therapeutic options merit exploration. The use of psychobiotics alone or in combination with antipsychotics may promote the development of novel therapeutic strategies. In view of the individual gut microbiome structure and personalized response to antipsychotic drugs, a tailored and targeted manipulation of gut microbial diversity naturally by novel prebiotics (non-digestible fiber) may be a successful alternative therapeutic for the treatment of schizophrenia patients.

Hidden Role of Gut Microbiome Dysbiosis in Schizophrenia: Antipsychotics or Psychobiotics as Therapeutics?

Schizophrenia is a chronic, heterogeneous neurodevelopmental disorder that has complex symptoms and uncertain etiology. Mounting evidence indicates the involvement of genetics and epigenetic disturbances, alteration in gut microbiome, immune system abnormalities, and environmental influence in the disease, but a single root cause and mechanism involved has yet to be conclusively determined. Consequently, the identification of diagnostic markers and the development of psychotic drugs for the treatment of schizophrenia faces a high failure rate. This article surveys the etiology of schizophrenia with a particular focus on gut microbiota regulation and the microbial signaling system that correlates with the brain through the vagus nerve, enteric nervous system, immune system, and production of postbiotics. Gut microbially produced molecules may lay the groundwork for further investigations into the role of gut microbiota dysbiosis and the pathophysiology of schizophrenia. Current treatment of schizophrenia is limited to psychotherapy and antipsychotic drugs that have significant side effects. Therefore, alternative therapeutic options merit exploration. The use of psychobiotics alone or in combination with antipsychotics may promote the development of novel therapeutic strategies. In view of the individual gut microbiome structure and personalized response to antipsychotic drugs, a tailored and targeted manipulation of gut microbial diversity naturally by novel prebiotics (non-digestible fiber) may be a successful alternative therapeutic for the treatment of schizophrenia patients.

Smartphones and the Neuroscience of Mental Health

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Negative Air Ions in Neuropsychiatric Disorders

BACKGROUND

Air ions (AIs) are clusters of ionized particles present in the atmosphere, carrying an electrical charge of negative or positive polarity. Past speculations suggested that exposure to positive air ions may be harmful, while exposure to negative air ions (NAIs) may be associated with beneficial health effects. Increasing attention has been directed towards investigating the potential effect of NAIs on human brain activities since initial observations of their beneficial effects on some cognitive processes and mood.

AIMS

Given the paucity and scattered literature, our paper aims to review the available studies on potential positive effects of NAIs exposure on cognitive performances and depression.

DISCUSSION

The review of the literature seems to confirm the effects of NAIs on several brain functions. Indeed, a significant association between NAIs exposure and both well-being and high cognitive performances has been described. Furthermore, exposure to high concentrations of NAIs could be related to the improvement of depressive symptoms.

CONCLUSION

A growing evidence of data, although not yet conclusive, would suggest that NAIs might improve cognitive processes. These findings require specific and urgent controlled trials adopting systems based on AIs release to possibly prevent and treat cognitive dysfunctions present in a broad range of neuropsychiatric conditions.

A single dose of escitalopram blunts the neural response in the thalamus and caudate during monetary loss

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) show acute effects on the neural processes associated with negative affective bias in healthy people and people with depression. However, whether and how SSRIs also affect reward and punishment processing on a similarly rapid time scale remains unclear.

METHODS

We investigated the effects of an acute and clinically relevant dose (20 mg) of the SSRI escitalopram on brain response during reward and punishment processing in 19 healthy participants. In a doubleblind, placebo-controlled study using functional MRI, participants performed a well-established monetary reward task at 3 time points: at baseline; after receiving placebo or escitalopram; and after receiving placebo or escitalopram following an 8-week washout period.

RESULTS

Acute escitalopram administration reduced blood-oxygen-level-dependent (BOLD) response during punishment feedback in the right thalamus (family-wise error corrected [FWE] p = 0.013 at peak level) and the right caudate head (pFWE = 0.011 at peak level) compared to placebo. We did not detect any significant BOLD changes during reward feedback.

LIMITATIONS

We included only healthy participants, so interpretation of findings are limited to the healthy human brain and require future testing in patient populations. The paradigm we used was based on monetary stimuli, and results may not be generalizable to other forms of reward.

CONCLUSION

Our findings extend theories of rapid SSRI action on the neural processing of rewarding and aversive stimuli and suggest a specific and acute effect of escitalopram in the punishment neurocircuitry.

Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.

The Relationship between Overweight/Obesity and Executive Control in College Students: The Mediating Effect of BDNF and 5-HT

The main aim of this study was to explore the association between overweight/obesity and executive control (EC) in young adults, and to further analyze the mediating effect of brain-derived neurotrophic factor (BDNF) and serotonin (5-hydroxytryptamine (5-HT)) on the relationship between overweight/obesity and EC. A total of 449 college students aged between 18 and 20 years were recruited for the study between March and December 2019. Their height and weight were then measured professionally. Subsequently, body mass index (BMI) was calculated as weight (kg) divided by the square of height (m). The EC of the participants was then estimated using the Flanker task, while their serum BDNF levels and 5-HT levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit. Finally, the multiple intermediary models in SPSS were used to analyze the mediating effect of 5-HT and BDNF between overweight/obesity and EC. The result show that the overweight/obesity of college students was positively correlated with the response of EC (p ≤ 0.005). However, it was negatively correlated with BDNF (p ≤ 0.05) and 5-HT (p ≤ 0.05). Moreover, BDNF (p ≤ 0.001) and 5-HT (p ≤ 0.001) were negatively correlated with the response of EC. The BDNF level played a partial mediating role between overweight/obesity and EC that accounted for 7.30% of the total effect value. Similarly, the 5-HT of college students played a partial mediating role between overweight/obesity and EC that accounted for 8.76% of the total effect value. Gender and age had no regulatory effect on the relationship between overweight/obesity, BDNF, 5-HT, and EC. This study provides the evidence that 5-HT and BDNF mediated the association between overweight/obesity and executive control. It is indicated that 5-HT and BDNF might be the biological pathways underpinning the link between overweight/obesity and executive control.

A comparison of 'pruning' during multi-step planning in depressed and healthy individuals

BACKGROUND

Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people 'prune' (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals.

METHODS

Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms.

RESULTS

Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety.

CONCLUSIONS

We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.

Motivational competition and the paraventricular thalamus

Although significant progress has been made in understanding the behavioral and brain mechanisms for motivational systems, much less is known about competition between motivational states or motivational conflict (e.g., approach - avoidance conflict). Despite being produced under diverse conditions, behavior during motivational competition has two signatures: bistability and metastability. These signatures reveal the operation of positive feedback mechanisms in behavioral selection. Different neuronal architectures can instantiate this selection to achieve bistability and metastability in behavior, but each relies on circuit-level inhibition to achieve rapid and stable selection between competing tendencies. Paraventricular thalamus (PVT) is identified as critical to this circuit level inhibition, resolving motivational competition via its extensive projections to local inhibitory networks in the ventral striatum and extended amygdala, enabling adaptive responding under motivational conflict.

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.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

Frontal white matter abnormalities reveal the pathological basis underlying negative symptoms in antipsychotic-naïve, first-episode patients with adolescent-onset schizophrenia: Evidence from multimodal brain imaging

A major challenge in schizophrenia is to uncover the pathophysiological basis of its negative symptoms. Recent neuroimaging studies revealed that disrupted structural properties of frontal white matter (FWM) are associated with the negative symptoms of schizophrenia. However, there is little direct functional evidence of FWM for negative symptoms in schizophrenia. To address this issue, we combined resting-state connectome-wide functional connectivity (FC) and diffusion tensor imaging tractography to investigate the alteration of FWM underlying the negative symptoms in 39 drug-naive patients with adolescent-onset schizophrenia (AOS) and 31 age- and sex- matched healthy controls (HCs). Results revealed that the intrinsic FC and structural properties (fraction anisotropy and fibers) of the left FWM correspond to individual negative symptoms in AOS. Moreover, the serotonin network (raphe nuclei, anterior and posterior cingulate cortices, and prefrontal and inferior parietal cortices) and FWM-cingulum network were found to contributed to the negative symptom severity in AOS. Furthermore, the patients showed abnormal functional and structural connectivities between the interhemispheric FWM compared with HCs. Importantly, the decreased fiber counts between the interhemispheric FWM were inversely correlated with the negative symptoms in AOS. Our findings demonstrated the association between FWM and negative symptoms, and offered initial evidence by using WM connectome to uncover WM functional information in schizophrenia.

Animal affect and decision-making

The scientific study of animal affect (emotion) is an area of growing interest. Whilst research on mechanism and causation has predominated, the study of function is less advanced. This is not due to a lack of hypotheses; in both humans and animals, affective states are frequently proposed to play a pivotal role in coordinating adaptive responses and decisions. However, exactly how they might do this (what processes might implement this function) is often left rather vague. Here we propose a framework for integrating animal affect and decision-making that is couched in modern decision theory and employs an operational definition that aligns with dimensional concepts of core affect and renders animal affect empirically tractable. We develop a model of how core affect, including short-term (emotion-like) and longer-term (mood-like) states, influence decision-making via processes that we label affective options, affective predictions, and affective outcomes and which correspond to similar concepts in schema of the links between human emotion and decision-making. Our framework is generalisable across species and generates questions for future research.

Individually irrational pruning is essential for ecological rationality in a social context

Heuristics, commonly thought to violate the full rationality assumptions, are paradoxically indispensable parts of our decision-making and learning processes. To resolve this seemingly paradox, there have been several studies in the literature that aim at finding some broad daily life conditions and situations where employing heuristics are rational. However, these researches mainly focus on non-social conditions, whereas, for human beings, social and individual processes are interwoven and it would be better to study them jointly. Here, we study the role of pruning heuristic in individual reinforcement learning in a social context, where our simulated learning agents make many of their decisions relying on others' knowledge. Our simulation results suggest that the seemingly irrational pruning heuristic leads to less cost in the social settings. That is, we have a meaningfully more social outcome in the presence of this heuristic in social contexts, and social learning helps the agents to learn better where the pruning heuristic is an obstacle in the way of finding the optimal solution in the individual setting. In sum, the synergy between the pruning behavior and social learning leads to ecological rationality.

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Bipolar disorder is characterized by mood swings-oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patient's mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms (action responses) leads to a positive outcome, while the other leads to a negative outcome. We explore the dynamics of key reward and risk related parameters in the system while the model agent receives various outcomes. Particularly, we study the system using a model that represents the fast dynamics of decision making, and a module to capture the slow dynamics that describe the variation of some meta-parameters of fast dynamics over long time scales. The model is cast at three levels of abstraction: (1) a two-dimensional dynamical system model, that is a simple two variable model capable of showing bistability for rewarding and punitive outcomes; (2) a phenomenological basal ganglia model, to extend the implications from the reduced model to a cortico-basal ganglia setup; (3) a detailed network model of basal ganglia, that incorporates detailed cellular level models for a more realistic understanding. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological 'bipolar-like' oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The network's dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

Meta-analysis of alcohol induced gut dysbiosis and the resulting behavioral impact

About 99% of the unique genes and almost half of the cells found in the human body come from microbes including bacteria, archaea, fungi, and viruses. Collectively these microorganisms contribute to the microbiome and often reside in the gut. The gut microbiome plays an important role in the body and contributes to digestive health, the immune system, and brain function. The gut microbiome interacts with the central nervous system through the vagal pathways as well as the endocrine or immune pathways. Changes in the proportion or diversity of the microbiota can have an impact on normal physiology and has been implicated in inflammation, depression, obesity, and addiction. Several animal studies suggest the involvement of gut microbiome in the regulation of pain, emotion, and cognition. Alcoholism has been linked with gut microbiome dysbiosis and thus can have deleterious effects on the gut-brain axis balance. Gut microbiome produces important metabolites such as gastrointestinal hormones, short chain fatty acids, precursors to the neuroactive compounds and neurotransmitters that impact the physiology and normal functioning of the body. The microbiome imbalance has been correlated with behavioral changes and alcohol dependence in the host. The objective of this study is to elucidate the link between alcohol induced gut microbiota dysbiosis and any behavioral impact that could incur. A thorough literature search of various databases was conducted to gather data for the alcohol prompted gut microbiome dysbiosis. Ingenuity Pathway Analysis (IPA¹) software was then utilized to identify links between alcoholism, gut microbiome derived metabolites, and their role in behavior alterations. Overall, this meta-analysis reviews information available on the connection between alcohol induced gut microbiome dysbiosis and the resulting behavioral impact.

Dopamine signals related to appetitive and aversive events in paradigms that manipulate reward and avoidability

Using environmental cues to acquire good and avoid harmful things is critical for survival. Rewarding and aversive outcomes both drive behavior through reinforcement learning and sometimes occur together in the environment, but it remains unclear how these signals are encoded within the brain and if signals for positive and negative reinforcement are encoded similarly. Recent studies demonstrate that the dopaminergic system and interconnected brain regions process both positive and negative reinforcement necessary for approach and avoidance behaviors, respectively. Here, we review these data with a special focus on behavioral paradigms that manipulate both expected reward and the avoidability of aversive events to reveal neural correlates related to value, prediction error encoding, motivation, and salience.

Gut microbiota and bipolar disorder: a review of mechanisms and potential targets for adjunctive therapy

There is increasing evidence that connections formed between microbiome, the gut, and the brain play a role in health and well-being. Non-pharmaceutical targets for management of mood disorders, such as bipolar disorder, are relatively under-researched. At the same time, it is clear that there is an intimate connection between psychiatry and gastrointestinal health. Here, we have discussed various comorbid conditions associated with bipolar disorders such as inflammation, irritable bowel disease and antibiotic induced mania with importance to demonstrate possible involvement of the gut microbiota. Gut microbiota-targeted preclinical and clinical interventions have demonstrated enhancement in various psychological conditions. Further in this review, we explore links between bipolar disorder, inflammation and gut microbiome with a focus on dietary, pro- and pre-biotic interventions as potential adjuvant therapies for use in the management of mood disorders such as bipolar disorder.

Computational Underpinnings of Neuromodulation in Humans

We summarize a new approach to neuromodulator detection that provides colocalized detection of dopamine, serotonin, and norepinephrine at subsecond timescales and promises to provide submillisecond estimates of the same. The methodology, elastic net electrochemistry, is used to estimate dopamine and serotonin in the striatum of conscious human subjects during active decision-making. We show a proof-of-principle example of the same method working on commercially available depth electrodes in common use for epilepsy monitoring and neurosurgical planning in humans, which further promises to make such electrodes sources of fast neuromodulator information never before available in human subjects. We discuss the implications of this methodology for making direct tests in humans of the computations carried by these three important neuromodulatory systems. The methods also promise great utility in model organisms, but this chapter focuses on the possibilities for human use.

Optimizing the depth and the direction of prospective planning using information values

Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future. Expanding deep trees, however, is computationally taxing. Therefore, machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future. Two outstanding questions in this scheme are “in which directions the search tree should be expanded?”, and “when should the expansion stop?”. Here we propose a principled solution to these questions based on a speed/accuracy tradeoff: deeper expansion in the appropriate directions leads to more accurate planning, but at the cost of slower decision-making. Our simulation results show how this algorithm expands the search tree effectively and efficiently in a grid-world environment. We further show that our algorithm can explain several behavioral patterns in animals and humans, namely the effect of time-pressure on the depth of planning, the effect of reward magnitudes on the direction of planning, and the gradual shift from goal-directed to habitual behavior over the course of training. The algorithm also provides several predictions testable in animal/human experiments.

When faced with several choices in complex environments like chess, thinking about all the potential consequences of each choice, infinitely deep into the future, is simply impossible due to time and cognitive limitations. An outstanding question is what is the best direction and depth of thinking about the future? Here we propose a mathematical algorithm that computes, along the course of planning, the benefit of thinking another step in a given direction into the future, and compares that with the cost of thinking in order to compute the net benefit. We show that this algorithm is consistent with several behavioral patterns observed in humans and animals, suggesting that they, too, make efficient use of their time and cognitive resources when deciding how deep to think.

A computational perspective on social attachment

Humans depend on social relationships for survival and wellbeing throughout life. Yet, individuals differ markedly in their ability to form and maintain healthy social relationships. Here we use a simple mathematical model to formalize the contention that a person's attachment style is determined by what they learn from relationships early in life. For the sake of argument, we therefore discount individual differences in the innate personality or attachment style of a child, assuming instead that all children are simply born with an equivalent, generic, hardwired desire and instinct for social proximity, and a capacity to learn. In line with the evidence, this innate endowment incorporates both simple bonding instincts and a capacity for cognitively sophisticated beliefs and generalizations. Under this assumption, we then explore how distinct attachment styles might emerge through interaction with the child's early caregivers. Our central question is, how an apparently adaptive capacity to learn can yield enduring maladaptive attachment styles that generalize to new relationships. We believe extensions of our model will ultimately help clarify the complex interacting mechanisms - both acquired and innate - that underpin individual differences in attachment styles. While our model is relatively abstract, we also attempt some connection to known biological mechanisms of attachment.

Hierarchical Au nanoclusters electrodeposited on amine-terminated ITO glass as a SERS-active substrate for the reliable and sensitive detection of serotonin in a Tris-HCl buffer solution

In this study, a SERS-active substrate was fabricated by electrodepositing hierarchical Au nanostructures on amine-terminated ITO (indium tin oxide) glass to achieve an enhanced Raman signal of 5-HT.