Confidence and psychosis: a neuro-computational account of contingency learning disruption by NMDA blockade

Navigating Uncertainty: The Role of Mood and Confidence in Decision-Making Flexibility and Performance

Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety. Recent evidence, however, highlights that uncertainty can also evoke positive emotions, such as surprise, interest, excitement, and enthusiasm, depending on one's task expectations. Despite this, the interplay between mood, confidence, and learning remains underexplored. Some studies indicate that self-reported mood does not always align with confidence, as these constructs evolve on different timescales. We propose that mood influences confidence, thereby enhancing decision flexibility-defined as the ability to switch effectively between exploration and exploitation. This increased flexibility is expected to improve task performance by increasing accuracy. Our findings support this hypothesis, revealing that confidence modulates exploration/exploitation strategies and learning rates, while mood affects reward perception and confidence levels. These findings indicate that metacognition entails a dynamic balance between exploration and exploitation, integrating mood states with high-level cognitive processes.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

A Meta-Analysis of Functional Neuroimaging Studies of Ketamine Administration in Healthy Volunteers

Ketamine administration leads to a psychotomimetic state when taken in large bolus doses, making it a valid model of psychosis. Therefore, understanding ketamine's effects on brain functioning is particularly relevant. This meta-analysis focused on neuroimaging studies that examined ketamine-induced brain activation at rest and during a task. Included are 10 resting-state studies and 23 task-based studies, 9 of which were measuring executive functions. Using a stringent statistical threshold (TFCE <0.05), the results showed increased activity at rest in the dorsal anterior cingulate cortex (ACC), and increased activation of the right Heschl's gyrus during executive tasks, following ketamine administration. Uncorrected results showed increased activation at rest in the right (anterior) insula and the right-fusiform gyrus, as well as increased activation during executive tasks in the rostral ACC. Rest-state studies highlighted alterations in core hubs of the salience network, while task-based studies suggested an impact on task-irrelevant brain regions. Increased activation in the rostral ACC may indicate a failure to deactivate the default mode network during executive tasks following ketamine administration. The results are coherent with alterations found in schizophrenia, which confer external validity to the ketamine model of psychosis. Studies investigating the neural mechanisms of ketamine's antidepressant action are warranted.

Dopamine release in human associative striatum during reversal learning

The dopaminergic system is firmly implicated in reversal learning but human measurements of dopamine release as a correlate of reversal learning success are lacking. Dopamine release and hemodynamic brain activity in response to unexpected changes in action-outcome probabilities are here explored using simultaneous dynamic [11C]Raclopride PET-fMRI and computational modelling of behavior. When participants encounter reversed reward probabilities during a card guessing game, dopamine release is observed in associative striatum. Individual differences in absolute reward prediction error and sensitivity to errors are associated with peak dopamine receptor occupancy. The fMRI response to perseverance errors at the onset of a reversal spatially overlap with the site of dopamine release. Trial-by-trial fMRI correlates of absolute prediction errors show a response in striatum and association cortices, closely overlapping with the location of dopamine release, and separable from a valence signal in ventral striatum. The results converge to implicate striatal dopamine release in associative striatum as a central component of reversal learning, possibly signifying the need for increased cognitive control when new stimuli-responses should be learned.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

Anxiety associated with perceived uncontrollable stress enhances expectations of environmental volatility and impairs reward learning

Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. Such updating is however useful only if action values are sufficiently stable, something that a lack of control may impair. We examined whether self-reported stress uncontrollability during the first wave of the COVID-19 pandemic predicted impaired reward-learning. In a preregistered study during the first-wave of the COVID-19 pandemic, we used self-reported measures of depression, anxiety, uncontrollable stress, and COVID-19 risk from 427 online participants to predict performance in a three-armed-bandit probabilistic reward learning task. As hypothesised, uncontrollable stress predicted impaired learning, and a greater proportion of probabilistic errors following negative feedback for correct choices, an effect mediated by state anxiety. A parameter from the best-fitting hidden Markov model that estimates expected beliefs that the identity of the optimal choice will shift across images, mediated effects of state anxiety on probabilistic errors and learning deficits. Our findings show that following uncontrollable stress, anxiety promotes an overly volatile representation of the reward-structure of uncertain environments, impairing reward attainment, which is a potential path to anhedonia in depression.

A characterization of the neural representation of confidence during probabilistic learning

Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.

Reward disturbances in antipsychotic-naïve patients with first-episode psychosis and their association to glutamate levels

BACKGROUND

Aberrant anticipation of motivational salient events and processing of outcome evaluation in striatal and prefrontal regions have been suggested to underlie psychosis. Altered glutamate levels have likewise been linked to schizophrenia. Glutamatergic abnormalities may affect the processing of motivational salience and outcome evaluation. It remains unresolved, whether glutamatergic dysfunction is associated with the coding of motivational salience and outcome evaluation in antipsychotic-naïve patients with first-episode psychosis.

METHODS

Fifty-one antipsychotic-naïve patients with first-episode psychosis (22 ± 5.2 years, female/male: 31/20) and 52 healthy controls (HC) matched on age, sex, and parental education underwent functional magnetic resonance imaging and magnetic resonance spectroscopy (3T) in one session. Brain responses to motivational salience and negative outcome evaluation (NOE) were examined using a monetary incentive delay task. Glutamate levels were estimated in the left thalamus and anterior cingulate cortex using LCModel.

RESULTS

Patients displayed a positive signal change to NOE in the caudate (p = 0.001) and dorsolateral prefrontal cortex (DLPFC; p = 0.003) compared to HC. No group difference was observed in motivational salience or in levels of glutamate. There was a different association between NOE signal in the caudate and DLPFC and thalamic glutamate levels in patients and HC due to a negative correlation in patients (caudate: p = 0.004, DLPFC: p = 0.005) that was not seen in HC.

CONCLUSIONS

Our findings confirm prior findings of abnormal outcome evaluation as a part of the pathophysiology of schizophrenia. The results also suggest a possible link between thalamic glutamate and NOE signaling in patients with first-episode psychosis.

Evaluation of Early Ketamine Effects on Belief-Updating Biases in Patients With Treatment-Resistant Depression

IMPORTANCE

Clinical research has shown that persistent negative beliefs maintain depression and that subanesthetic ketamine infusions induce rapid antidepressant responses.

OBJECTIVE

To evaluate whether ketamine alters belief updating and how such cognitive effects are associated with the clinical effects of ketamine.

DESIGN, SETTING, AND PARTICIPANTS

This study used an observational case-control protocol with a mixed-effects design that nested 2 groups by 2 testing time points. Observers were not blinded. Patients with treatment-resistant depression (TRD) and healthy volunteer participants aged 34 to 68 years were included. Patients with TRD were diagnosed with major depressive disorder or bipolar depression, had a Montgomery-Åsberg Depression Rating Scale score greater than 20, a Maudsley Staging Method score greater than 7, and failed to respond to at least 2 prior antidepressant trials. Exclusion criteria were any other psychiatric, neurological, or neurosurgical comorbidities, substance use or addictive disorders, and recreational ketamine consumption. Data were collected from January to February 2019 and from May to December 2019, and data were analyzed from January 2020 to July 2021.

EXPOSURES

Patients with TRD were observed 24 hours before single ketamine infusion, 4 hours after the infusion, and 4 hours after the third infusion, which was 1 week after the first infusion. Healthy control participants were observed twice 1 week apart without ketamine exposure.

MAIN OUTCOMES AND MEASURES

Montgomery-Åsberg Depression Rating Scale score and belief updating after belief updating when patients received good news and bad news measured by a cognitive belief-updating task and mathematically formalized by a computational reinforcement learning model.

RESULTS

Of 56 included participants, 29 (52%) were male, and the mean (SEM) age was 52.3 (1.2) years. A total of 26 patients with TRD and 30 control participants were included. A significant group × testing time point × news valence interaction showed that patients with TRD updated their beliefs more after good than bad news following a single ketamine infusion (controlled for age and education: β = -0.91; 95% CI, -1.58 to -0.24; t216 = -2.67; P = .008) than controls. Computational modeling showed that this effect was associated with asymmetrical learning rates (LRs) after ketamine treatment (good news LRs after ketamine, 0.51 [SEM, 0.04]; bad news LRs after ketamine 0.36 [SEM, 0.03], t25 = 3.8; P < .001) and partially mediated early antidepressant responses (path a*b: β = -1.00 [SEM, 0.66]; t26 = -1.53; z = -1.98; P = .04).

CONCLUSIONS AND RELEVANCE

These findings provide novel insights into the cognitive mechanisms of the action of ketamine in patients with TRD, with promising perspectives for augmented psychotherapy for individuals with mood disorders.

A polymorphism in the glutamate metabotropic receptor 7 is associated with cognitive deficits in the early phases of psychosis

Schizophrenia is an illness characterized by positive symptoms, negative symptoms, and cognitive impairments. Cognitive impairments occur before the onset of psychosis and could reflect glutamatergic dysregulation. Thus, identifying associations between genetic variations in genes coding for glutamatergic receptors and cognitive impairment in schizophrenia may help in understanding the basis of these deficits and in identifying potential drug targets. In a discovery cohort of 144 first-episode of psychosis patients (FEP), we genotyped 58 candidate Single Nucleotide Polymorphisms (SNPs) located in NMDA and metabotropic glutamatergic receptors. These SNPs were selected according to the results from the Psychiatric Genomic Consortium and were tested for association with intellectual quotient (IQ) as assessed with the Wechsler Intelligence Scales. For replication, we used the ICAAR cohort including 121 ultra-high-risk patients (UHR) with the same cognitive assessment. A polymorphism located in GRM7, rs1396409, was significantly associated with performance IQ in the discovery cohort of FEP. This association was replicated in the UHR cohort. This polymorphism is also associated with total IQ and verbal IQ in the merged dataset, with a predominant effect on the arithmetic subtest. The rs1396409 polymorphism is significantly associated with cognitive impairment during the onset of psychosis. This genetic association highlights the possible impact of glutamatergic genes in cognitive deficits in the early phases of psychosis and enforces the interest for new therapeutic interventions targeting the glutamatergic pathway.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Motivational signals disrupt metacognitive signals in the human ventromedial prefrontal cortex

A growing body of evidence suggests that, during decision-making, BOLD signal in the ventromedial prefrontal cortex (VMPFC) correlates both with motivational variables – such as incentives and expected values – and metacognitive variables – such as confidence judgments – which reflect the subjective probability of being correct. At the behavioral level, we recently demonstrated that the value of monetary stakes bias confidence judgments, with gain (respectively loss) prospects increasing (respectively decreasing) confidence judgments, even for similar levels of difficulty and performance. If and how this value-confidence interaction is reflected in the VMPFC remains unknown. Here, we used an incentivized perceptual decision-making fMRI task that dissociates key decision-making variables, thereby allowing to test several hypotheses about the role of the VMPFC in the value-confidence interaction. While our initial analyses seemingly indicate that the VMPFC combines incentives and confidence to form an expected value signal, we falsified this conclusion with a meticulous dissection of qualitative activation patterns. Rather, our results show that strong VMPFC confidence signals observed in trials with gain prospects are disrupted in trials with no – or negative (loss) – monetary prospects. Deciphering how decision variables are represented and interact at finer scales seems necessary to better understand biased (meta)cognition.

The human ventromedial prefrontal cortex helps to determine value and confidence in certain decisions, but only in situations when there is a potential for a (monetary) reward.

Premature commitment to uncertain decisions during human NMDA receptor hypofunction

Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction.

Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches

To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment’s latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.

A New Look on an Old Issue: Comprehensive Review of Neurotransmitter Studies in Cerebrospinal Fluid of Patients with Schizophrenia and Antipsychotic Effect on Monoamine's Metabolism

Neurotransmitters metabolism has a key role in the physiopathology of schizophrenia as demonstrated by studies measuring monoamine metabolites in patient’s cerebrospinal fluid (CSF) since the beginning of the antipsychotic use. This comprehensive review aims to understand the anomalies of CSF monoamines in schizophrenia and their correlation with clinical and paraclinical features. We also review the influence of antipsychotic treatment on CSF monoamines and discuss the connection with metabolic and inflammatory processes. Studies comparing CSF homovanillic acid (HVA) levels between patients and controls are miscellaneous, due to the heterogeneity of samples studies. However, low HVA is associated with more positive symptoms and a poorer outcome and negatively correlated with brain ventricle size. Based on humans and animals’ studies, antipsychotic treatments increase HVA during the first week of administration and decrease progressively over the time with a fall-off after withdrawal. 5‐hydroxyindolacetic acetic acid levels do not seem to be different in the patient’s CSF compared to controls. Considering metabolic co-factors of neurotrans-mitters synthesis, there is evidence supporting an increase of kynurenic acid in the CSF of patients with schizophrenia. Few studies explore folate metabolism in CSF. Literature also emphasizes the relationship between folate metabolism, inflammation and monoamine’s metabolism. Those results suggest that the CSF monoamines could be correlated with schizophrenia symptoms and treatment outcome. However, further studies, exploring the role of CSF monoamines as biomarkers of disease severity and response to treatment are needed. They should assess the antipsychotic prescription, inflammatory markers and folate metabolism as potential confounding factors.

Electrophysiological Signatures of Hierarchical Learning

Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-making remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter [HGF]) with electroencephalography (EEG), while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, nonhierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning and encodes both low- and high-level learning signals separately and adaptively.

The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty

The ability to represent and respond to uncertainty is fundamental to human cognition and decision-making. Noradrenaline (NA) is hypothesized to play a key role in coordinating the sensory, learning, and physiological states necessary to adapt to a changing world, but direct evidence for this is lacking in humans. Here, we tested the effects of attenuating noradrenergic neurotransmission on learning under uncertainty. We probed the effects of the β-adrenergic receptor antagonist propranolol (40 mg) using a between-subjects, double-blind, placebo-controlled design. Participants performed a probabilistic associative learning task, and we employed a hierarchical learning model to formally quantify prediction errors about cue-outcome contingencies and changes in these associations over time (volatility). Both unexpectedness and noise slowed down reaction times, but propranolol augmented the interaction between these main effects such that behavior was influenced more by prior expectations when uncertainty was high. Computationally, this was driven by a reduction in learning rates, with people slower to update their beliefs in the face of new information. Attenuating the global effects of NA also eliminated the phasic effects of prediction error and volatility on pupil size, consistent with slower belief updating. Finally, estimates of environmental volatility were predicted by baseline cardiac measures in all participants. Our results demonstrate that NA underpins behavioral and computational responses to uncertainty. These findings have important implications for understanding the impact of uncertainty on human biology and cognition.

A continuum hypothesis of psychotomimetic rapid antidepressants

Ketamine, classical psychedelics and sleep deprivation are associated with rapid effects on depression. Interestingly, these interventions also have common psychotomimetic actions, mirroring aspects of psychosis such as an altered sense of self, perceptual distortions and distorted thinking. This raises the question whether these interventions might be acute antidepressants through the same mechanisms that underlie some of their psychotomimetic effects. That is, perhaps some symptoms of depression can be understood as occupying the opposite end of a spectrum where elements of psychosis can be found on the other side. This review aims at reviewing the evidence underlying a proposed continuum hypothesis of psychotomimetic rapid antidepressants, suggesting that a range of psychotomimetic interventions are also acute antidepressants as well as trying to explain these common features in a hierarchical predictive coding framework, where we hypothesise that these interventions share a common mechanism by increasing the flexibility of prior expectations. Neurobiological mechanisms at play and the role of different neuromodulatory systems affected by these interventions and their role in controlling the precision of prior expectations and new sensory evidence will be reviewed. The proposed hypothesis will also be discussed in relation to other existing theories of antidepressants. We also suggest a number of novel experiments to test the hypothesis and highlight research areas that could provide further insights, in the hope to better understand the acute antidepressant properties of these interventions.

The next psychoactive drugs: From imipramine to ketamine

Since the 1950s, the therapeutic arsenal against depression has grown considerably. From the discovery of mono-amine oxidase inhibitors (MAOIs) to the antidepressant effect of ketamine, several pharmacological breakthroughs made the history of psychiatry. These discoveries oriented the research about the pathophysiology of depression, which is one of the most disabling diseases worldwide affecting 10 to 20% of general population. In this article, we offer a short historical review of the various therapeutic options developed over the past century and the consequences of these innovations. We then review the discovery of the antidepressant effects of ketamine (and its S-enantiomer, esketamine), the lastest development in depression treatment. Ketamine's effects are spectacular both in terms of their very short onset time, and because they are observed even in treatment-resistant depression. Just as MAOIs and tricyclic antidepressants allowed the "monoaminergic hypothesis of depression" to emerge, unravelling the mechanisms of ketamine's antidepressant effects should highlight the role of glutamatergic system and neuro-inflammation in the neurobiology of depression. Ketamine might also help to refine our understanding of the cognitive pathophysiology of depression and to deeply transform the clinical representations of depressive disorder.

[The next psychoactive drugs: From imipramine to ketamine]

Depuis les années 1950, l’arsenal thérapeutique permettant de lutter contre la dépression s’est considérablement enrichi. De la découverte des inhibiteurs de la monoamine oxydase (IMAO) à celle de la kétamine, ces percées pharmacologiques ont marqué l’histoire de la psychiatrie et guidé la recherche sur la physiopathologie de la dépression, cette pathologie dévastatrice affectant entre 10 et 20 % de la population mondiale. Nous proposons dans cet article une courte revue historique des différentes options thérapeutiques développées au cours du siècle passé et des conséquences qu’ont eu ces innovations. Nous réalisons ensuite un état des lieux de la plus récente de ces découvertes, celle des effets antidépresseurs de la kétamine (et de son énantiomère S, l’eskétamine), spectaculaires de par leur délai d’apparition et leur efficacité même dans les formes les plus résistantes de dépression. De même que la découverte des IMAO et des tricycliques a permis de concevoir une théorie monoaminergique de la dépression, l’étude des mécanismes d’actions de la kétamine pourrait permettre de comprendre le rôle de la transmission glutamatergique ou de la neuro-inflammation dans la neurobiologie de cette pathologie, d’affiner nos connaissances sur sa physiopathologie cognitive ou encore de transformer en profondeur les représentations des cliniciens sur cette maladie.

Robust valence-induced biases on motor response and confidence in human reinforcement learning

In simple instrumental-learning tasks, humans learn to seek gains and to avoid losses equally well. Yet, two effects of valence are observed. First, decisions in loss-contexts are slower. Second, loss contexts decrease individuals’ confidence in their choices. Whether these two effects are two manifestations of a single mechanism or whether they can be partially dissociated is unknown. Across six experiments, we attempted to disrupt the valence-induced motor bias effects by manipulating the mapping between decisions and actions and imposing constraints on response times (RTs). Our goal was to assess the presence of the valence-induced confidence bias in the absence of the RT bias. We observed both motor and confidence biases despite our disruption attempts, establishing that the effects of valence on motor and metacognitive responses are very robust and replicable. Nonetheless, within- and between-individual inferences reveal that the confidence bias resists the disruption of the RT bias. Therefore, although concomitant in most cases, valence-induced motor and confidence biases seem to be partly dissociable. These results highlight new important mechanistic constraints that should be incorporated in learning models to jointly explain choice, reaction times and confidence.

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries.

Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, neurobiological interpretations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a hierarchical Bayesian model to single-trial EEG data from healthy human volunteers of either sex who received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207 ms poststimulus), while high-level PEs (about transition probability) are reflected by later components (152-199 and 215-277 ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts the inference on abstract statistical regularities. Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential pathophysiological mechanisms.

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.

Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia

Psychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.

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.

Contextual influence on confidence judgments in human reinforcement learning

The ability to correctly estimate the probability of one’s choices being correct is fundamental to optimally re-evaluate previous choices or to arbitrate between different decision strategies. Experimental evidence nonetheless suggests that this metacognitive process—confidence judgment- is susceptible to numerous biases. Here, we investigate the effect of outcome valence (gains or losses) on confidence while participants learned stimulus-outcome associations by trial-and-error. In two experiments, participants were more confident in their choices when learning to seek gains compared to avoiding losses, despite equal difficulty and performance between those two contexts. Computational modelling revealed that this bias is driven by the context-value, a dynamically updated estimate of the average expected-value of choice options, necessary to explain equal performance in the gain and loss domain. The biasing effect of context-value on confidence, revealed here for the first time in a reinforcement-learning context, is therefore domain-general, with likely important functional consequences. We show that one such consequence emerges in volatile environments, where the (in)flexibility of individuals’ learning strategies differs when outcomes are framed as gains or losses. Despite apparent similar behavior- profound asymmetries might therefore exist between learning to avoid losses and learning to seek gains.

In order to arbitrate between different decision strategies, as well as to inform future choices, a decision maker needs to estimate the probability of her choices being correct as precisely as possible. Surprisingly, this metacognitive operation, known as confidence judgment, has not been systematically investigated in the context of simple instrumental-learning tasks. Here, we assessed how confident individuals are in their choices when learning stimulus-outcome associations by trial-and-errors to maximize gains or to minimize losses. In two experiments, we show that individuals are more confident in their choices when learning to seek gains compared to avoiding losses, despite equal difficulty and performance between those two contexts. To simultaneously account for this pattern of choices and confidence judgments, we propose that individuals learn context-values, which approximate the average expected-value of choice options. We finally show that, in volatile environments, the biasing effect of context-value on confidence induces difference in learning flexibility when outcomes are framed as gains or losses.

Confidence resets reveal hierarchical adaptive learning in humans

Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory.

The psychopathology of NMDAR-antibody encephalitis in adults: a systematic review and phenotypic analysis of individual patient data

BACKGROUND

Early immunotherapy administration improves outcomes in patients with N-methyl-D-aspartate receptor (NMDAR)-antibody encephalitis. As most patients with NMDAR-antibody encephalitis present to psychiatrists, the psychopathology of NMDAR-antibody encephalitis needs to be clearly defined to encourage accurate clinical identification and prompt treatment.

METHODS

For this systematic review, we searched PubMed for all studies published in English between Jan 1, 2005, and Oct 7, 2017, to identify individually reported adult patients (≥18 years) who satisfied consensus criteria for definite NMDAR-antibody encephalitis. After generating a list of 50 fine-grained, lower-level features, we extracted psychopathological data in addition to demographic and aetiological data. The lower-level features were later ordered within higher-level categories. As a means of quality control, we filtered the data according to proxy markers of psychiatric involvement in their description. Subsequently, we compared lower-level features from individual patient data with operationalised psychiatric syndromes using a constrained combination approach and principal component analysis, and did a network analysis to explore the inter-relationships between multiple lower-level features. The review protocol was prospectively registered with PROSPERO, number CRD42017068981.

FINDINGS

Of 1096 records identified in PubMed, 333 satisfied inclusion criteria and described 1100 patients in total with NMDAR-antibody encephalitis. The psychopathology of 505 (46%) patients with reported psychiatric symptoms was described in more detailed terms than only psychiatric or behavioural. 464 (91%) of the 505 patients were from papers in which patient data were reported individually. The remainder of the analyses focused exclusively on these 464 patients. Median age was 27 years (IQR 22-34), 368 (79%) of 464 patients were female and in 147 (32%), NMDAR-antibody encephalitis was associated with ovarian teratoma. The five higher-level categories into which the 464 patients most frequently grouped were behaviour (316 [68%]), psychosis (310 [67%]), mood (219 [47%]), catatonia (137 [30%]), and sleep disturbance (97 [21%]). The overall pattern of lower-level features was statistically stable across subgroups classified by age, sex, pregnancy association, presence of ovarian teratoma, prior herpes simplex virus encephalitis, and isolated psychiatric presentations (two-way ANOVA p=0·6-0·9). Constrained combination and principal component analyses found that mixtures of mood and psychosis syndromes fit each patient better than any single diagnosis alone, particularly for the patients in the psychiatric-described subgroup (mean ΔAkaike information criterion -0·04 in non-psychiatric-described subgroup vs 0·61 in psychiatric-described subgroup). The overlapping nature of the higher-level features was also enriched upon analysis of the psychiatric-described data (221 [67%] of 329 overlaps in non-psychiatric-described subgroup vs 96 [81%] of 118 overlaps in psychiatric-described subgroup, p=0·0052). Network analysis confirmed that the features were closely related and consistent between individual patients; the psychiatric-described subgroup had a markedly high and narrow range of closeness centralities (92% above 0·93 in psychiatric-described subgroup vs 51% above 0·93 in the non-psychiatric group).

INTERPRETATION

The distinctive aspect of NMDAR-antibody encephalitis psychopathology is complexity; core aspects of mood and psychotic disorders consistently coexist within individual patients. Alongside the predominant young female demographic, these psychopathological features could help psychiatrists identify patients who would benefit from cerebrospinal fluid testing and immunotherapies. Well-controlled prospective studies with bespoke inventories are needed to advance this clinically grounded approach.

FUNDING

Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Oxford Health Biomedical Research Centre, British Medical Association Foundation for Medical Research.

Attractor-like Dynamics in Belief Updating in Schizophrenia

Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes "aberrantly salient." A neurobiological explanation for this effect is that diminished synaptic gain (e.g., hypofunction of cortical NMDARs) in Scz destabilizes quasi-stable neuronal network states (or "attractors"). This attractor instability account predicts that (1) Scz would overweight unexpected evidence but underweight consistent evidence, (2) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and (3) these effects would correlate. Hierarchical Bayesian belief updating models were tested in two independent datasets (n = 80 male and n = 167 female) comprising human subjects with Scz, and both clinical and nonclinical controls (some tested when unwell and on recovery) performing the "probability estimates" version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to "disconfirmatory evidence," or a parameter encoding belief instability were formally compared. The "belief instability" model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and nonclinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different from controls once recovered. These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.SIGNIFICANCE STATEMENT Subjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias toward "disconfirmatory" information, but a more mechanistic explanation may be that in Scz, neural firing patterns ("attractor states") are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts.

Two sides of the same coin: Monetary incentives concurrently improve and bias confidence judgments

Decisions are accompanied by a feeling of confidence, that is, a belief about the decision being correct. Confidence accuracy is critical, notably in high-stakes situations such as medical or financial decision-making. We investigated how incentive motivation influences confidence accuracy by combining a perceptual task with a confidence incentivization mechanism. By varying the magnitude and valence (gains or losses) of monetary incentives, we orthogonalized their motivational and affective components. Corroborating theories of rational decision-making and motivation, our results first reveal that the motivational value of incentives improves aspects of confidence accuracy. However, in line with a value-confidence interaction hypothesis, we further show that the affective value of incentives concurrently biases confidence reports, thus degrading confidence accuracy. Finally, we demonstrate that the motivational and affective effects of incentives differentially affect how confidence builds on perceptual evidence. Together, these findings may provide new hints about confidence miscalibration in healthy or pathological contexts.

Attenuation of dopamine-modulated prefrontal value signals underlies probabilistic reward learning deficits in old age

Probabilistic reward learning is characterised by individual differences that become acute in aging. This may be due to age-related dopamine (DA) decline affecting neural processing in striatum, prefrontal cortex, or both. We examined this by administering a probabilistic reward learning task to younger and older adults, and combining computational modelling of behaviour, fMRI and PET measurements of DA D1 availability. We found that anticipatory value signals in ventromedial prefrontal cortex (vmPFC) were attenuated in older adults. The strength of this signal predicted performance beyond age and was modulated by D1 availability in nucleus accumbens. These results uncover that a value-anticipation mechanism in vmPFC declines in aging, and that this mechanism is associated with DA D1 receptor availability.

Glutamatergic regulation of cognition and functional brain connectivity: insights from pharmacological, genetic and translational schizophrenia research

The pharmacological modulation of glutamatergic neurotransmission to improve cognitive function has been a focus of intensive research, particularly in relation to the cognitive deficits seen in schizophrenia. Despite this effort, there has been little success in the clinical use of glutamatergic compounds as procognitive drugs. Here, we review a selection of the drugs used to modulate glutamatergic signalling and how they impact on cognitive function in rodents and humans. We highlight how glutamatergic dysfunction, and NMDA receptor hypofunction in particular, is a key mechanism contributing to the cognitive deficits observed in schizophrenia and outline some of the glutamatergic targets that have been tested as putative procognitive targets for this disorder. Using translational research in this area as a leading exemplar, namely, models of NMDA receptor hypofunction, we discuss how the study of functional brain network connectivity can provide new insight into how the glutamatergic system impacts on cognitive function. Future studies characterizing functional brain network connectivity will increase our understanding of how glutamatergic compounds regulate cognition and could contribute to the future success of glutamatergic drug validation. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc.

Cortical and Striatal Reward Processing in Parkinson's Disease Psychosis

Psychotic symptoms frequently occur in Parkinson's disease (PD), but their pathophysiology is poorly understood. According to the National Institute of Health RDoc programme, the pathophysiological basis of neuropsychiatric symptoms may be better understood in terms of dysfunction of underlying domains of neurocognition in a trans-diagnostic fashion. Abnormal cortico-striatal reward processing has been proposed as a key domain contributing to the pathogenesis of psychotic symptoms in schizophrenia. This theory has received empirical support in the study of schizophrenia spectrum disorders and preclinical models of psychosis, but has not been tested in the psychosis associated with PD. We, therefore, investigated brain responses associated with reward expectation and prediction error signaling during reinforcement learning in PD-associated psychosis. An instrumental learning task with monetary gains and losses was conducted during an fMRI study in PD patients with (n = 12), or without (n = 17), a history of psychotic symptoms, along with a sample of healthy controls (n = 24). We conducted region of interest analyses in the ventral striatum (VS), ventromedial prefrontal and posterior cingulate cortices, and whole-brain analyses. There was reduced activation in PD patients with a history of psychosis, compared to those without, in the posterior cingulate cortex and the VS during reward anticipation (p < 0.05 small volume corrected). The results suggest that cortical and striatal abnormalities in reward processing, a putative pathophysiological mechanism of psychosis in schizophrenia, may also contribute to the pathogenesis of psychotic symptoms in PD. The finding of posterior cingulate dysfunction is in keeping with prior results highlighting cortical dysfunction in the pathogenesis of PD psychosis.

Predictive Processing, Source Monitoring, and Psychosis

A comprehensive understanding of psychosis requires models that link multiple levels of explanation: the neurobiological, the cognitive, the subjective, and the social. Until we can bridge several explanatory gaps, it is difficult to explain how neurobiological perturbations can manifest in bizarre beliefs or hallucinations, or how trauma or social adversity can perturb lower-level brain processes. We propose that the predictive processing framework has much to offer in this respect. We show how this framework may underpin and complement source monitoring theories of delusions and hallucinations and how, when considered in terms of a dynamic and hierarchical system, it may provide a compelling model of several key clinical features of psychosis. We see little conflict between source monitoring theories and predictive coding. The former act as a higher-level description of a set of capacities, and the latter aims to provide a deeper account of how these and other capacities may emerge.

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

Altering the course of schizophrenia: progress and perspectives

Despite a lack of recent progress in the treatment of schizophrenia, our understanding of its genetic and environmental causes has considerably improved, and their relationship to aberrant patterns of neurodevelopment has become clearer. This raises the possibility that 'disease-modifying' strategies could alter the course to - and of - this debilitating disorder, rather than simply alleviating symptoms. A promising window for course-altering intervention is around the time of the first episode of psychosis, especially in young people at risk of transition to schizophrenia. Indeed, studies performed in both individuals at risk of developing schizophrenia and rodent models for schizophrenia suggest that pre-diagnostic pharmacotherapy and psychosocial or cognitive-behavioural interventions can delay or moderate the emergence of psychosis. Of particular interest are 'hybrid' strategies that both relieve presenting symptoms and reduce the risk of transition to schizophrenia or another psychiatric disorder. This Review aims to provide a broad-based consideration of the challenges and opportunities inherent in efforts to alter the course of schizophrenia.

Opposing effects of ketamine and acetyl L-carnitine on the serotonergic system of zebrafish

Ketamine, a pediatric anesthetic, is a noncompetitive N-methyl-D-aspartic acid (NMDA) receptor antagonist. Studies show that ketamine is neurotoxic in developing mammals and zebrafish. In both mammals and zebrafish, acetyl L-carnitine (ALCAR) has been shown to be protective against ketamine toxicity. Ketamine is known to modulate the serotonergic system in mammals. Here, we measured the levels of serotonin (5-HT) and its metabolite, 5-hydroxyindoleacetic acid (5-HIAA) in the embryos exposed to ketamine in the presence and absence of ALCAR. Ketamine, at lower doses, did not produce significant changes in the 5-HT or 5-HIAA levels in 3 dpf (day post-fertilization) embryos. However, 2 mM ketamine (internal embryo exposure levels comparable to human anesthetic plasma concentration) significantly reduced 5-HT level, and 5-HIAA was not detectable indicating that 5-HT metabolism was abolished. In the presence or absence of 2 mM ketamine, ALCAR by itself did not significantly alter 5-HT or 5-HIAA levels compared to the control. Ratios of metabolite/5-HT indicated that 2 mM ketamine inhibited 5-HT metabolism to 5-HIAA whereas lower doses (0.1-0.3 mM) of ketamine did not have any effect. ALCAR reversed the effects of 2 mM ketamine not only by restoring 5-HT and 5-HIAA levels but also 5-HT turnover rate to control levels. Whole mount immunohistochemical studies showed that 2 mM ketamine reduced the serotonergic area in the brain whereas ALCAR expanded it with increased axonal sprouting and branching. These results indicate that ketamine and ALCAR have opposing effects on the zebrafish serotonergic system.