Striatal Dopamine and Reward Prediction Error Signaling in Unmedicated Schizophrenia Patients

Pharmacological Enhancement of Dopamine Neurotransmission Does Not Affect Illusory Pattern Perception

Psychotic symptoms and delusional beliefs have been linked to dopamine transmission in both healthy and clinical samples and are assumed to result at least in part from perceiving illusory patterns in noise. However, the existing literature on the role of dopamine in detecting patterns in noise is inconclusive. To address this issue, we assessed the effect of manipulating dopaminergic neurotransmission on illusory pattern perception in healthy individuals (n = 48, n = 19 female) in a double-blind placebo-controlled within-subjects design (see preregistration at https://osf.io/a4k9j/). We predicted individuals on versus off ʟ-DOPA to be more likely to perceive illusory patterns, specifically objects in images containing only noise. Using a signal detection model, however, we found no credible evidence that ʟ-DOPA compared with placebo increased false alarm rates. Further, ʟ-DOPA did not reliably modulate measures of accuracy, discrimination sensitivity, and response bias. In all cases, Bayesian statistics revealed strong evidence in favor of the null hypothesis. The task design followed previous work on illusory pattern perception and comprised a limited number of items per condition. The results therefore need to be interpreted with caution, as power was limited. Future studies should address illusory pattern perception using more items and take into account potential dose-dependent effects and differential effects in healthy versus clinical samples.

Striatal Functional Hypoconnectivity in Patients With Schizophrenia Suffering From Negative Symptoms, Longitudinal Findings

BACKGROUND

Negative symptoms in schizophrenia (SZ), such as apathy and diminished expression, have limited treatments and significantly impact daily life. Our study focuses on the functional division of the striatum: limbic-motivation and reward, associative-cognition, and sensorimotor-sensory and motor processing, aiming to identify potential biomarkers for negative symptoms.

STUDY DESIGN

This longitudinal, 2-center resting-state-fMRI (rsfMRI) study examines striatal seeds-to-whole-brain functional connectivity. We examined connectivity aberrations in patients with schizophrenia (PwSZ), focusing on stable group differences across 2-time points using intra-class-correlation and associated these with negative symptoms and measures of cognition. Additionally, in PwSZ, we used negative symptoms to predict striatal connectivity aberrations at the baseline and used the striatal aberration to predict symptoms 9 months later.

STUDY RESULTS

A total of 143 participants (77 PwSZ, 66 controls) from 2 centers (Berlin/Geneva) participated. We found sensorimotor-striatum and associative-striatum hypoconnectivity. We identified 4 stable hypoconnectivity findings over 3 months, revealing striatal-fronto-parietal-cerebellar hypoconnectivity in PwSZ. From those findings, we found hypoconnectivity in the bilateral associative striatum with the bilateral paracingulate-gyrus and the anterior cingulate cortex in PwSZ. Additionally, hypoconnectivity between the associative striatum and the superior frontal gyrus was associated with lower cognition scores in PwSZ, and weaker sensorimotor striatum connectivity with the superior parietal lobule correlated negatively with diminished expression and could predict symptom severity 9 months later.

CONCLUSIONS

Importantly, patterns of weaker sensorimotor striatum and superior parietal lobule connectivity fulfilled the biomarker criteria: clinical significance, reflecting underlying pathophysiology, and stability across time and centers.

Neural correlates of prediction error in patients with schizophrenia: evidence from an fMRI meta-analysis

Abnormal processes of learning from prediction errors, i.e. the discrepancies between expectations and outcomes, are thought to underlie motivational impairments in schizophrenia. Although dopaminergic abnormalities in the mesocorticolimbic reward circuit have been found in patients with schizophrenia, the pathway through which prediction error signals are processed in schizophrenia has yet to be elucidated. To determine the neural correlates of prediction error processing in schizophrenia, we conducted a meta-analysis of whole-brain neuroimaging studies that investigated prediction error signal processing in schizophrenia patients and healthy controls. A total of 14 studies (324 schizophrenia patients and 348 healthy controls) using the reinforcement learning paradigm were included. Our meta-analysis showed that, relative to healthy controls, schizophrenia patients showed increased activity in the precentral gyrus and middle frontal gyrus and reduced activity in the mesolimbic circuit, including the striatum, thalamus, amygdala, hippocampus, anterior cingulate cortex, insula, superior temporal gyrus, and cerebellum, when processing prediction errors. We also found hyperactivity in frontal areas and hypoactivity in mesolimbic areas when encoding prediction error signals in schizophrenia patients, potentially indicating abnormal dopamine signaling of reward prediction error and suggesting failure to represent the value of alternative responses during prediction error learning and decision making.

Resolving the Delusion Paradox

BACKGROUND AND HYPOTHESIS

The neurocomputational framework of predictive processing (PP) provides a promising approach to explaining delusions, a key symptom of psychotic disorders. According to PP, the brain makes inferences about the world by weighing prior beliefs against the available sensory data. Mismatches between prior beliefs and sensory data result in prediction errors that may update the brain's model of the world. Psychosis has been associated with reduced weighting of priors relative to the sensory data. However, delusional beliefs are highly resistant to change, suggesting increased rather than decreased weighting of priors. We propose that this "delusion paradox" can be resolved within a hierarchical PP model: Reduced weighting of prior beliefs at low hierarchical levels may be compensated by an increased influence of higher-order beliefs represented at high hierarchical levels, including delusional beliefs. This may sculpt perceptual processing into conformity with delusions and foster their resistance to contradictory evidence.

STUDY DESIGN

We review several lines of experimental evidence on low- and high-level processes, and their neurocognitive underpinnings in delusion-related phenotypes and link them to predicted processing.

STUDY RESULTS

The reviewed evidence supports the notion of decreased weighting of low-level priors and increased weighting of high-level priors, in both delusional and delusion-prone individuals. Moreover, we highlight the role of prefrontal cortex as a neural basis for the increased weighting of high-level prior beliefs and discuss possible clinical implications of the proposed hierarchical predictive-processing model.

CONCLUSIONS

Our review suggests the delusion paradox can be resolved within a hierarchical PP model.

Do the negative symptoms of schizophrenia reflect reduced responsiveness to reward? Examination using a reward prediction error (RPE) task

BACKGROUND

A leading theory of the negative symptoms of schizophrenia is that they reflect reduced responsiveness to rewarding stimuli. This proposal has been linked to abnormal (reduced) dopamine function in the disorder, because phasic release of dopamine is known to code for reward prediction error (RPE). Nevertheless, few functional imaging studies have examined if patients with negative symptoms show reduced RPE-associated activations.

METHODS

Matched groups of DSM-5 schizophrenia patients with high negative symptom scores (HNS, N = 27) or absent negative symptoms (ANS, N = 27) and healthy controls (HC, N = 30) underwent fMRI scanning while they performed a probabilistic monetary reward task designed to generate a measure of RPE.

RESULTS

In the HC, whole-brain analysis revealed that RPE was positively associated with activation in the ventral striatum, the putamen, and areas of the lateral prefrontal cortex and orbitofrontal cortex, among other regions. Group comparison revealed no activation differences between the healthy controls and the ANS patients. However, compared to the ANS patients, the HNS patients showed regions of significantly reduced activation in the left ventrolateral and dorsolateral prefrontal cortex, and in the right lingual and fusiform gyrus. HNS and ANS patients showed no activation differences in ventral striatal or midbrain regions-of-interest (ROIs), but the HNS patients showed reduced activation in a left orbitofrontal cortex ROI.

CONCLUSIONS

The findings do not suggest that a generalized reduction of RPE signalling underlies negative symptoms. Instead, they point to a more circumscribed dysfunction in the lateral frontal and possibly the orbitofrontal cortex.

Reward prediction error in learning-related behaviors

Learning is a complex process, during which our opinions and decisions are easily changed due to unexpected information. But the neural mechanism underlying revision and correction during the learning process remains unclear. For decades, prediction error has been regarded as the core of changes to perception in learning, even driving the learning progress. In this article, we reviewed the concept of reward prediction error, and the encoding mechanism of dopaminergic neurons and the related neural circuities. We also discussed the relationship between reward prediction error and learning-related behaviors, including reversal learning. We then demonstrated the evidence of reward prediction error signals in several neurological diseases, including Parkinson's disease and addiction. These observations may help to better understand the regulatory mechanism of reward prediction error in learning-related behaviors.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model

Background

Disturbances in presynaptic dopamine activity and levels of GABA (gamma-aminobutyric acid) and glutamate plus glutamine collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve patients with first-episode psychosis from healthy control subjects.

Methods

We included 23 patients (mean age 22.3 years, 9 male) and 20 control subjects (mean age 22.4 years, 8 male). We determined dopamine metabolism in the nucleus accumbens and striatum from 18F-fluorodopa (18F-FDOPA) positron emission tomography. We measured GABA levels in the anterior cingulate cortex (ACC) and glutamate plus glutamine levels in the ACC and left thalamus with 3T proton magnetic resonance spectroscopy. We used binominal logistic regression for unimodal prediction when we modeled neurotransmitters individually and for multimodal prediction when we combined the 3 neurotransmitters. We selected the best combination based on Akaike information criterion.

Results

Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p = .003) and included dopamine synthesis capacity (Ki4p) in the nucleus accumbens (p = .664), GABA levels in the ACC (p = .019), glutamate plus glutamine levels in the thalamus (p = .678), and the interaction term Ki4p × GABA (p = .016).

Conclusions

Our multimodal approach proved superior classification accuracy, implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between Ki4p in the nucleus accumbens and GABA values in the ACC appeared to contribute diagnostic information.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Acute stress alters probabilistic reversal learning in healthy male adults

Behavioural adaptation is a fundamental cognitive ability, ensuring survival by allowing for flexible adjustment to changing environments. In laboratory settings, behavioural adaptation can be measured with reversal learning paradigms requiring agents to adjust reward learning to stimulus-action-outcome contingency changes. Stress is found to alter flexibility of reward learning, but effect directionality is mixed across studies. Here, we used model-based functional MRI (fMRI) in a within-subjects design to investigate the effect of acute psychosocial stress on flexible behavioural adaptation. Healthy male volunteers (n = 28) did a reversal learning task during fMRI in two sessions, once after the Trier Social Stress Test (TSST), a validated psychosocial stress induction method, and once after a control condition. Stress effects on choice behaviour were investigated using multilevel generalized linear models and computational models describing different learning processes that potentially generated the data. Computational models were fitted using a hierarchical Bayesian approach, and model-derived reward prediction errors (RPE) were used as fMRI regressors. We found that acute psychosocial stress slightly increased correct response rates. Model comparison revealed that double-update learning with altered choice temperature under stress best explained the observed behaviour. In the brain, model-derived RPEs were correlated with BOLD signals in striatum and ventromedial prefrontal cortex (vmPFC). Striatal RPE signals for win trials were stronger during stress compared with the control condition. Our study suggests that acute psychosocial stress could enhance reversal learning and RPE brain responses in healthy male participants and provides a starting point to explore these effects further in a more diverse population.

MicroRNA schizophrenia: Etiology, biomarkers and therapeutic targets

The three sets of symptoms associated with schizophrenia-positive, negative, and cognitive-are burdensome and have serious effects on public health, which affects up to 1% of the population. It is now commonly believed that in addition to the traditional dopaminergic mesolimbic pathway, the etiology of schizophrenia also includes neuronal networks, such as glutamate, GABA, serotonin, BDNF, oxidative stress, inflammation and the immune system. Small noncoding RNA molecules called microRNAs (miRNAs) have come to light as possible participants in the pathophysiology of schizophrenia in recent years by having an impact on these systems. These small RNAs regulate the stability and translation of hundreds of target transcripts, which has an impact on the entire gene network. There may be improved approaches to treat and diagnose schizophrenia if it is understood how these changes in miRNAs alter the critical related signaling pathways that drive the development and progression of the illness.

Nanosystems for Brain Targeting of Antipsychotic Drugs: An Update on the Most Promising Nanocarriers for Increased Bioavailability and Therapeutic Efficacy

Orally administered antipsychotic drugs are the first-line treatment for psychotic disorders, such as schizophrenia and bipolar disorder. Nevertheless, adverse drug reactions jeopardize clinical outcomes, resulting in patient non-compliance. The design formulation strategies for enhancing brain drug delivery has been a major challenge, mainly due to the restrictive properties of the blood-brain barrier. However, recent pharmacokinetic and pharmacodynamic in vivo assays confirmed the advantage of the intranasal route when compared to oral and intravenous administration, as it allows direct nose-to-brain drug transport via neuronal pathways, reducing systemic side effects and maximizing therapeutic outcomes. In addition, the incorporation of antipsychotic drugs into nanosystems such as polymeric nanoparticles, polymeric mixed micelles, solid lipid nanoparticles, nanostructured lipid carriers, nanoemulsions, nanoemulgels, nanosuspensions, niosomes and spanlastics, has proven to be quite promising. The developed nanosystems, having a small and homogeneous particle size (ideal for nose-to-brain delivery), high encapsulation efficiency and good stability, resulted in improved brain bioavailability and therapeutic-like effects in animal models. Hence, although it is essential to continue research in this field, the intranasal delivery of nanosystems for the treatment of schizophrenia, bipolar disorder and other related disorders has proven to be quite promising, opening a path for future therapies with higher efficacy.

Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia

Negative symptoms, such as lack of motivation or social withdrawal, are highly prevalent and debilitating in patients with schizophrenia. Underlying mechanisms of negative symptoms are incompletely understood, thereby preventing the development of targeted treatments. We hypothesized that in patients with schizophrenia during psychotic remission, impaired influences of both model-based and model-free reward predictions on decision-making ('reward prediction influence', RPI) underlie negative symptoms. We focused on psychotic remission, because psychotic symptoms might confound reward-based decision-making. Moreover, we hypothesized that impaired model-based/model-free RPIs depend on alterations of both associative striatum dopamine synthesis and storage (DSS) and executive functioning. Both factors influence RPI in healthy subjects and are typically impaired in schizophrenia. Twenty-five patients with schizophrenia with pronounced negative symptoms during psychotic remission and 24 healthy controls were included in the study. Negative symptom severity was measured by the Positive and Negative Syndrome Scale negative subscale, model-based/model-free RPI by the two-stage decision task, associative striatum DSS by 18F-DOPA positron emission tomography and executive functioning by the symbol coding task. Model-free RPI was selectively reduced in patients and associated with negative symptom severity as well as with reduced associative striatum DSS (in patients only) and executive functions (both in patients and controls). In contrast, model-based RPI was not altered in patients. Results provide evidence for impaired model-free reward prediction influence as a mechanism for negative symptoms in schizophrenia as well as for reduced associative striatum dopamine and executive dysfunction as relevant factors. Data suggest potential treatment targets for patients with schizophrenia and pronounced negative symptoms.

Sex and pubertal influences on the neurodevelopmental underpinnings of schizophrenia: A case for longitudinal research on adolescents

Sex is a significant source of heterogeneity in schizophrenia, with more negative symptoms in males and more affective symptoms and internalizing comorbidity in females. In this narrative review, we argue that there are likely sex differences in the pathophysiological mechanisms of schizophrenia-spectrum disorders (SZ) that originate during puberty and relate to the sex-specific impacts of pubertal maturation on brain development. Pubertal maturation might also trigger underlying (genetic or other) vulnerabilities in at-risk individuals, influencing brain development trajectories that contribute to the emergence of SZ. This review is the first to integrate links between pubertal development and neural development with cognitive neuroscience research in SZ to form and evaluate these hypotheses, with a focus on the frontal-striatal and frontal-limbic networks and their hypothesized contribution to negative and mood symptoms respectively. To test these hypotheses, longitudinal research with human adolescents is needed that examines the role of sex and pubertal development using large cohorts or high risk samples. We provide recommendations for such studies, which will integrate the fields of psychiatry, developmental cognitive neuroscience, and developmental endocrinology towards a more nuanced understanding of the role of pubertal factors in the hypothesized sex-specific pathophysiological mechanisms of schizophrenia.

Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification

The ability to appropriately update the value of a given action is a critical component of flexible decision making. Several psychiatric disorders, including schizophrenia, are associated with impairments in flexible decision making that can be evaluated using the probabilistic reversal learning (PRL) task. The PRL task has been reverse-translated for use in rodents. Disrupting glutamate neurotransmission during early postnatal neurodevelopment in rodents has induced behavioral, cognitive, and neuropathophysiological abnormalities relevant to schizophrenia. Here, we tested the hypothesis that using the NMDA receptor antagonist phencyclidine (PCP) to disrupt postnatal glutamatergic transmission in rats would lead to impaired decision making in the PRL. Consistent with this hypothesis, compared to controls the postnatal PCP-treated rats completed fewer reversals and exhibited disruptions in reward and punishment sensitivity (i.e., win-stay and lose-shift responding, respectively). Moreover, computational analysis of behavior revealed that postnatal PCP-treatment resulted in a pronounced impairment in the learning rate throughout PRL testing. Finally, a deep neural network (DNN) trained on the rodent behavior could accurately predict the treatment group of subjects. These data demonstrate that disrupting early postnatal glutamatergic neurotransmission impairs flexible decision making and provides evidence that DNNs can be trained on behavioral datasets to accurately predict the treatment group of new subjects, highlighting the potential for DNNs to aid in the diagnosis of schizophrenia.

Sufficient reliability of the behavioral and computational readouts of a probabilistic reversal learning task

Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it imposes an upper bound to potential correlations with other variables (e.g., symptom or brain data). However, the reliability of many task readouts is low. In this study, we scrutinized the retest reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from N = 40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data were partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We found good to excellent reliability for behavioral indices as derived from mixed-effects models that included data from both sessions. The internal consistency was good to excellent. For indices derived from computational modeling, we found excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences in cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modeling of the longitudinal data (whether sessions are modeled separately or jointly), on estimation methods, and on the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools.

Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia

Brain network models of cognitive control are central to advancing our understanding of psychopathology and cognitive dysfunction in schizophrenia. This review examines the role of large-scale brain organization in schizophrenia, with a particular focus on a triple-network model of cognitive control and its role in aberrant salience processing. First, we provide an overview of the triple network involving the salience, frontoparietal, and default mode networks and highlight the central role of the insula-anchored salience network in the aberrant mapping of salient external and internal events in schizophrenia. We summarize the extensive evidence that has emerged from structural, neurochemical, and functional brain imaging studies for aberrancies in these networks and their dynamic temporal interactions in schizophrenia. Next, we consider the hypothesis that atypical striatal dopamine release results in misattribution of salience to irrelevant external stimuli and self-referential mental events. We propose an integrated triple-network salience-based model incorporating striatal dysfunction and sensitivity to perceptual and cognitive prediction errors in the insula node of the salience network and postulate that dysregulated dopamine modulation of salience network-centered processes contributes to the core clinical phenotype of schizophrenia. Thus, a powerful paradigm to characterize the neurobiology of schizophrenia emerges when we combine conceptual models of salience with large-scale cognitive control networks in a unified manner. We conclude by discussing potential therapeutic leads on restoring brain network dysfunction in schizophrenia.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

Striatal dopamine synthesis capacity and its association with negative symptoms upon resolution of positive symptoms in first-episode schizophrenia and delusional disorder

RATIONALE

How striatal dopamine synthesis capacity (DSC) contributes to the pathogenesis of negative symptoms in first-episode schizophrenia (SZ) and delusional disorder (DD) has seldom been explored. As negative symptoms during active psychotic episodes can be complicated by secondary influences, such as positive symptoms, longitudinal investigations may help to clarify the relationship between striatal DSC and negative symptoms and differentiate between primary and secondary negative symptoms.

OBJECTIVE

A longitudinal study was conducted to examine whether baseline striatal DSC would be related to negative symptoms at 3 months in first-episode SZ and DD patients.

METHODS

Twenty-three first-episode age- and gender-matched patients (11 DD and 12 SZ) were consecutively recruited through an early intervention service for psychosis in Hong Kong. Among them, 19 (82.6%) patients (9 DD and 10 SZ) were followed up at 3 months. All patients received an ¹⁸F-DOPA PET/MR scan at baseline.

RESULTS

Baseline striatal DSC (K^occ;30-60) was inversely associated with negative symptoms at 3 months in first-episode SZ patients (r_s =  - 0.80, p = 0.010). This association remained in SZ patients even when controlling for baseline negative, positive, and depressive symptoms, as well as cumulative antipsychotic dosage (β =  - 0.69, p = 0.012). Such associations were not observed in first-episode DD patients. Meanwhile, the severity of negative symptoms at 3 months was associated with more positive symptoms in DD patients (r_s = 0.74, p = 0.010) and not in SZ patients.

CONCLUSIONS

These findings highlight the role of striatal DSC in negative symptoms upon resolution of active psychotic episodes among first-episode SZ patients. Baseline striatal dopamine activity may inform future symptom expression with important treatment implications.

Postdiction in Visual Awareness in Schizophrenia

Background: The mistiming of predictive thought and real perception leads to postdiction in awareness. Individuals with high delusive thinking confuse prediction and perception, which results in impaired reality testing. The present observational study investigated how antipsychotic medications and cognitive-behavioral therapy (CBT) modulate postdiction in schizophrenia. We hypothesized that treatment reduces postdiction, especially when antipsychotics and CBT are combined. Methods: We enrolled patients with schizophrenia treated in a natural clinical setting and not in a randomized controlled trial. We followed up two schizophrenia groups matched for age, sex, education, and illness duration: patients on antipsychotics (n = 25) or antipsychotics plus CBT (n = 25). The treating clinician assigned the patients to the two groups. Participants completed a postdiction and a temporal discrimination task at weeks 0 and 12. Results: At week 0, postdiction was enhanced in patients relative to controls at a short prediction–perception time interval, which correlated with PANSS positive symptoms and delusional conviction. At week 12, postdiction was reduced in schizophrenia, especially when they received antipsychotics plus CBT. Patients with schizophrenia were also impaired on the temporal discrimination task, which did not change during the treatment. During the 12-week observational period, all PANSS scores were significantly reduced in both clinical groups, but the positive symptoms and emotional distress exhibited a more pronounced response in the antipsychotics plus CBT group. Conclusion: Perceptual postdiction is a putative neurocognitive marker of delusive thinking. Combined treatment with antipsychotics and CBT significantly ameliorates abnormally elevated postdiction in schizophrenia.

The prediction-error hypothesis of schizophrenia: new data point to circuit-specific changes in dopamine activity

Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide. People with schizophrenia suffer from symptoms including psychosis and delusions, apathy, anhedonia, and cognitive deficits. Strikingly, schizophrenia is characterised by a learning paradox involving difficulties learning from rewarding events, whilst simultaneously 'overlearning' about irrelevant or neutral information. While dysfunction in dopaminergic signalling has long been linked to the pathophysiology of schizophrenia, a cohesive framework that accounts for this learning paradox remains elusive. Recently, there has been an explosion of new research investigating how dopamine contributes to reinforcement learning, which illustrates that midbrain dopamine contributes in complex ways to reinforcement learning, not previously envisioned. This new data brings new possibilities for how dopamine signalling contributes to the symptomatology of schizophrenia. Building on recent work, we present a new neural framework for how we might envision specific dopamine circuits contributing to this learning paradox in schizophrenia in the context of models of reinforcement learning. Further, we discuss avenues of preclinical research with the use of cutting-edge neuroscience techniques where aspects of this model may be tested. Ultimately, it is hoped that this review will spur to action more research utilising specific reinforcement learning paradigms in preclinical models of schizophrenia, to reconcile seemingly disparate symptomatology and develop more efficient therapeutics.

Schizophrenia

Schizophrenia, characterised by psychotic symptoms and in many cases social and occupational decline, remains an aetiological and therapeutic challenge. Contrary to popular belief, the disorder is modestly more common in men than in women. Nor is the outcome uniformly poor. A division of symptoms into positive, negative, and disorganisation syndromes is supported by factor analysis. Catatonic symptoms are not specific to schizophrenia and so-called first rank symptoms are no longer considered diagnostically important. Cognitive impairment is now recognised as a further clinical feature of the disorder. Lateral ventricular enlargement and brain volume reductions of around 2% are established findings. Brain functional changes occur in different subregions of the frontal cortex and might ultimately be understandable in terms of disturbed interaction among large-scale brain networks. Neurochemical disturbance, involving dopamine function and glutamatergic N-methyl-D-aspartate receptor function, is supported by indirect and direct evidence. The genetic contribution to schizophrenia is now recognised to be largely polygenic. Birth and early life factors also have an important aetiological role. The mainstay of treatment remains dopamine receptor-blocking drugs; a psychological intervention, cognitive behavioural therapy, has relatively small effects on symptoms. The idea that schizophrenia is better regarded as the extreme end of a continuum of psychotic symptoms is currently influential. Other areas of debate include cannabis and childhood adversity as causative factors, whether there is progressive brain change after onset, and the long-term success of early intervention initiatives.

Dopaminergic Activity in Antipsychotic-Naïve Patients Assessed With Positron Emission Tomography Before and After Partial Dopamine D2 Receptor Agonist Treatment: Association With Psychotic Symptoms and Treatment Response

BACKGROUND

Dopamine activity has been associated with the response to antipsychotic treatment. Our study used a four-parameter model to test the association between the striatal decarboxylation rate of ¹⁸F-DOPA to ¹⁸F-dopamine (k₃) and the effect of treatment on psychotic symptoms in antipsychotic-naïve patients with first-episode psychosis. We further explored the effect of treatment with a partial dopamine D₂ receptor agonist (aripiprazole) on k₃ and dopamine synthesis capacity (DSC) determined by the four-parameter model and by the conventional tissue reference method.

METHODS

Sixty-two individuals (31 patients and 31 control subjects) underwent ¹⁸F-DOPA positron emission tomography at baseline, and 15 patients were re-examined after 6 weeks. Clinical re-examinations were completed after 6 weeks (n = 28) and 6 months (n = 15). Symptoms were evaluated with the Positive and Negative Syndrome Scale.

RESULTS

High baseline decarboxylation rates (k₃) were associated with more positive symptoms at baseline (p < .001) and with symptom improvement after 6 weeks (p = .006). Subregion analyses showed that baseline k₃ for the putamen (p = .003) and nucleus accumbens (p = .013) and DSC values for the nucleus accumbens (p = .003) were associated with psychotic symptoms. The tissue reference method yielded no associations between DSC and symptoms or symptom improvement. Neither method revealed any effects of group or treatment on average magnitudes of k₃ or DSC, whereas changes in dopamine synthesis were correlated with higher baseline values, implying a potential effect of treatment.

CONCLUSIONS

Striatal decarboxylation rate at baseline was associated with psychotic symptoms and treatment response. The strong association between k₃ and treatment effect potentially implicate on new treatment strategies.

Target selection for deep brain stimulation in treatment resistant schizophrenia

The use of deep brain stimulation (DBS) in treatment resistant patients with schizophrenia is of considerable current interest, but where to site the electrodes is challenging. This article reviews rationales for electrode placement in schizophrenia based on evidence for localized brain abnormality in the disorder and the targets that have been proposed and employed to date. The nucleus accumbens and the subgenual anterior cingulate cortex are of interest on the grounds that they are sites of potential pathologically increased brain activity in schizophrenia and so susceptible to the local inhibitory effects of DBS; both sites have been employed in trials of DBS in schizophrenia. Based on other lines of reasoning, the ventral tegmental area, the substantia nigra pars reticulata and the habenula have also been proposed and in some cases employed. The dorsolateral prefrontal cortex has not been suggested, probably reflecting evidence that it is underactive rather than overactive in schizophrenia. The hippocampus is also of theoretical interest but there is no clear functional imaging evidence that it shows overactivity in schizophrenia. On current evidence, the nucleus accumbens may represent the strongest candidate for DBS electrode placement in schizophrenia, with the substantia nigra pars reticulata also showing promise in a single case report; the ventral tegmental area is also of potential interest, though it remains untried.

Pleasure, Reward Value, Prediction Error and Anhedonia

In order to develop effective treatments for anhedonia we need to understand its underlying neurobiological mechanisms. Anhedonia is conceptually strongly linked to reward processing, which involves a variety of cognitive and neural operations. This chapter reviews the evidence for impairments in experiencing hedonic response (pleasure), reward valuation and reward learning based on outcomes (commonly conceptualised in terms of "reward prediction error"). Synthesising behavioural and neuroimaging findings, we examine case-control studies of patients with depression and schizophrenia, including those focusing specifically on anhedonia. Overall, there is reliable evidence that depression and schizophrenia are associated with disrupted reward processing. In contrast to the historical definition of anhedonia, there is surprisingly limited evidence for impairment in the ability to experience pleasure in depression and schizophrenia. There is some evidence that learning about reward and reward prediction error signals are impaired in depression and schizophrenia, but the literature is inconsistent. The strongest evidence is for impairments in the representation of reward value and how this is used to guide action. Future studies would benefit from focusing on impairments in reward processing specifically in anhedonic samples, including transdiagnostically, and from using designs separating different components of reward processing, formulating them in computational terms, and moving beyond cross-sectional designs to provide an assessment of causality.

Neural Circuitry of Salience and Reward Processing in Psychosis

The processing of salient and rewarding stimuli is integral to engaging our attention, stimulating anticipation for future events, and driving goal-directed behaviors. Widespread impairments in these processes are observed in psychosis, which may be associated with worse functional outcomes or mechanistically linked to the development of symptoms. Here, we summarize the current knowledge of behavioral and functional neuroimaging in salience, prediction error, and reward. Although each is a specific process, they are situated in multiple feedback and feedforward systems integral to decision making and cognition more generally. We argue that the origin of salience and reward processing dysfunctions may be centered in the subcortex during the earliest stages of psychosis, with cortical abnormalities being initially more spared but becoming more prominent in established psychotic illness/schizophrenia. The neural circuits underpinning salience and reward processing may provide targets for delaying or preventing progressive behavioral and neurobiological decline.

Neurochemistry of Visual Attention

Visual attention is the cognitive process that mediates the selection of important information from the environment. This selection is usually controlled by bottom-up and top-down attentional biasing. Since for most humans vision is the dominant sense, visual attention is critically important for higher-order cognitive functions and related deficits are a core symptom of many neuropsychiatric and neurological disorders. Here, we summarize the importance and relative contributions of different neuromodulators and neurotransmitters to the neural mechanisms of top-down and bottom-up attentional control. We will not only review the roles of widely accepted neuromodulators, such as acetylcholine, dopamine and noradrenaline, but also the contributions of other modulatory substances. In doing so, we hope to shed some light on the current understanding of the role of neurochemistry in shaping neuron properties contributing to the allocation of attention in the visual field.

Neuromelanin-Sensitive Magnetic Resonance Imaging in Schizophrenia: A Meta-Analysis of Case-Control Studies

Background: Psychiatry is in urgent need of reliable biomarkers. Novel neuromelanin-sensitive magnetic resonance imaging (NM-MRI) sequences provide a time-efficient and non-invasive way to investigate the human brain in-vivo. This gives insight into the metabolites of dopaminergic signaling and may provide further evidence for potential dopaminergic alterations in patients with schizophrenia (SCZ). The present systematic review provides a meta-analysis of case-control studies using neuromelanin-sensitive sequences in SCZ vs. healthy controls (HC). Methods: According to predefined search terms and inclusion criteria studies were extracted on PubMed. Meta-analyses with a fixed and random-effects model with inverse variance method, DerSimonian-Laird estimator for τ², and Cohen's d were calculated. Bias was assessed using funnel plots. The primary study outcome was contrast-to-noise ratio (CNR) in the substantia nigra compared between HC and SCZ. Results: The total sample of k = 6 studies included n = 183 cases and n = 162 controls. Across all studies we found a significant elevation of CNR in the substantia nigra (d = 0.42 [0.187; 0.655], z = 3.521, p < 0.001) in cases compared to controls. We found no significant difference in the control region of locus coeruleus (d = -0.07 [-0.446; 0.302], z = -0.192, p = 0.847), with CNR for the latter only reported in k = 3 studies. Conclusion: CNR in the substantia nigra were significantly elevated in cases compared to controls. Our results support neuromelanin as a candidate biomarker for dopaminergic dysfunction in schizophrenia. Further studies need to assess this candidate marker in large, longitudinal cohorts and address potential effects of disease state, medication and correlations with symptoms.

Molecular imaging of schizophrenia: Neurochemical findings in a heterogeneous and evolving disorder

The past four decades have seen enormous efforts placed on a search for molecular markers of schizophrenia using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In this narrative review, we cast a broad net to define and summarize what researchers have learned about schizophrenia from molecular imaging studies. Some PET studies of brain energy metabolism with the glucose analogue FDGhave have shown a hypofrontality defect in patients with schizophrenia, but more generally indicate a loss of metabolic coherence between different brain regions. An early finding of significantly increased striatal trapping of the dopamine synthesis tracer FDOPA has survived a meta-analysis of many replications, but the increase is not pathognomonic of the disorder, since one half of patients have entirely normal dopamine synthesis capacity. Similarly, competition SPECT studies show greater basal and amphetamine-evoked dopamine occupancy at post-synaptic dopamine D_2/3 receptors in patients with schizophrenia, but the difference is likewise not pathognomonic. We thus propose that molecular imaging studies of brain dopamine indicate neurochemical heterogeneity within the diagnostic entity of schizophrenia. Occupancy studies have established the relevant target engagement by antipsychotic medications at dopamine D_2/3 receptors in living brain. There is evidence for elevated frontal cortical dopamine D₁ receptors, especially in relation to cognitive deficits in schizophrenia. There is a general lack of consistent findings of abnormalities in serotonin markers, but some evidence for decreased levels of nicotinic receptors in patients. There are sparse and somewhat inconsistent findings of reduced binding of muscarinic, glutamate, and opioid receptors ligands, inconsistent findings of microglial activation, and very recently, evidence of globally reduced levels of synaptic proteins in brain of patients. One study reports a decline in histone acetylase binding that is confined to the dorsolateral prefrontal cortex. In most contexts, the phase of the disease and effects of past or present medication can obscure or confound PET and SPECT findings in schizophrenia.