Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence

Developing algorithmic psychiatry via multi-level spanning computational models

Modern psychiatry faces challenges in translating neurobiological insights into treatments for severe illnesses. The mid-20th century witnessed the rise of molecular mechanisms as pathophysiological and treatment models, with recent holistic proposals keeping this focus unaltered. In this perspective, we explore how psychiatry can utilize systems neuroscience to develop a vertically integrated understanding of brain function to inform treatment. Using schizophrenia as a case study, we discuss scale-related challenges faced by researchers studying molecules, circuits, networks, and cognition and clinicians operating within existing frameworks. We emphasize computation as a bridging language, with algorithmic models like hierarchical predictive processing offering explanatory potential for targeted interventions. Developing such models will not only facilitate new interventions but also optimize combining existing treatments by predicting their multi-level effects. We conclude with the prognosis that the future is bright, but that continued investment in research closely driven by clinical realities will be critical.

Exploratory analysis of the relationship between striatal connectivity and apathy during phosphodiesterase 10 inhibition in schizophrenia: findings from a randomized crossover trial

BACKGROUND

Negative symptoms in schizophrenia remain a challenge with limited therapeutic strategies. The novel compound RG7203 promotes reward learning via dopamine D1-dependent signaling and therefore holds promise, especially to improve the apathy dimension of negative symptoms. When tested as add-on to antipsychotic medication, apathy did not change significantly with RG7203 versus placebo. However, the response varied across patients, and a subset showed clinically relevant improvement of apathy. It remains unclear if these interindividual differences are related to neurobiological correlates.

METHODS

Due to the predominant binding of RG7203 in the striatum, we investigated how apathy changes with RG7203 are related to changes in cortico-striatal connectivity by computing rank correlations (rs). In a post hoc exploratory analysis, we focused on cortico-striatal circuits that have been associated with apathy and previously showed connectivity alterations in schizophrenia. In a double-blind, 3-way randomized and counterbalanced crossover study, resting-state functional magnetic resonance imaging was acquired from 24 individuals with schizophrenia following a 3-week administration of placebo, 5 mg, or 15 mg of RG7203 as an add-on to antipsychotics.

RESULTS

We found that 5 mg or 15 mg of RG7203 did not lead to significant changes in striatal connectivity. However, changes in the apathy response across individuals were reflected by striatal connectivity changes. Apathy improvement with 5 mg and 15 mg RG7203 vs. placebo was associated with increased striatal connectivity to paracingulate (rs = - 0.58, p = 0.047 for both doses) and anterior cingulate regions (rs = - 0.56, p = 0.047 for both doses). Such associations were not observed for the negative symptom dimension of expressive deficits. We additionally observed that lower striatal connectivity to paracingulate and anterior cingulate regions during placebo was linked to greater apathy improvement during RG7203 treatment at both doses (rs = 0.61-0.79 and p = 0.0002-0.02 across regions and doses).

CONCLUSIONS

These findings suggest that striatal connectivity with the paracingulate gyrus and anterior cingulate cortex may be associated with apathy modulation under RG7203 treatment. Replication and further elaboration of these findings in larger clinical studies could help to advance biologically informed and personalized treatment options for negative symptoms.

TRIAL REGISTRATION

NCT02824055, registered on ClinicalTrials.gov (2016-06-21).

Evidence for shallow cognitive maps in Schizophrenia

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could arise when goal-directed control and motivation are intact but used to plan over ill-formed maps. We tested the hypothesis that individuals with SZ are impaired in constructing cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that individuals with SZ were able to display similar performance on this task compared with controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

Probabalistic reinforcement learning impairments predict negative symptom severity and risk for conversion in youth at clinical high-risk for psychosis

BACKGROUND

Elucidation of transphasic mechanisms (i.e., mechanisms that occur across illness phases) underlying negative symptoms could inform early intervention and prevention efforts and additionally identify treatment targets that could be effective regardless of illness stage. This study examined whether a key reinforcement learning behavioral pattern characterized by reduced difficulty learning from rewards that have been found to underlie negative symptoms in those with a schizophrenia diagnosis also contributes to negative symptoms in those at clinical high-risk (CHR) for psychosis.

METHODS

CHR youth (n = 46) and 51 healthy controls (CN) completed an explicit reinforcement learning task with two phases. During the acquisition phase, participants learned to select between pairs of stimuli probabilistically reinforced with feedback indicating receipt of monetary gains or avoidance of losses. Following training, the transfer phase required participants to select between pairs of previously presented stimuli during the acquisition phase and novel stimuli without receiving feedback. These test phase pairings allowed for inferences about the contributions of prediction error and value representation mechanisms to reinforcement learning deficits.

RESULTS

In acquisition, CHR participants displayed impaired learning from gains specifically that were associated with greater negative symptom severity. Transfer performance indicated these acquisition deficits were largely driven by value representation deficits. In addition to negative symptoms, this profile of deficits was associated with a greater risk of conversion to psychosis and lower functioning.

CONCLUSIONS

Impairments in positive reinforcement learning, specifically effectively representing reward value, may be an important transphasic mechanism of negative symptoms and a marker of psychosis liability.

A generalized reward processing deficit pathway to negative symptoms across diagnostic boundaries

BACKGROUND

Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

METHODS

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort-cost computation.

RESULTS

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

CONCLUSIONS

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.

Modeling Decision-Making in Schizophrenia: Associations Between Computationally Derived Risk Propensity and Self-Reported Risk Perception

BACKGROUND AND HYPOTHESIS

Schizophrenia is associated with a decreased pursuit of risky rewards during uncertain-risk decision-making. However, putative mechanisms subserving this disadvantageous risky reward pursuit, such as contributions of cognition and relevant traits, remain poorly understood.

STUDY DESIGN

Participants (30 schizophrenia/schizoaffective disorder [SZ]; 30 comparison participants [CP]) completed the Balloon Analogue Risk Task (BART). Computational modeling captured subprocesses of uncertain-risk decision-making: Risk Propensity, Prior Belief of Success, Learning Rate, and Behavioral Consistency. IQ, self-reported risk-specific processes (ie, Perceived Risks and Expected Benefit of Risks), and non-risk-specific traits (ie, defeatist beliefs; hedonic tone) were examined for relationships with Risk Propensity to determine what contributed to differences in risky reward pursuit.

STUDY RESULTS

On the BART, the SZ group exhibited lower Risk Propensity, higher Prior Beliefs of Success, and comparable Learning Rates. Furthermore, Risk Propensity was positively associated with IQ across groups. Linear models predicting Risk Propensity revealed 2 interactions: 1 between group and Perceived Risk, and 1 between IQ and Perceived Risk. Specifically, in both the SZ group and individuals with below median IQ, lower Perceived Risks was related to lower Risk Propensity. Thus, lower perception of financial risks was associated with a less advantageous pursuit of uncertain-risk rewards.

CONCLUSIONS

Findings suggest consistently decreased risk-taking on the BART in SZ may reflect risk imperception, the failure to accurately perceive and leverage relevant information to guide the advantageous pursuit of risky rewards. Additionally, our results highlight the importance of cognition in uncertain-risk decision-making.

The neuroscience of mental illness: Building toward the future

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Global reward processing deficits predict negative symptoms transdiagnostically and transphasically in a severe mental illness-spectrum sample

Reward processing impairments are a key factor associated with negative symptoms in those with severe mental illnesses. However, past findings are inconsistent regarding which reward processing components are impaired and most strongly linked to negative symptoms. The current study examined the hypothesis that these mixed findings may be the result of multiple reward processing pathways (i.e., equifinality) to negative symptoms that cut across diagnostic boundaries and phases of illness. Participants included healthy controls (n = 100) who served as a reference sample and a severe mental illness-spectrum sample (n = 92) that included psychotic-like experiences, clinical high-risk for psychosis, bipolar disorder, and schizophrenia participants. All participants completed tasks measuring four RDoC Positive Valence System constructs: value representation, reinforcement learning, effort-cost computation, and hedonic reactivity. A k-means cluster analysis of the severe mental illness-spectrum samples identified three clusters with differential reward processing profiles that were characterized by: (1) global reward processing deficits (22.8%), (2) selective impairments in hedonic reactivity alone (40.2%), and (3) preserved reward processing (37%). Elevated negative symptoms were only observed in the global reward processing cluster. All clusters contained participants from each clinical group, and the distribution of these groups did not significantly differ among the clusters. Findings identified one pathway contributing to negative symptoms that was transdiagnostic and transphasic. Future work further characterizing divergent pathways to negative symptoms may help to improve symptom trajectories and personalized treatments.

The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward

Introduction

Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward.

Methods

fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure.

Results

From the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval.

Discussion

Our analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model's notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain's sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.

The Causes and Consequences of Drifting Expectations

Awaiting news of uncertain outcomes is distressing because the news might be disappointing. To prevent such disappointments, people often "brace for the worst," pessimistically lowering expectations before news arrives to decrease the possibility of surprising disappointment (a negative prediction error, or PE). Computational decision-making research commonly assumes that expectations do not drift within trials, yet it is unclear whether expectations pessimistically drift in real-world, high-stakes settings, what factors influence expectation drift, and whether it effectively buffers emotional responses to goal-relevant outcomes. Moreover, individuals learn from PEs to accurately anticipate future outcomes, but it is unknown whether expectation drift also impedes PE-based learning. In a sample of students awaiting exam grades (N = 625), we found that expectations often drift and tend to drift pessimistically. We demonstrate that bracing is preferentially modulated by uncertainty; it transiently buffers the initial emotional impact of negative PEs but impairs PE-based learning, counterintuitively sustaining uncertainty into the future.

The challenge of learning adaptive mental behavior

Many psychotherapies aim to help people replace maladaptive mental behaviors (such as those leading to unproductive worry) with more adaptive ones (such as those leading to active problem solving). Yet, little is known empirically about how challenging it is to learn adaptive mental behaviors. Mental behaviors entail taking mental operations and thus may be more challenging to perform than motor actions; this challenge may enhance or impair learning. In particular, challenge when learning is often desirable because it improves retention. Yet, it is also plausible that the necessity of carrying out mental operations interferes with learning the expected values of mental actions by impeding credit assignment: the process of updating an action's value after reinforcement. Then, it may be more challenging not only to perform-but also to learn the consequences of-mental (vs. motor) behaviors. We designed a task to assess learning to take adaptive mental versus motor actions via matched probabilistic feedback. In two experiments (N = 300), most participants found it more difficult to learn to select optimal mental (vs. motor) actions, as evident in worse accuracy not only in a learning but also test (retention) phase. Computational modeling traced this impairment to an indicator of worse credit assignment (impaired construction and maintenance of expected values) when learning mental actions, accounting for worse accuracy in the learning and retention phases. The results suggest that people have particular difficulty learning adaptive mental behavior and pave the way for novel interventions to scaffold credit assignment and promote adaptive thinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evidence for shallow cognitive maps in schizophrenia

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

Reward Learning as a Potential Mechanism for Improvement in Schizophrenia Spectrum Disorders Following Cognitive Remediation: Protocol for a Clinical, Nonrandomized, Pre-Post Pilot Study

BACKGROUND

Cognitive impairment is common with schizophrenia spectrum disorders. Cognitive remediation (CR) is effective in improving global cognition, but not all individuals benefit from this type of intervention. A better understanding of the potential mechanism of action of CR is needed. One proposed mechanism is reward learning (RL), the cognitive processes responsible for adapting behavior following positive or negative feedback. It is proposed that the structure of CR enhances RL and motivation to engage in increasingly challenging tasks, and this is a potential mechanism by which CR improves cognitive functioning in schizophrenia.

OBJECTIVE

Our primary objective is to examine reward processing in individuals with schizophrenia before and after completing CR and to compare this with a group of matched clinical controls. We will assess whether RL mediates the relationship between CR and improved cognitive function and reduced negative symptoms. Potential differences in social RL and nonsocial RL in individuals with schizophrenia will also be investigated and compared with a healthy matched control group.

METHODS

We propose a clinical, nonrandomized, pre-post pilot study comparing the impact of CR on RL and neurocognitive outcomes. The study will use a combination of objective and subjective measures to assess neurocognitive, psychiatric symptoms, and neurophysiological domains. A total of 40 individuals with schizophrenia spectrum disorders (aged 18-35 years) will receive 12 weeks of CR therapy (n=20) or treatment as usual (n=20). Reward processing will be evaluated using a reinforcement learning task with 2 conditions (social reward vs nonsocial reward) at baseline and the 12-week follow-up. Functional magnetic resonance imaging responses will be measured during this task. To validate the reinforcement learning task, RL will also be assessed in 20 healthy controls, matched for age, sex, and premorbid functioning. Mixed-factorial ANOVAs will be conducted to evaluate treatment group differences. For the functional magnetic resonance imaging analysis, computational modeling will allow the estimation of learning parameters at each point in time, during each task condition, for each participant. We will use a variational Bayesian framework to measure how learning occurred during the experimental task and the subprocesses that underlie this learning. Second-level group analyses will examine how learning in patients differs from that observed in control participants and how CR alters learning efficiency and the underlying neural activity.

RESULTS

As of September 2023, this study has enrolled 15 participants in the CR group, 1 participant in the treatment-as-usual group, and 11 participants in the healthy control group. Recruitment is expected to be completed by September 2024. Data analysis is expected to be completed and published in early 2025.

CONCLUSIONS

The results of this study will contribute to the knowledge of CR and RL processes in severe mental illness and the understanding of the systems that impact negative symptoms and cognitive impairments within this population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52505.

Schizophrenia: from neurochemistry to circuits, symptoms and treatments

Schizophrenia is a leading cause of global disability. Current pharmacotherapy for the disease predominantly uses one mechanism - dopamine D2 receptor blockade - but often shows limited efficacy and poor tolerability. These limitations highlight the need to better understand the aetiology of the disease to aid the development of alternative therapeutic approaches. Here, we review the latest meta-analyses and other findings on the neurobiology of prodromal, first-episode and chronic schizophrenia, and the link to psychotic symptoms, focusing on imaging evidence from people with the disorder. This evidence demonstrates regionally specific neurotransmitter alterations, including higher glutamate and dopamine measures in the basal ganglia, and lower glutamate, dopamine and γ-aminobutyric acid (GABA) levels in cortical regions, particularly the frontal cortex, relative to healthy individuals. We consider how dysfunction in cortico-thalamo-striatal-midbrain circuits might alter brain information processing to underlie psychotic symptoms. Finally, we discuss the implications of these findings for developing new, mechanistically based treatments and precision medicine for psychotic symptoms, as well as negative and cognitive symptoms.

Differential deficits in social versus monetary reinforcement learning in schizophrenia: Associations with facial emotion recognition

Despite evidence that individuals with schizophrenia (SZ) have an intact desire for social relationships, they have small social networks and report high levels of loneliness. Difficulty with reinforcement learning (RL), the ability to update behavior based on feedback, may inhibit the formation and maintenance of social relationships in SZ. However, impaired RL in SZ has largely been demonstrated via monetary tasks. Thus, it remains unclear whether SZ are similarly impaired in social and monetary RL, or whether social-specific factors may further inhibit their ability to learn from social feedback. Thirty-one individuals with SZ and 31 healthy controls (HCs) participated in a RL paradigm to test hypotheses about social versus monetary RL. SZ exhibited impaired RL compared to HCs in both social and monetary tasks. Further, a Group × Task interaction demonstrated that SZ was more impaired when learning from social than monetary reinforcement, F(1, 59) = 5.99, p = .017. This differential deficit to social RL was not accounted for by reported pleasure from social feedback, which did not differ between groups. Instead, SZ had poorer emotion recognition than HCs, t(1, 60) = 4.80, p < .001, particularly for negative emotions, and controlling for this eliminated the differential social RL impairment. These results suggest the possibility that difficulty recognizing social cues, especially those indicating negative feedback, may relate to a reduced ability to learn from others' feedback. Thus, future research could elucidate whether targeting these emotion recognition difficulties in treatment could serve as a potential mechanism for improving social functioning in SZ. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Reinforcement learning profiles and negative symptoms across chronic and clinical high-risk phases of psychotic illness

Negative symptoms are prominent in individuals with schizophrenia (SZ) and youth at clinical high-risk for psychosis (CHR). In SZ, negative symptoms are linked to reinforcement learning (RL) dysfunction; however, previous research suggests implicit RL remains intact. It is unknown whether implicit RL is preserved in the CHR phase where negative symptom mechanisms are unclear, knowledge of which may assist in developing early identification and prevention methods. Participants from two studies completed an implicit RL task: Study 1 included 53 SZ individuals and 54 healthy controls (HC); Study 2 included 26 CHR youth and 23 HCs. Bias trajectories reflecting implicit RL were compared between groups and correlations with negative symptoms were examined. Cluster analysis investigated RL profiles across the combined samples. Implicit RL was comparable between HC and their corresponding SZ and CHR groups. However, cluster analysis was able to parse performance heterogeneity across diagnostic boundaries into two distinct RL profiles: a Positive/Early Learning cluster (65% of participants) with positive bias scores increasing from the first to second task block, and a Negative/Late Learning cluster (35% of participants) with negative bias scores increasing from the second to third block. Clusters did not differ in the proportion of CHR vs. SZ cases; however, the Negative/Late Learning cluster had more severe negative symptoms. Although implicit RL is intact in CHR similar to SZ, distinct implicit RL phenotypic profiles with elevated negative symptoms were identified trans-phasically, suggesting distinct reward-processing mechanisms can contribute to negative symptoms independent of phases of illness.

Revisiting reward impairments in schizophrenia spectrum disorders: a systematic review and meta-analysis for neuroimaging findings

BACKGROUND

Abnormal reward functioning is central to anhedonia and amotivation symptoms of schizophrenia (SCZ). Reward processing encompasses a series of psychological components. This systematic review and meta-analysis examined the brain dysfunction related to reward processing of individuals with SCZ spectrum disorders and risks, covering multiple reward components.

METHODS

After a systematic literature search, 37 neuroimaging studies were identified and divided into four groups based on their target psychology components (i.e. reward anticipation, reward consumption, reward learning, effort computation). Whole-brain Seed-based d Mapping (SDM) meta-analyses were conducted for all included studies and each component.

RESULTS

The meta-analysis for all reward-related studies revealed reduced functional activation across the SCZ spectrum in the striatum, orbital frontal cortex, cingulate cortex, and cerebellar areas. Meanwhile, distinct abnormal patterns were found for reward anticipation (decreased activation of the cingulate cortex and striatum), reward consumption (decreased activation of cerebellum IV/V areas, insula and inferior frontal gyri), and reward learning processing (decreased activation of the striatum, thalamus, cerebellar Crus I, cingulate cortex, orbitofrontal cortex, and parietal and occipital areas). Lastly, our qualitative review suggested that decreased activation of the ventral striatum and anterior cingulate cortex was also involved in effort computation.

CONCLUSIONS

These results provide deep insights on the component-based neuro-psychopathological mechanisms for anhedonia and amotivation symptoms of the SCZ spectrum.

A computational neuroimaging study of reinforcement learning and goal-directed exploration in schizophrenia spectrum disorders

BACKGROUND

Prior evidence indicates that negative symptom severity and cognitive deficits, in people with schizophrenia (PSZ), relate to measures of reward-seeking and loss-avoidance behavior (implicating the ventral striatum/VS), as well as uncertainty-driven exploration (reliant on rostrolateral prefrontal cortex/rlPFC). While neural correlates of reward-seeking and loss-avoidance have been examined in PSZ, neural correlates of uncertainty-driven exploration have not. Understanding neural correlates of uncertainty-driven exploration is an important next step that could reveal insights to how this mechanism of cognitive and negative symptoms manifest at a neural level.

METHODS

We acquired fMRI data from 29 PSZ and 36 controls performing the Temporal Utility Integration decision-making task. Computational analyses estimated parameters corresponding to learning rates for both positive and negative reward prediction errors (RPEs) and the degree to which participates relied on representations of relative uncertainty. Trial-wise estimates of expected value, certainty, and RPEs were generated to model fMRI data.

RESULTS

Behaviorally, PSZ demonstrated reduced reward-seeking behavior compared to controls, and negative symptoms were positively correlated with loss-avoidance behavior. This finding of a bias toward loss avoidance learning in PSZ is consistent with previous work. Surprisingly, neither behavioral measures of exploration nor neural correlates of uncertainty in the rlPFC differed significantly between groups. However, we showed that trial-wise estimates of relative uncertainty in the rlPFC distinguished participants who engaged in exploratory behavior from those who did not. rlPFC activation was positively associated with intellectual function.

CONCLUSIONS

These results further elucidate the nature of reinforcement learning and decision-making in PSZ and healthy volunteers.

Current status, challenges and future prospects in computational psychiatry: a narrative review

BACKGROUND

Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience, psychiatry, and computer science. It employs mathematical models and computational simulations to shed light on the complexities inherent to mental disorders.

AIM

The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, to discuss its significant challenges, as well as the potential opportunities for the fields growth.

METHODS

The authors have carried out a narrative review of the scientific literature published on the topic of computational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholar databases. A descriptive analysis was used to summarize the published information on the theoretical and practical aspects of computational psychiatry.

RESULTS

The article relates the development of the scientific approach in computational psychiatry since the mid-1980s. The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining the mechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) are summarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.

CONCLUSION

The capacity of current computational technologies in psychiatry has reached a stage where its integration into psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are no longer rooted in technological advancement, but in ethics, education, and understanding.

Transdiagnostic Predictors of Everyday Functioning: Examining the Relationships of Depression and Reinforcement Learning

BACKGROUND AND HYPOTHESIS

Impairments in function (ie, the ability to independently accomplish daily tasks) have been established in psychotic disorders. Identifying factors that contribute to these deficits is essential to developing effective interventions. The current study had several goals: examine potential differential relationships across domains of neurocognition, assess whether reinforcement learning is related to function, identify if predictors of function are transdiagnostic, determine whether depression and positive symptoms contribute to function, and to explore whether the modality of assessment impacts observed relationships.

STUDY DESIGN

Data from 274 participants were examined with schizophrenia/schizoaffective disorder (SZ; n = 195) and bipolar disorder (BD; n = 79). To reduce dimensionality, a PCA was completed on neurocognitive tasks which resulted in 3 components. These components and clinical interview data were used to investigate predictors of functional domains across measures of function (self- and informant-report SLOF and UPSA).

RESULTS

Two components, working memory/processing speed/episodic memory (βs = 0.18-0.42), and negative/positive reinforcement learning (β = -0.04), predicted different functional domains. Predictors of function were largely transdiagnostic with two exceptions: reinforcement learning had a positive association with self-reported interpersonal relationships for SZ and a negative association for BD (β = 0.34), and the negative association between positive symptoms and self-reported social acceptability was stronger for BD than for SZ (β = 0.93). Depression robustly predicted self-reported but not informant-reported function, and anhedonia predicted all domains of informant-reported function.

CONCLUSIONS

These findings imply that reinforcement learning may differentially relate to function across disorders, traditional domains of neurocognition can be effective transdiagnostic targets for interventions, and positive symptoms and depression play a critical role in self-perceived functional impairments.

Social Motivation in Schizophrenia: What's Effort Got to Do With It?

BACKGROUND AND HYPOTHESIS

Social motivation, defined as the fundamental human desire to seek out, engage in, and maintain interpersonal bonds, has become a growing area of research in schizophrenia. The major focus has been on understanding the impact of social reward-related processes. An obvious but rarely acknowledged fact is that social interactions, much like other goal-directed acts, require the exertion of effort. In this Review Article, we argue that social motivation in schizophrenia can be conceptualized through the lens of an established framework: effort-based decision-making (EBDM).

STUDY DESIGN

We conducted a literature review on social reward processing in schizophrenia, then extended these findings by applying concepts and insights from the literature on EBDM to the study of social motivation.

STUDY RESULTS

Within the EBDM framework, decisions about whether or not to pursue social interactions are bound by cost/benefit calculations. That is, people do not pursue social behaviors when the estimated "cost" of the required effort outweighs the anticipated "benefit" or reward. We propose that people with schizophrenia are less likely to engage in social interaction compared with healthy samples because they: (1) underestimate the benefits of relationships (based on expectations of reward/punishment), (2) overestimate the effort costs associated with social interaction, and/or (3) fail to integrate cost-benefit information in an optimal manner.

CONCLUSIONS

EBDM is an especially promising framework of social motivation that goes beyond the current focus on social reward processing to include a focus on effort.

Negative symptoms in schizophrenia: Newly emerging measurements, pathways, and treatments

The negative symptoms of schizophrenia, which often appear earlier than any other symptom, are prominent and clinically relevant in the majority of patients. As a result, interest in their treatment has increased. Patients who exhibit significant negative symptoms have worse functional outcomes than those without, resulting in impairments in occupational, household, and recreational functioning, as well as difficulties in relationships. Yet treatment with currently available medications does not lead to any significant improvements in this core component of schizophrenia. An increased understanding of the pathophysiology underlying negative symptoms and the discovery of novel treatments that do not directly target dopamine offer the potential to develop therapies that may reduce negative symptoms and increase quality of life for patients. The current article will discuss the impact of negative symptoms, outline current measurement tools for the assessment of negative symptoms, and examine how these measures may be improved. Insights into the neural circuitry underlying negative symptoms will be discussed, and promising targets for the development of effective treatments for these symptoms will be identified. As more prospective, large-scale, randomized studies focus on the effects of treatments on negative symptoms, progress in this area is foreseeable. However, improvements in clinical assessment instruments, a better understanding of the underlying neural mechanisms, development of novel treatments with varied targets, and a greater focus on personalized treatment are all important to produce significant benefits for patients with negative symptoms of schizophrenia.

Trust and psychosis: a systematic review and meta-analysis

BACKGROUND

Impaired trust in other humans is commonly seen in psychosis and it leads to poor societal functioning. However, examining trust behavior in an experimental setting is challenging. Investigators have used the trust game, a neuro-economic game to assess trust behavior in psychosis. However, the findings are inconsistent. Hence, we systematically reviewed the existing literature and conducted a meta-analysis to examine trust behavior in patients with psychosis, their relatives, and those at high risk for psychosis.

METHODS

We searched electronic databases for studies that have examined trust game in patients with psychosis, published up to November 2021. The primary outcome measure was the baseline trust in a trust game by patients and controls. The meta-analysis was performed if at least three data sets of control and patient groups were available for that measure/design. We conducted meta-analyses with a random-effects model. The results were described narratively wherever meta-analysis was not possible due to paucity of studies.

RESULTS

The searches across the databases including cross-references yielded 465 publications of which 10 studies were included in the final analysis. Baseline trust in the trust game was significantly lower in patients with psychosis compared to controls (SMD 0.39, 95% CI -0.14 to 0.64, p -0.002). However, a similar decrease in baseline trust was not present in relatives of patients (SMD 0.08, 95% CI -0.20 to 0.36, p -0.58).

CONCLUSIONS

The current meta-analysis suggests significant trust deficits in patients with psychosis. Future studies with a bigger sample size are required to understand the nature of trust deficits and factors affecting this impairment.

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.

Reward processing in schizophrenia and its relation to Mu opioid receptor availability and negative symptoms: A [11C]-carfentanil PET and fMRI study

BACKGROUND

Reward processing deficits are a core feature of schizophrenia and are thought to underlie negative symptoms. Pre-clinical evidence suggests that opioid neurotransmission is linked to reward processing. However, the contribution of Mu Opioid Receptor (MOR) signalling to the reward processing abnormalities in schizophrenia is unknown. Here, we examined the association between MOR availability and the neural processes underlying reward anticipation in patients with schizophrenia using multimodal neuroimaging.

METHOD

37 subjects (18 with Schizophrenia with moderate severity negative symptoms and 19 age and sex-matched healthy controls) underwent a functional MRI scan while performing the Monetary Incentive Delay (MID) task to measure the neural response to reward anticipation. Participants also had a [11C]-carfentanil PET scan to measure MOR availability.

RESULTS

Reward anticipation was associated with increased neural activation in a widespread network of brain regions including the striatum. Patients with schizophrenia had both significantly lower MOR availability in the striatum as well as striatal hypoactivation during reward anticipation. However, there was no association between MOR availability and striatal neural activity during reward anticipation in either patient or controls (Pearson's Correlation, controls df = 17, r = 0.321, p = 0.18, patients df = 16, r = 0.295, p = 0.24). There was no association between anticipation-related neural activation and negative symptoms (r = -0.120, p = 0.14) or anhedonia severity (social r = -0.365, p = 0.14 physical r = -0.120, p = 0.63).

CONCLUSIONS

Our data suggest reduced MOR availability in schizophrenia might not underlie striatal hypoactivation during reward anticipation in patients with established illness. Therefore, other mechanisms, such as dopamine dysfunction, warrant further investigation as treatment targets for this aspect of the disorder.

Associations Between Cognitive and Physical Effort-Based Decision Making in People With Schizophrenia and Healthy Control Subjects

BACKGROUND

Effort can take a variety of forms including physical (e.g., button pressing) and cognitive (e.g., working memory tasks). Few studies have examined whether individual differences in willingness to expend effort are similar or different across modalities.

METHODS

We recruited 30 individuals with schizophrenia and 44 healthy control subjects to complete 2 effort-cost decision-making tasks: the Effort Expenditure for Rewards Task (physical effort) and the cognitive effort discounting task (cognitive effort).

RESULTS

Willingness to expend cognitive and physical effort was positively associated for both individuals with schizophrenia and control subjects. Further, we found that individual differences in motivation and pleasure dimension of negative symptoms modulated the association between physical and cognitive effort. Specifically, participants with lower motivation and pleasure scores, irrespective of group status, showed stronger associations between task measures of cognitive and physical effort-cost decision making.

CONCLUSIONS

These results suggest a generalized deficit across effort modalities in individuals with schizophrenia. Further, reductions in motivation and pleasure may impact effort-cost decision making in a domain-general manner.

Intrinsic rewards explain context-sensitive valuation in reinforcement learning

When observing the outcome of a choice, people are sensitive to the choice's context, such that the experienced value of an option depends on the alternatives: getting $1 when the possibilities were 0 or 1 feels much better than when the possibilities were 1 or 10. Context-sensitive valuation has been documented within reinforcement learning (RL) tasks, in which values are learned from experience through trial and error. Range adaptation, wherein options are rescaled according to the range of values yielded by available options, has been proposed to account for this phenomenon. However, we propose that other mechanisms-reflecting a different theoretical viewpoint-may also explain this phenomenon. Specifically, we theorize that internally defined goals play a crucial role in shaping the subjective value attributed to any given option. Motivated by this theory, we develop a new "intrinsically enhanced" RL model, which combines extrinsically provided rewards with internally generated signals of goal achievement as a teaching signal. Across 7 different studies (including previously published data sets as well as a novel, preregistered experiment with replication and control studies), we show that the intrinsically enhanced model can explain context-sensitive valuation as well as, or better than, range adaptation. Our findings indicate a more prominent role of intrinsic, goal-dependent rewards than previously recognized within formal models of human RL. By integrating internally generated signals of reward, standard RL theories should better account for human behavior, including context-sensitive valuation and beyond.

Negative Symptom Inventory-Self-Report (NSI-SR): Initial development and validation

Negative symptoms (i.e., anhedonia, avolition, asociality, blunted affect, alogia) are frequently observed in the schizophrenia-spectrum (SZ) and associated with functional disability. While semi-structured interviews of negative symptoms represent a gold-standard approach, they require specialized training and may be vulnerable to rater biases. Thus, brief self-report questionnaires measuring negative symptoms may be useful. Existing negative symptom questionnaires demonstrate that this approach may be promising in schizophrenia, but no measure has been devised for use across stages of psychotic illness. The present study reports initial psychometric validation of the Negative Symptom Inventory-Self-Report (NSI-SR), the self-report counterpart of the Negative Symptom Inventory-Psychosis Risk clinical interview. The NSI-SR is a novel transphasic negative symptoms measure assessing the domains of anhedonia, avolition, and asociality. The NSI-SR and related measures were administered to two samples: 1) undergraduates (n = 335), 2) community participants, including: SZ (n = 32), clinical-high risk for psychosis (CHR, n = 25), and healthy controls matched to SZ (n = 31) and CHR (n = 30). The psychometrically trimmed 11-item NSI-SR showed good internal consistency and a three-factor solution reflecting avolition, asociality, and anhedonia. The NSI-SR demonstrated convergent validity via moderate to large correlations with clinician-rated negative symptoms and related constructs in both samples. Discriminant validity was supported by lower correlations with positive symptoms in both samples; however, correlations with positive symptoms were still significant. These initial psychometric findings suggest that the NSI-SR is a reliable and valid brief questionnaire capable of measuring negative symptoms across phases of psychotic illness.

"Goals in Focus"-a targeted CBT approach for motivational negative symptoms of psychosis: study protocol for a randomized-controlled feasibility trial

BACKGROUND

The reduction of goal-directed behavior is the main characteristic in motivational negative symptoms of psychosis as it accounts for the long-term decline in psychological well-being and psychosocial functioning. However, the available treatment options are largely unspecific and show only small effects on motivational negative symptoms. Interventions that directly target the relevant psychological mechanisms are likely to be more effective. For "Goals in Focus", we translated findings from basic clinical research on mechanisms underlying motivational negative symptoms into a tailored and comprehensive novel psychological outpatient treatment program. With this study, we will test the feasibility of the therapy manual and the trial procedures. We also aim to examine first estimates of the effect size that can be expected from "Goals in Focus" to inform the sample size calculation of a subsequent fully powered trial.

METHODS

Thirty participants diagnosed with a schizophrenia spectrum disorder and at least moderate motivational negative symptoms will be randomly assigned to either 24 sessions of "Goals in Focus" over the course of 6 months (n = 15) or to a 6-month wait-list control group (n = 15). Single-blind assessments will be conducted at baseline (t₀) and 6 months after baseline completion (t₁). Feasibility outcomes include patient recruitment, retention, and attendance rates. Acceptability will be rated by trial therapists and by participants at end of treatment. Primary outcome for effect size estimation is the motivational negative symptom subscale sum score of the Brief Negative Symptom Scale at t₁ corrected for baseline values. Secondary outcomes include psychosocial functioning, psychological well-being, depressive symptoms, expressive negative symptoms, negative symptom factor scores, and goal pursuit in everyday life.

DISCUSSION

The feasibility and acceptability data will be used to improve trial procedures and the "Goals in Focus" intervention where necessary. The treatment effect on the primary outcome will provide the basis for the sample size calculation for a fully powered RCT.

TRIAL REGISTRATION

1) ClinicalTrials.gov, NCT05252039 . Registered on 23 February 2022. 2) Deutsches Register Klinischer Studien, DRKS00018083 . Registered on 28 August 2019.

Alpha Event-Related Desynchronization During Reward Processing in Schizophrenia

BACKGROUND

Alterations in the brain's reward system may underlie motivation and pleasure deficits in schizophrenia (SZ). Neuro-oscillatory desynchronization in the alpha band is thought to direct resource allocation away from the internal state, to prioritize processing salient environmental events, including reward feedback. We hypothesized reduced reward-related alpha event-related desynchronization (ERD) in SZ, consistent with less externally focused processing during reward feedback.

METHODS

Electroencephalography was recorded while participants with SZ (n = 54) and healthy control participants (n = 54) played a simple slot machine task. Total alpha band power (8-14 Hz), a measure of neural oscillation magnitude, was extracted via principal component analysis and compared between groups and reward outcomes. The clinical relevance of hypothesized alpha power alterations was examined by testing associations with negative symptoms within the SZ group and with trait rumination, dimensionally, across groups.

RESULTS

A group × reward outcome interaction (p = .018) was explained by healthy control participants showing significant posterior-occipital alpha power suppression to wins versus losses (p < .001), in contrast to participants with SZ (p > .1). Among participants with SZ, this alpha ERD was unrelated to negative symptoms (p > .1). Across all participants, less alpha ERD to reward outcomes covaried with greater trait rumination for both win (p = .005) and loss (p = .002) outcomes, with no group differences in slope.

CONCLUSIONS

These findings demonstrate alpha ERD alterations in SZ during reward outcome processing. Additionally, higher trait rumination was associated with less alpha ERD during reward feedback, suggesting that individual differences in rumination covary with external attention to reward processing, regardless of reward outcome valence or group membership.

Neural Index of Reinforcement Learning Predicts Improved Stimulus-Response Retention under High Working Memory Load

Human learning and decision-making are supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM/RL computational model predicts that although high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here, we tested this account by parametrically manipulating WM load during RL in conjunction with EEG in both male and female participants and administered two surprise memory tests. We further leveraged single-trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus-response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention.SIGNIFICANCE STATEMENT Successful learning is achieved by the joint contribution of the dopaminergic RL system and WM. The cooperative WM/RL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the retention of learned stimulus-response associations remained unestablished. Our results show that increased neural signatures of learning, indicative of greater RL computation, under high WM load also predicted better stimulus-response retention. This result supports a trade-off between the two systems, where degraded WM increases RL processing, which improves retention. Notably, we show that this cooperative interplay remains largely unaffected by acute stress.

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.

On the normative advantages of dopamine and striatal opponency for learning and choice

The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation of opponent striatal pathways to do so. We develop the OpAL* model to assess the normative advantages of this circuitry. In OpAL*, learning induces opponent pathways to differentially emphasize the history of positive or negative outcomes for each action. Dynamic DA modulation then amplifies the pathway most tuned for the task environment. This efficient coding mechanism avoids a vexing explore-exploit tradeoff that plagues traditional RL models in sparse reward environments. OpAL* exhibits robust advantages over alternative models, particularly in environments with sparse reward and large action spaces. These advantages depend on opponent and nonlinear Hebbian plasticity mechanisms previously thought to be pathological. Finally, OpAL* captures risky choice patterns arising from DA and environmental manipulations across species, suggesting that they result from a normative biological mechanism.

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.

Using Nonhuman Primate Models to Reverse-Engineer Prefrontal Circuit Failure Underlying Cognitive Deficits in Schizophrenia

In this chapter, I review studies in nonhuman primates that emulate the circuit failure in prefrontal cortex responsible for working memory and cognitive control deficits in schizophrenia. These studies have characterized how synaptic malfunction, typically induced by blockade of NMDAR, disrupts neural function and computation in prefrontal networks to explain errors in cognitive tasks that are seen in schizophrenia. This work is finding causal relationships between pathogenic events of relevance to schizophrenia at vastly different levels of scale, from synapses, to neurons, local, circuits, distributed networks, computation, and behavior. Pharmacological manipulation, the dominant approach in primate models, has limited construct validity for schizophrenia pathogenesis, as the disease results from a complex interplay between environmental, developmental, and genetic factors. Genetic manipulation replicating schizophrenia risk is more advanced in rodent models. Nonetheless, gene manipulation in nonhuman primates is rapidly advancing, and primate developmental models have been established. Integration of large scale neural recording, genetic manipulation, and computational modeling in nonhuman primates holds considerable potential to provide a crucial schizophrenia model moving forward. Data generated by this approach is likely to fill several crucial gaps in our understanding of the causal sequence leading to schizophrenia in humans. This causal chain presents a vexing problem largely because it requires understanding how events at very different levels of scale relate to one another, from genes to circuits to cognition to social interactions. Nonhuman primate models excel here. They optimally enable discovery of causal relationships across levels of scale in the brain that are relevant to cognitive deficits in schizophrenia. The mechanistic understanding of prefrontal circuit failure they promise to provide may point the way to more effective therapeutic interventions to restore function to prefrontal networks in the disease.

Social feedback enhances learning in Williams syndrome

Williams syndrome (WS) is a rare genetic condition characterized by high social interest and approach motivation as well as intellectual disability and anxiety. Despite the fact that social stimuli are believed to have an increased intrinsic reward value in WS, it is not known whether this translates to learning and decision making. Genes homozygously deleted in WS are linked to sociability in the general population, making it a potential model condition for understanding the social brain. Probabilistic reinforcement learning was studied with either social or non-social rewards for correct choices. Social feedback improved learning in individuals with Williams syndrome but not in typically developing controls or individuals with other intellectual disabilities. Computational modeling indicated that these effects on social feedback were mediated by a shift towards higher weight given to rewards relative to punishments and increased choice consistency. We conclude that reward learning in WS is characterized by high volatility and a tendency to learn how to avoid punishment rather than how to gain rewards. Social feedback can partly normalize this pattern and promote adaptive reward learning.

Aberrant reward processing to positive versus negative outcomes across psychotic disorders

Several studies of reward processing in schizophrenia have shown reduced sensitivity to positive, but not negative, outcomes although inconsistencies have been reported. In addition, few studies have investigated whether patients show a relative deficit to social versus nonsocial rewards, whether deficits occur across the spectrum of psychosis, or whether deficits relate to negative symptoms and functioning. This study examined probabilistic implicit learning via two visually distinctive slot machines for social and nonsocial rewards in 101 outpatients with diverse psychotic disorders and 48 community controls. The task consisted of two trial types: positive (optimal to choose a positive vs. neutral machine) and negative (optimal to choose a neutral vs. negative machine), with two reward conditions: social (faces) and nonsocial (money) reward conditions. A significant group X trial type interaction indicated that controls performed better on positive than negative trials, whereas patients showed the opposite pattern of better performance on negative than positive trials. In addition, both groups performed better for social than nonsocial stimuli, despite lower overall task performance in patients. Within patients, worse performance on negative trials showed significant, small-to-moderate correlations with motivation and pleasure-related negative symptoms and social functioning. The current findings suggest reward processing disturbances, particularly decreased sensitivity to positive outcomes, extend beyond schizophrenia to a broader spectrum of psychotic disorders and relate to important clinical outcomes.

Decision value signals in the ventromedial prefrontal cortex and motivational and hedonic symptoms across mood and psychotic disorders

Deficits in motivation and pleasure are common across many psychiatric disorders, and manifest as symptoms of amotivation and anhedonia, which are prominent features of both mood and psychotic disorders. Here we provide evidence for an association between neural value signals and symptoms of amotivation and anhedonia across adults with major depression, bipolar disorder, schizophrenia, or no psychiatric diagnosis. We found that value signals in the ventromedial prefrontal cortex (vmPFC) during intertemporal decision-making were dampened in individuals with greater motivational and hedonic deficits, after accounting for primary diagnosis. This relationship remained significant while controlling for diagnosis-specific symptoms of mood and psychosis, such as depression as well as positive and negative symptoms. Our results demonstrate that dysfunction in the vmPFC during value-based decision-making is specifically linked to motivational and hedonic impairments. These findings provide a quantitative neural target for the potential development of novel treatments for amotivation and anhedonia.

Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification

BACKGROUND

Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.

METHODS

We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72).

RESULTS

Using accuracy, there was a main effect of group (F3,279 = 7.87, p < .001), such that all patient groups were less accurate than control subjects. Using computationally derived parameters, both medicated and unmediated individuals with schizophrenia, but not patients with bipolar disorder, demonstrated a reduced mixing parameter (F3,295 = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F1,289 = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F3,295 = 2.67, p = .04).

CONCLUSIONS

Both medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.

Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions

The development of treatments for impaired cognition in schizophrenia has been characterized as the most important challenge facing psychiatry at the beginning of the twenty-first century. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) project was designed to build on the potential benefits of using tasks and tools from cognitive neuroscience to better understanding and treat cognitive impairments in psychosis. These benefits include: (1) the use of fine-grained tasks that measure discrete cognitive processes; (2) the ability to design tasks that distinguish between specific cognitive domain deficits and poor performance due to generalized deficits resulting from sedation, low motivation, poor test taking skills, etc.; and (3) the ability to link cognitive deficits to specific neural systems, using animal models, neuropsychology, and functional imaging. CNTRICS convened a series of meetings to identify paradigms from cognitive neuroscience that maximize these benefits and identified the steps need for translation into use in clinical populations. The Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CNTRaCS) Consortium was developed to help carry out these steps. CNTRaCS consists of investigators at five different sites across the country with diverse expertise relevant to a wide range of the cognitive systems identified as critical as part of CNTRICs. This work reports on the progress and current directions in the evaluation and optimization carried out by CNTRaCS of the tasks identified as part of the original CNTRICs process, as well as subsequent extensions into the Positive Valence systems domain of Research Domain Criteria (RDoC). We also describe the current focus of CNTRaCS, which involves taking a computational psychiatry approach to measuring cognitive and motivational function across the spectrum of psychosis. Specifically, the current iteration of CNTRaCS is using computational modeling to isolate parameters reflecting potentially more specific cognitive and visual processes that may provide greater interpretability in understanding shared and distinct impairments across psychiatric disorders.

Low goal-directed behavior in negative symptoms is explained by goal setting - Results of a diary study

BACKGROUND AND OBJECTIVES

Engaging in goal-directed activities is a core difficulty of people with negative symptoms in schizophrenia. A previously developed goal pursuit model of negative symptoms (Schlier et al. 2017) postulates that negative symptom severity correlates with a tendency to set more avoidance- than approach-oriented goals. This shift in goal orientation correlates with low levels of goal expectancy, goal importance, and goal commitment. We explored whether these alterations translate into reduced goal-directed behavior (i.e., reduced goal striving and goal attainment).

METHODS

We conducted a one-week diary-study in a population sample (N=91). Participants were assessed for subclinical negative symptoms at baseline. Next, they set a daily goal and completed an online survey measuring goal orientation, goal characteristics, goal pursuit, and goal attainment once per day for one week.

RESULTS

Multilevel regression analyses and structural equation models showed that negative symptoms correlated with a tendency to set less approach-oriented goals with reduced goal expectancy and goal commitment. Goal orientation, expectancy, and commitment mediated the association between negative symptoms and reduced goal pursuit and attainment.

LIMITATIONS

We used a community sample, thus our results need to be replicated in a clinical sample of people with motivational negative symptoms.

CONCLUSIONS

Our results support the hypothesis that dysfunctional goal pursuit processes explain why negative symptoms lead to reduced goal-directed behavior. Interventions focusing on goal setting and goal expectations could be promising in improving goal-directed behavior in people with negative symptoms.

Updated perspectives on the clinical significance of negative symptoms in patients with schizophrenia

INTRODUCTION

Negative symptoms in schizophrenia are associated with poor response to available treatments, poor quality of life, and functional outcome. Therefore, they represent a substantial burden for people with schizophrenia, their families, and health-care systems.

AREAS COVERED

In this manuscript, we will provide an update on the conceptualization, assessment, and treatment of this complex psychopathological dimension of schizophrenia.

EXPERT OPINION

Despite the progress in the conceptualization of negative symptoms and in the development of state-of-the-art assessment instruments made in the last decades, these symptoms are still poorly recognized, and not always assessed in line with current conceptualization. Every effort should be made to disseminate the current knowledge on negative symptoms, on their assessment instruments and available treatments whose efficacy is supported by research evidence. Longitudinal studies should be promoted to evaluate the natural course of negative symptoms, improve our ability to identify the different sources of secondary negative symptoms, provide effective interventions, and target primary and persistent negative symptoms with innovative treatment strategies. Further research is needed to identify pathophysiological mechanisms of primary negative symptoms and foster the development of new treatments.

Both unmedicated and medicated individuals with schizophrenia show impairments across a wide array of cognitive and reinforcement learning tasks

BACKGROUND

Schizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function.

METHODS

The present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning.

RESULTS

Individuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks.

CONCLUSIONS

These findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications.

Psychological Dimensions Relevant to Motivation and Pleasure in Schizophrenia

Motivation and pleasure deficits are common in schizophrenia, strongly linked with poorer functioning, and may reflect underlying alterations in brain functions governing reward processing and goal pursuit. While there is extensive research examining cognitive and reward mechanisms related to these deficits in schizophrenia, less attention has been paid to psychological characteristics that contribute to resilience against, or risk for, motivation and pleasure impairment. For example, psychological tendencies involving positive future expectancies (e.g., optimism) and effective affect management (e.g., reappraisal, mindfulness) are associated with aspects of reward anticipation and evaluation that optimally guide goal-directed behavior. Conversely, maladaptive thinking patterns (e.g., defeatist performance beliefs, asocial beliefs) and tendencies that amplify negative cognitions (e.g., rumination), may divert cognitive resources away from goal pursuit or reduce willingness to exert effort. Additionally, aspects of sociality, including the propensity to experience social connection as positive reinforcement may be particularly relevant for pursuing social goals. In the current review, we discuss the roles of several psychological characteristics with respect to motivation and pleasure in schizophrenia. We argue that individual variation in these psychological dimensions is relevant to the study of motivation and reward processing in schizophrenia, including interactions between these psychological dimensions and more well-characterized cognitive and reward processing contributors to motivation. We close by emphasizing the value of considering a broad set of modulating factors when studying motivation and pleasure functions in schizophrenia.

From Memories of Past Experiences to Present Motivation? A Meta-analysis on the Association Between Episodic Memory and Negative Symptoms in People With Psychosis

Based on findings from cognitive science, it has been theorized that the reductions in motivation and goal-directed behavior in people with psychosis could stem from impaired episodic memory. In the current meta-analysis, we investigated this putative functional link between episodic memory deficits and negative symptoms. We hypothesized that episodic memory deficits in psychosis would be related to negative symptoms in general but would be more strongly related to amotivation than to reduced expressivity. We included 103 eligible studies (13,622 participants) in the analyses. Results revealed significant, moderate negative associations of episodic memory with negative symptoms in general (k = 103; r = -.23; z = -13.40; P ≤ .001; 95% CI [-.26; -.20]), with amotivation (k = 16; r = -.18; z = -6.6; P ≤ .001; 95% CI [-.23; -.13]) and with reduced expressivity (k = 15; r = -.18; z = -3.30; P ≤.001; 95% CI[-.29; -.07]). These associations were not moderated by sociodemographic characteristics, positive symptoms, depression, antipsychotic medication or type of negative symptom scale. Although these findings provide sound evidence for the association between episodic memory deficits and amotivation, the rather small magnitude and the unspecific pattern of this relationship also indicate that episodic memory deficits are unlikely to be the only factor relevant to amotivation. This implicates that future research should investigate episodic memory in conjunction with other factors that could account for the association of episodic memory deficits and amotivation in psychosis.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

Reduced learning bias towards the reward context in medication-naive first-episode schizophrenia patients

BACKGROUND

Reinforcement learning has been proposed to contribute to the development of amotivation in individuals with schizophrenia (SZ). Accumulating evidence suggests dysfunctional learning in individuals with SZ in Go/NoGo learning and expected value representation. However, previous findings might have been confounded by the effects of antipsychotic exposure. Moreover, reinforcement learning also rely on the learning context. Few studies have examined the learning performance in reward and loss-avoidance context separately in medication-naïve individuals with first-episode SZ. This study aimed to explore the behaviour profile of reinforcement learning performance in medication-naïve individuals with first-episode SZ, including the contextual performance, the Go/NoGo learning and the expected value representation performance.

METHODS

Twenty-nine medication-naïve individuals with first-episode SZ and 40 healthy controls (HCs) who have no significant difference in age and gender, completed the Gain and Loss Avoidance Task, a reinforcement learning task involving stimulus pairs presented in both the reward and loss-avoidance context. We assessed the group difference in accuracy in the reward and loss-avoidance context, the Go/NoGo learning and the expected value representation. The correlations between learning performance and the negative symptom severity were examined.

RESULTS

Individuals with SZ showed significantly lower accuracy when learning under the reward than the loss-avoidance context as compared to HCs. The accuracies under the reward context (90%win- 10%win) in the Acquisition phase was significantly and negatively correlated with the Scale for the Assessment of Negative Symptoms (SANS) avolition scores in individuals with SZ. On the other hand, individuals with SZ showed spared ability of Go/NoGo learning and expected value representation.

CONCLUSIONS

Despite our small sample size and relatively modest findings, our results suggest possible reduced learning bias towards reward context among medication-naïve individuals with first-episode SZ. The reward learning performance was correlated with amotivation symptoms. This finding may facilitate our understanding of the underlying mechanism of negative symptoms. Reinforcement learning performance under the reward context may be important to better predict and prevent the development of schizophrenia patients' negative symptom, especially amotivation.

Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

A large body of research attributes learning deficits in schizophrenia (SZ) to the systems involved in value representation (prefrontal cortex, PFC) and reinforcement learning (basal ganglia, BG) as well as to the compromised connectivity of these regions. In this study, we employed learning tasks hypothesized to probe the function and interaction of the PFC and BG in patients with SZ-spectrum disorders in comparison to healthy control (HC) subjects. In the Instructed Probabilistic Selection task (IPST), participants received false instruction about one of the stimuli used in the course of probabilistic learning which creates confirmation bias, whereby the instructed stimulus is overvalued in comparison to its real experienced value. The IPST was administered to 102 patients with SZ and 120 HC subjects. We have shown that SZ patients and HC subjects were equally influenced by false instruction in reinforcement learning (RL) probabilistic task (IPST) (p-value = 0.441); however, HC subjects had significantly higher learning rates associated with the process of overcoming cognitive bias in comparison to SZ patients (p-value = 0.018). The behavioral results of our study could be hypothesized to provide further evidence for impairments in the SZ-BG circuitry; however, this should be verified by neurofunctional imaging studies.

Computational Psychiatry Needs Time and Context

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

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.