Reward and punishment learning in schizophrenia and bipolar disorder

Reward and punishment learning deficits among bipolar disorder subtypes

BACKGROUND

Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission.

METHODS

Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances.

RESULTS

Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II.

LIMITATIONS

Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition.

CONCLUSIONS

Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.

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.

Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review

Background and Aim

Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease.

Methods

A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers.

Results

In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity.

Conclusion

ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.

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.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

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.

Negative belief-updating bias for positive daily life events in individuals with schizophrenia and social anhedonia

INTRODUCTION

Low-pleasure beliefs are found in both patients with schizophrenia (SZ) and individuals with high social anhedonia (SocAnh), and are associated with anhedonia. However, little is known about the development and maintenance of these low-pleasure beliefs in the clinical and subclinical populations. We investigated whether patients with SZ and individuals with high SocAnh have deficits in updating their beliefs, which may contribute to the understanding of the formation and maintenance of low-pleasure beliefs.

METHODS

The Modified Belief Updating Task was administered to assess belief-updating patterns in a clinical sample (36 SZ patients and 30 matched controls) and a subclinical sample (27 individuals with high SocAnh and 30 matched controls).

RESULTS

We found that compared with controls, SZ patients updated their beliefs to a greater extent and more frequently when receiving bad news for positive life events, but not for negative life events. Moreover, individuals with high SocAnh also exhibited similar patterns in updating their beliefs for positive life events after controlling depressive symptoms.

CONCLUSIONS

Our findings suggest that negative belief-updating patterns for positive events may play an important role in the formation and maintenance of low-pleasure beliefs in patients with SZ and individuals with high SocAnh.

MiR-4763-3p targeting RASD2as a Potential Biomarker and Therapeutic Target for Schizophrenia

Existing diagnostic methods are limited to observing appearance and demeanor, even though genetic factors play important roles in the pathology of schizophrenia. Indeed, no molecular-level test exists to assist diagnosis, which has limited treatment strategies. To address this serious shortcoming, we used a bioinformatics approach to identify 61 genes that are differentially expressed in schizophrenia patients compared with healthy controls. In particular, competing endogenous RNA network revealed the important role of the gene RASD2, which is regulated by miR-4763-3p. Indeed, analysis of blood samples confirmed that RASD2 is downregulated in schizophrenia patients. Moreover, positron emission tomography data collected for 44 human samples identified the prefrontal and temporal lobes as potential key brain regions in schizophrenia patients. Mechanistic studies indicated that miR-4763-3p inhibits RASD2 by base-pairing with the 3’ untranslated region of RASD2 mRNA. Importantly, RASD2 has been shown to interact with β-arrestin2, which contributes to the regulation of the DRD2-dependent CREB response element-binding protein pathway in the dopamine system. Finally, results obtained with a mouse model of schizophrenia revealed that inhibition of miR-4763-3p function alleviated anxiety symptoms and improved memory. The dopamine transporters in the striatal regions were significantly reduced in schizophrenia model mice as compared with wild-type mice, suggesting that inhibition of miR-4763-3p can lessen the symptoms of schizophrenia. Our findings demonstrate that miR-4763-3p may target RASD2 mRNA and thus may serve as a potential biomarker and therapeutic target for schizophrenia, providing a theoretical foundation for further studies of the molecular basis of this disease.

Altered Risk-Taking Behavior in Early-Stage Bipolar Disorder With a History of Psychosis

Altered risk-taking propensity is an important determinant of functional impairment in bipolar disorder. However, prior studies primarily assessed patients with chronic illness, and risk-taking has not been evaluated in the early illness course. This study investigated risk-taking behavior in 39 euthymic early-stage bipolar disorder patients aged 16-40 years who were treated within 3 years from their first-episode mania with psychotic features and 36 demographically-matched healthy controls using the Balloon Analog Risk Task (BART), a well-validated risk-taking performance-based paradigm requiring participants to make responses for cumulative gain at increasing risk of loss. Relationships of risk-taking indices with symptoms, self-reported impulsivity, cognitive functions, and treatment characteristics were also assessed. Our results showed that patients exhibited significantly lower adjusted scores (i.e., average balloon pumps in unexploded trials) (p = 0.001), lower explosion rate (p = 0.007) and lower cumulative scores (p = 0.003) than controls on BART, indicating their suboptimal risk-taking performance with increased propensity for risk aversion. Risk-taking indices were not correlated with any symptom dimensions, self-reported impulsivity, cognitive functions or antipsychotic dose. No significant difference was observed between patients with and without antipsychotic medications on self-reported impulsivity or any of the BART performance indices. This is the first study to examine risk-taking behavior in early-stage bipolar disorder with history of psychosis and indicates that patients displayed altered risk-taking with increased risk aversion compared with controls. Further research is needed to clarify longitudinal trajectory of risk-taking propensity and its relationships with psychosis and functional outcome in the early stage of bipolar disorder.

Distinguishing patterns of impairment on inhibitory control and general cognitive ability among bipolar with and without psychosis, schizophrenia, and schizoaffective disorder

BACKGROUND

Deficits in inhibitory control on a Stop Signal Task (SST) were previously observed to be of similar magnitude across schizophrenia, schizoaffective, and bipolar disorder with psychosis, despite variation in general cognitive ability. Understanding different patterns of performance on the SST may elucidate different pathways to the impaired inhibitory control each group displayed. Comparing nonpsychotic bipolar disorder to the psychosis groups on SST may also expand our understanding of the shared neurobiology of this illness spectrum.

METHODS

We tested schizophrenia (n = 220), schizoaffective (n = 216), bipolar disorder with (n = 192) and without psychosis (n = 67), and 280 healthy comparison participants with a SST and the Brief Assessment of Cognition in Schizophrenia (BACS), a measure of general cognitive ability.

RESULTS

All patient groups had a similar degree of impaired inhibitory control over prepotent responses. However, bipolar groups differed from schizophrenia and schizoaffective groups in showing speeded responses and inhibition errors that were not accounted for by general cognitive ability. Schizophrenia and schizoaffective groups had a broader set of deficits on inhibition and greater general cognitive deficit, which fully accounted for the inhibition deficits. No differences were found between the clinically well-matched bipolar with and without psychosis groups, including for inhibitory control or general cognitive ability.

CONCLUSIONS

We conclude that 1) while impaired inhibitory control on a SST is of similar magnitude across the schizo-bipolar spectrum, including nonpsychotic bipolar, different mechanisms may underlie the impairments, and 2) history of psychosis in bipolar disorder does not differentially impact inhibitory behavioral control or general cognitive abilities.