Adam J. Culbreth

Impaired Activation in Cognitive Control Regions Predicts Reversal Learning in Schizophrenia

Reinforcement learning deficits have been associated with schizophrenia (SZ). However, the pathophysiology that gives rise to these abnormalities remains unclear. To address this question, SZ patients (N = 58) and controls (CN; N = 36) completed a probabilistic reversal-learning paradigm during functional magnetic resonance imaging scanning. During the task, participants choose between 2 stimuli. Initially, 1 stimulus was frequently rewarded (80%); the other was infrequently rewarded (20%). The reward contingencies reversed periodically because the participant learned the more rewarded stimulus. The results indicated that SZ patients achieved fewer reversals than CN, and demonstrated decreased winstay-loseshift decision-making behavior. On loseshift compared to winstay trials, SZ patients showed reduced Blood Oxygen Level Dependent activation compared to CN in a network of brain regions widely associated with cognitive control, and striatal regions. Importantly, relationships between group membership and behavior were mediated by alterations in the activity of cognitive control regions, but not striatum. These findings indicate an important role for the cognitive control network in mediating the use and updating of value representations in SZ. Such results provide biological targets for further inquiry because researchers attempt to better characterize decision-making neural circuitry in SZ as a means to discover new pathways for interventions.

Reduced model-based decision-making in schizophrenia.

Individuals with schizophrenia have a diminished ability to use reward history to adaptively guide behavior. However, tasks traditionally used to assess such deficits often rely on multiple cognitive and neural processes, leaving etiology unresolved. In the current study, we adopted recent computational formalisms of reinforcement learning to distinguish between model-based and model-free decision-making in hopes of specifying mechanisms associated with reinforcement-learning dysfunction in schizophrenia. Under this framework, decision-making is model-free to the extent that it relies solely on prior reward history, and model-based if it relies on prospective information such as motivational state, future consequences, and the likelihood of obtaining various outcomes. Model-based and model-free decision-making was assessed in 33 schizophrenia patients and 30 controls using a 2-stage 2-alternative forced choice task previously demonstrated to discern individual differences in reliance on the 2 forms of reinforcement-learning. We show that, compared with controls, schizophrenia patients demonstrate decreased reliance on model-based decision-making. Further, parameter estimates of model-based behavior correlate positively with IQ and working memory measures, suggesting that model-based deficits seen in schizophrenia may be partially explained by higher-order cognitive deficits. These findings demonstrate specific reinforcement-learning and decision-making deficits and thereby provide valuable insights for understanding disordered behavior in schizophrenia. (PsycINFO Database Record

EMOTION REGULATION ABNORMALITIES IN SCHIZOPHRENIA: DIRECTED ATTENTION STRATEGIES FAIL TO DECREASE THE NEUROPHYSIOLOGICAL RESPONSE TO UNPLEASANT STIMULI

Previous research provides evidence that individuals with schizophrenia (SZ) have emotion regulation abnormalities, particularly when attempting to use reappraisal to decrease negative emotion. The current study extended this literature by examining the effectiveness of a different form of emotion regulation, directed attention, which has been shown to be effective at reducing negative emotion in healthy individuals. Participants included outpatients with SZ (n = 28) and healthy controls (CN: n = 25), who viewed unpleasant and neutral images during separate event-related potential and eye-movement tasks. Trials included both passive viewing and directed attention segments. During directed attention, gaze was directed toward highly arousing aspects of an unpleasant image, less arousing aspects of an unpleasant image, or a nonarousing aspect of a neutral image. The late positive potential (LPP) event-related potential component indexed emotion regulation success. Directing attention to nonarousing aspects of unpleasant images decreased the LPP in CN; however, SZ showed similar LPP amplitude when attention was directed toward more or less arousing aspects of unpleasant scenes. Eye tracking indicated that SZ were more likely than CN to attend to arousing portions of unpleasant scenes when attention was directed toward less arousing scene regions. Furthermore, pupilary data suggested that SZ patients failed to engage effortful cognitive processes needed to inhibit the prepotent response of attending to arousing aspects of unpleasant scenes when attention was directed toward nonarousing scene regions. Findings add to the growing literature indicating that individuals with SZ display emotion regulation abnormalities and provide novel evidence that dysfunctional emotion-attention interactions and generalized cognitive control deficits are associated with ineffective use of directed attention strategies to regulate negative emotion. (PsycINFO Database Record

Ecological momentary assessment of negative symptoms in schizophrenia: Relationships to effort-based decision making and reinforcement learning.

Negative symptoms are a core clinical feature of schizophrenia, but conceptual and methodological problems with current instruments can make their assessment challenging. One hypothesis is that current symptom assessments may be influenced by impairments in memory and may not be fully reflective of actual functioning outside of the laboratory. The present study sought to investigate the validity of assessing negative symptoms using ecological momentary assessment (EMA). Participants with schizophrenia (N = 31) completed electronic questionnaires on smartphones 4 times a day for 1 week. Participants also completed effort-based decision making and reinforcement learning (RL) tasks to assess the relationship between EMA and laboratory measures, which tap into negative symptom relevant domains. Hierarchical linear modeling analyses revealed that clinician-rated and self-report measures of negative symptoms were significantly related to negative symptoms assessed via EMA. However, working memory moderated the relationship between EMA and retrospective measures of negative symptoms, such that there was a stronger relationship between EMA and retrospective negative symptom measures among individuals with better working memory. The authors also found that negative symptoms assessed via EMA were related to poor performance on the effort task, whereas clinician-rated symptoms and self-reports were not. Further, they found that negative symptoms were related to poorer performance on learning reward contingencies. The findings suggest that negative symptoms can be assessed through EMA and that working memory impairments frequently seen in schizophrenia may affect recall of symptoms. Moreover, these findings suggest the importance of examining the relationship between laboratory tasks and symptoms assessed during daily life.

Intact Ventral Striatal Prediction Error Signaling in Medicated Schizophrenia Patients

BACKGROUND Midbrain dopaminergic neurons code a computational quantity, reward prediction error (RPE), which has been causally related to learning. Recently, this insight has been leveraged to link phenomenological and biological levels of understanding in psychiatric disorders, such as schizophrenia. However, results have been mixed, possibly due to small sample sizes. Here we present results from two studies with relatively large Ns to assess VS RPE in schizophrenia. METHODS In the current study we analyzed data from two independent studies, involving a total of 87 chronic medicated schizophrenia patients and 61 controls. Subjects completed a probabilistic reinforcement-learning task in conjunction with fMRI scanning. We fit each participants choice behavior to a Q-learning model and derived trial-wise RPEs. We then modeled BOLD signal data with parametric regressor functions using these values to determine whether patient and control groups differed in prediction-error-related BOLD signal modulations. RESULTS Both groups demonstrated robust VS RPE BOLD activations. Interestingly, these BOLD activation patterns did not differ between groups in either study. This was true when we included all participants in the analysis, as well as when we excluded participants whose data was not sufficiently fit by the models. CONCLUSIONS These data demonstrate the utility of computational methods in isolating/testing underlying mechanisms of interest in psychiatric disorders. Importantly, similar VS RPE signal encoding across groups suggests that this mechanism does not drive task deficits in these patients. Deficits may instead stem from aberrant prefrontal/parietal circuits associated with maintenance and selection of goal-relevant information.

Emotion Regulation Predicts Everyday Emotion Experience and Social Function in Schizophrenia

While recent evidence has pointed to disturbances in emotion regulation strategy use in schizophrenia, few studies have examined how these regulation strategies relate to emotionality and social behavior in daily life. Using ecological momentary assessment (EMA), we investigated the relationship between emotion regulation, emotional experience, and social interaction in the daily lives of individuals with schizophrenia. Participants (N = 30) used mobile phones to complete online questionnaires reporting their daily emotional experience and social interaction. Participants also completed self-report measures of habitual emotion regulation. Hierarchical linear modeling revealed that self-reported use of cognitive reappraisal and savoring of emotional experiences were related to greater positive emotion in daily life. In contrast, self-reported suppression was related to greater negative emotion, reduced positive emotion, and reduced social interaction in daily life. These findings suggest that individual differences in habitual emotion regulation strategy usage have important relationships to everyday emotional and social experiences in schizophrenia.

Effort-based decision-making in schizophrenia

Motivational impairment has long been associated with schizophrenia but the underlying mechanisms are not clearly understood. Recently, a small but growing literature has suggested that aberrant effort-based decision-making may be a potential contributory mechanism for motivational impairments in psychosis. Specifically, multiple reports have consistently demonstrated that individuals with schizophrenia are less willing than healthy controls to expend effort to obtain rewards. Further, this effort-based decision-making deficit has been shown to correlate with severity of negative symptoms and level of functioning, in many but not all studies. In the current review, we summarize this literature and discuss several factors that may underlie aberrant effort-based decision-making in schizophrenia.

Systems Level Modeling of Cognitive Control in Psychiatric Disorders: A Focus on Schizophrenia

Researchers who work in the area of psychopathology have increasingly come to recognize the transdiagnostic nature of many of the core features of mental illness. Transdiagnostic refers to the fact that many putatively different psychiatric disorders appear to share what on the surface appear to be similar symptoms, albeit to differing degrees of severity. There are many such symptoms, but one that clearly cuts across diagnostic boundaries is a deficit in cognitive control. Here we provide a selective review of formal modeling approaches for understanding cognitive control deficits in psychopathology. We focus on schizophrenia as an example domain of psychopathology as it has received the most attention in this literature and is the form of psychopathology with arguable the strongest documentation for evidence of cognitive control impairments. Furthermore, we focus primarily on system-level computational modeling approaches, as those, again arguably, have most explicitly attempted to model the mechanisms of cognitive control. However, it is also clear that mathematical formalisms have much to offer in this domain as well, and we bring such models into the review when they make strong contact with the cognitive control deficits literature in schizophrenia. We outline a general model of core mechanisms of cognitive control and their impairment in schizophrenia. We then overview modeling approaches that have attempted to capture these mechanisms, and what hypotheses they have helped to generate about the mechanisms of cognitive control impairment in schizophrenia.

Assessing Psychosis in African American Clients

Schizophrenia is a heterogeneous disorder that has been documented in nearly every culture in the world. However, the symptom patterns associated with schizophrenia do not manifest identically across cultures or people of different ethnicities. Evidences suggest that the schizophrenia diagnosis is more prevalent in African American clients, which may be attributed to factors such as diagnostic bias and error, immigration, and sociocultural factors (e.g., urban density and socioeconomic status). Given these diagnostic differences and the factors contributing to them, it would also be important to determine whether psychiatric rating scales used to evaluate the positive, negative, and disorganized symptoms of schizophrenia in clinical and research settings show differences in their psychometric properties and test characteristics in African American and Caucasian clients. The literature on this topic is scarce; however, in this chapter we report archival data on major clinical rating scales used to assess symptoms of schizophrenia in large samples of African American and Caucasian clients. These psychometric analyses suggested that the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms have good reliability and validity in both ethnic groups, supporting their use in African Americans with schizophrenia and other psychotic disorders.

Breaking Apart the Reinforcement Learning Deficit in Schizophrenia

OF THE THESIS Breaking Apart the Reinforcement Learning Deficit in Schizophrenia by Adam J. Culbreth Master of Arts in Psychology Washington University in St. Louis, 2015 Professor Deanna M. Barch, Chair Reinforcement learning deficits have long been associated with schizophrenia. However, tasks traditionally used to assess these deficits often rely on multiple processing streams leaving the etiology of these task deficits unclear. In the current study, we borrowed a recent framework from computational neuroscience, which separates reinforcement-learning into two distinct systems, model-based and modelfree. Under this framework, the model-free system learns about the value of actions in the immediate context, while the model-based system learns about the value of actions in both immediate and subsequent states that may be encountered as a result of their actions. Using a decision task that has been previously validated to assess relative reliance on each system we showed that individuals with schizophrenia demonstrated decreased model-based but intact model-free learning estimates. Furthermore, parameter estimates of model-based behavior correlated positively with IQ, suggesting that model-based deficits in schizophrenia may relate to reduced intellectual function. These findings specify reinforcement-learning deficits in schizophrenia by showing both intact and disturbed components. Such findings and computational frameworks provide meaningful insights as researchers continue to characterize decision-making circuitry in schizophrenia as a means to discover new pathways for interventions.