Adaptive coding of reward in schizophrenia, its change over time and relation to apathy.

Adaptive coding of reward is the process by which neurons adapt their response to the context of available compensations. Higher rewards lead to a stronger brain response, but the increase of the response depends on the range of available rewards. A steeper increase is observed in a narrow range, and a more gradual slope in a wider range. In schizophrenia, adaptive coding appears affected in different domains, and in the reward domain in particular. Here we tested adaptive coding of reward in a large group of patients with schizophrenia (N = 86) and controls (N = 66). We assessed 1) the association between adaptive coding deficits and symptoms; 2) the longitudinal stability of deficits (the same task was performed three months apart); 3) the stability of results between two experimental sites. We used fMRI and the Monetary Incentive Delay task to assess participant' adaptation to two different reward ranges: a narrow and a wide range. We used a region of interest analysis, evaluating adaptation within striatal and visual regions. Patients and controls underwent a full demographic and clinical assessment. We found reduced adaptive coding in patients, due to a decreased slope in the narrow reward range, with respect to that of control participants in striatal but not visual regions. This pattern was observed at both research sites. Upon re-test, patients increased their narrow range slopes, showing improved adaptive coding, whereas controls slightly reduced them. At re-test, patients with overly steep slopes in the narrow range also showed higher levels of negative symptoms. Our data confirm deficits in reward adaptation in schizophrenia and reveal a practice effect in patients, leading to improvement, with steeper slopes upon retest. However, in some patients, an overly steep slope may result in poor discriminability of larger rewards, due to early saturation of the brain response. Together, the loss of precision of reward representation in new (first exposure, underadaptation) and more familiar (re-test, overadaptation) situations may contribute to the multiple motivational symptoms in schizophrenia.

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

BACKGROUND: Negative symptoms in schizophrenia (SZ), such as apathy and diminished expression, have limited treatments and significantly impact daily life. Our study focuses on the functional division of the striatum: limbic-motivation and reward, associative-cognition, and sensorimotor-sensory and motor processing, aiming to identify potential biomarkers for negative symptoms. STUDY DESIGN: This longitudinal, 2-center resting-state-fMRI (rsfMRI) study examines striatal seeds-to-whole-brain functional connectivity. We examined connectivity aberrations in patients with schizophrenia (PwSZ), focusing on stable group differences across 2-time points using intra-class-correlation and associated these with negative symptoms and measures of cognition. Additionally, in PwSZ, we used negative symptoms to predict striatal connectivity aberrations at the baseline and used the striatal aberration to predict symptoms 9 months later. STUDY RESULTS: A total of 143 participants (77 PwSZ, 66 controls) from 2 centers (Berlin/Geneva) participated. We found sensorimotor-striatum and associative-striatum hypoconnectivity. We identified 4 stable hypoconnectivity findings over 3 months, revealing striatal-fronto-parietal-cerebellar hypoconnectivity in PwSZ. From those findings, we found hypoconnectivity in the bilateral associative striatum with the bilateral paracingulate-gyrus and the anterior cingulate cortex in PwSZ. Additionally, hypoconnectivity between the associative striatum and the superior frontal gyrus was associated with lower cognition scores in PwSZ, and weaker sensorimotor striatum connectivity with the superior parietal lobule correlated negatively with diminished expression and could predict symptom severity 9 months later. CONCLUSIONS: Importantly, patterns of weaker sensorimotor striatum and superior parietal lobule connectivity fulfilled the biomarker criteria: clinical significance, reflecting underlying pathophysiology, and stability across time and centers.

Striatal Response to Reward Anticipation as a Biomarker for Schizophrenia and Negative Symptoms: Effects, Test-Retest Reliability, and Stability Across Sites.

BACKGROUND: Ventral striatal hypoactivation during reward anticipation has consistently been observed in patients with schizophrenia. In addition, that hypoactivation has been shown to correlate negatively with negative symptoms, and in particular with apathy. However, little is known about the stability of these results over time and their reliability across different centers. METHODS: In total, 67 patients with schizophrenia (15 females) and 55 healthy controls (13 females) were recruited in 2 centers in Switzerland and Germany. To assess the neural bases of reward anticipation, all participants performed a variant of the Monetary Incentive Delay task while undergoing event-related functional magnetic resonance imaging at baseline and after 3 months. Stability over time was measured using intra-class correlation (ICC(A,1)) and stability between centers was measured with mixed models. RESULTS: Results showed the expected ventral striatal hypoactivation in patients compared to controls during reward anticipation. We showed that these results were stable across centers. The primary analysis did not reveal an effect of time. Test-retest reliability was moderate for controls, and poor for patients. We did not find an association between ventral striatal hypoactivation and negative symptoms in patients. CONCLUSIONS: Our results align with the hypothesis that ventral striatal activation is related to modulation of motivational saliency during reward anticipation. They also confirm that patients with schizophrenia show impaired reward anticipation. However, the poor test-retest reliability and the absence of an association with symptoms suggests that further research is needed before ventral striatal activity can be used as a biomarker on the individual patient level.

Computational mechanisms of belief updating in relation to psychotic-like experiences.

Introduction: Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design. Methods: We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs. Results: PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (ß = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (ß = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (ß = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (ßPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (ßCPP = -0.84 ± 0.38, p = 0.023). Discussion: We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.

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

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

Acute stress alters probabilistic reversal learning in healthy male adults.

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

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

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

Glutamate in the Dorsolateral Prefrontal Cortex in Patients With Schizophrenia: A Meta-analysis of 1H-Magnetic Resonance Spectroscopy Studies.

BACKGROUND: To date, there is no systematic overview of glutamate in the dorsolateral prefrontal cortex (DLPFC) of patients with schizophrenia. Here, we meta-analyzed case-control studies of high-field proton magnetic resonance spectroscopy (1H-MRS) investigating glutamate in DLPFC. Additionally, we estimated variance ratios to investigate homo/heterogeneity. METHODS: Preregistration of the study was performed on September 20, 2019. The predefined literature search on PubMed comprised articles with search terms (magnetic resonance spectroscopy OR MRS) AND (glutamate OR glut∗ OR GLX) AND (schizophrenia OR psychosis OR schizophren∗). Meta-analyses with a fixed- and random-effects model with inverse variance method, DerSimonian-Laird estimator for τ2, and Cohen's d were calculated. For differences in variability, we calculated a random-effects model for measures of variance ratios. The primary study outcome was the difference in glutamate in the DLPFC in cases versus controls. Secondary outcomes were differences in variability. RESULTS: The quantitative analysis comprised 429 cases and 365 controls. Overall, we found no group difference (d = 0.03 [95% confidence interval (CI), -0.20 to 0.26], z = 0.28, p = .78). Sensitivity analysis revealed an effect for medication status (Q = 8.35, p = .039), i.e., increased glutamate in antipsychotic-naïve patients (d = 0.46 [95% CI, 0.08 to 0.84], z = 2.37, p = .018). Concerning variance ratios, we found an effect of medication status (Q = 16.95, p < .001) due to lower coefficient of variation ratio (CVR) in medication-naïve patients (logCVR = -0.49 [95% CI, -0.78 to -0.20], z = -3.33, p < .001). In studies with medicated patients, we found higher CVR (logCVR = 0.22 [95% CI, 0.06 to 0.39], z = 2.67; p = .008). CONCLUSIONS: We carefully interpret the higher levels and lower variability in cortical glutamate in antipsychotic-naïve patients as a possible key factor resulting from a putative allostatic mechanism. We conclude that care has to be taken when evaluating metabolite levels in clinical samples in which medication might confound findings.

Striatal Dopamine and Reward Prediction Error Signaling in Unmedicated Schizophrenia Patients.

Increased striatal dopamine synthesis capacity has consistently been reported in patients with schizophrenia. However, the mechanism translating this into behavior and symptoms remains unclear. It has been proposed that heightened striatal dopamine may blunt dopaminergic reward prediction error signaling during reinforcement learning. In this study, we investigated striatal dopamine synthesis capacity, reward prediction errors, and their association in unmedicated schizophrenia patients (n = 19) and healthy controls (n = 23). They took part in FDOPA-PET and underwent functional magnetic resonance imaging (fMRI) scanning, where they performed a reversal-learning paradigm. The groups were compared regarding dopamine synthesis capacity (Kicer), fMRI neural prediction error signals, and the correlation of both. Patients did not differ from controls with respect to striatal Kicer. Taking into account, comorbid alcohol abuse revealed that patients without such abuse showed elevated Kicer in the associative striatum, while those with abuse did not differ from controls. Comparing all patients to controls, patients performed worse during reversal learning and displayed reduced prediction error signaling in the ventral striatum. In controls, Kicer in the limbic striatum correlated with higher reward prediction error signaling, while there was no significant association in patients. Kicer in the associative striatum correlated with higher positive symptoms and blunted reward prediction error signaling was associated with negative symptoms. Our results suggest a dissociation between striatal subregions and symptom domains, with elevated dopamine synthesis capacity in the associative striatum contributing to positive symptoms while blunted prediction error signaling in the ventral striatum related to negative symptoms.

Volatility Estimates Increase Choice Switching and Relate to Prefrontal Activity in Schizophrenia.

BACKGROUND: Reward-based decision making is impaired in patients with schizophrenia (PSZ), as reflected by increased choice switching. The underlying cognitive and motivational processes as well as associated neural signatures remain unknown. Reinforcement learning and hierarchical Bayesian learning account for choice switching in different ways. We hypothesized that enhanced choice switching, as seen in PSZ during reward-based decision making, relates to higher-order beliefs about environmental volatility, and we examined the associated neural activity. METHODS: In total, 46 medicated PSZ and 43 healthy control subjects performed a reward-based decision-making task requiring flexible responses to changing action-outcome contingencies during functional magnetic resonance imaging. Detailed computational modeling of choice data was performed, including reinforcement learning and the hierarchical Gaussian filter. Trajectories of learning from computational modeling informed the analysis of functional magnetic resonance imaging data. RESULTS: A 3-level hierarchical Gaussian filter accounted best for the observed choice data. This model revealed a heightened initial belief about environmental volatility and a stronger influence of volatility on lower-level learning of action-outcome contingencies in PSZ as compared with healthy control subjects. This was replicated in an independent sample of nonmedicated PSZ. Beliefs about environmental volatility were reflected by higher activity in dorsolateral prefrontal cortex of PSZ as compared with healthy control subjects. CONCLUSIONS: Our study suggests that PSZ inferred the environment as overly volatile, which may explain increased choice switching. In PSZ, activity in dorsolateral prefrontal cortex was more strongly related to beliefs about environmental volatility. Our computational phenotyping approach may provide useful information to dissect clinical heterogeneity and could improve prediction of outcome.

Association of Cortical Glutamate and Working Memory Activation in Patients With Schizophrenia: A Multimodal Proton Magnetic Resonance Spectroscopy and Functional Magnetic Resonance Imaging Study.

BACKGROUND: Cognitive deficits such as working memory (WM) impairment are core features of schizophrenia. One candidate marker for the integrity of synaptic neurotransmission necessary for cognitive processes is glutamate. It is frequently postulated that antipsychotic medication possibly alters functional mechanisms in the living brain. We tested in vivo for group differences in activation of the dorsolateral prefrontal cortex (DLPFC) during WM performance and the association with glutamate concentration in DLPFC depending on medication status. METHODS: A total of 90 subjects (35 control subjects, 36 medicated patients, and 19 unmedicated patients) contributed magnetic resonance spectroscopy data. We estimated glutamate in left DLPFC. Subjects performed an n-back WM task (2-back vs. 0-back) during functional magnetic resonance imaging, and local activation in left DLPFC was measured. For analysis of association with medication status, we calculated linear regression models including an interaction effect with group. RESULTS: Medicated and unmedicated patients with schizophrenia showed impaired performance. We found significantly reduced WM activation in left DLPFC in medicated patients and a trendwise reduction in unmedicated patients as compared with control subjects. We found no group difference in local glutamate concentration. However, we found differential effects of medication status on the association between local glutamate concentration and WM activation in left DLPFC, with a positive association in unmedicated patients but not in medicated patients. CONCLUSIONS: We provide evidence that WM-dependent activation is associated with glutamate concentration in unmedicated patients with schizophrenia. Our finding points to putative allostatic changes that affect the functioning of the brain and might be altered through medication.

[Computational psychiatry : Data-driven vs. mechanistic approaches]. / Computationale Psychiatrie : Datengetriebene vs. mechanistische Ansätze.

The emerging research field of so-called computational psychiatry attempts to contribute to an understanding of complex psychiatric phenomena by applying computational methods and to promote the translation of neuroscientific research results into clinical practice. This article presents this field of research using selected examples based on the distinction between data-driven and theory-driven approaches. Exemplary for a data-driven approach are studies to predict clinical outcome, for example, in persons with a high-risk state for psychosis or on the response to pharmacological treatment for depression. Theory-driven approaches attempt to describe the mechanisms of altered information processing as the cause of psychiatric symptoms at the behavioral and neuronal level. In computational models possible mechanisms can be described that may have produced the measured behavioral or neuronal data. For example, in schizophrenia patients the clinical phenomenon of aberrant salience has been described as learning irrelevant information or cognitive deficits have been linked to connectivity changes in frontoparietal networks. Computational psychiatry can make important contributions to the prediction of individual clinical courses as well as to a mechanistic understanding of psychiatric symptoms. For this a further development of reliable and valid methods across different disciplines is indispensable.

Pupil dilation as an implicit measure of appetitive Pavlovian learning.

Appetitive Pavlovian conditioning is a learning mechanism of fundamental biological and pathophysiological significance. Nonetheless, its exploration in humans remains sparse, which is partly attributed to the lack of an established psychophysiological parameter that aptly represents conditioned responding. This study evaluated pupil diameter and other ocular response measures (gaze dwelling time, blink duration and count) as indices of conditioning. Additionally, a learning model was used to infer participants' learning progress on the basis of their pupil dilation. Twenty-nine healthy volunteers completed an appetitive differential delay conditioning paradigm with a primary reward, while the ocular response measures along with other psychophysiological (heart rate, electrodermal activity, postauricular and eyeblink reflex) and behavioral (ratings, contingency awareness) parameters were obtained to examine the relation among different measures. A significantly stronger increase in pupil diameter, longer gaze duration and shorter eyeblink duration was observed in response to the reward-predicting cue compared to the control cue. The Pearce-Hall attention model best predicted the trial-by-trial pupil diameter. This conditioned response was corroborated by a pronounced heart rate deceleration to the reward-predicting cue, while no conditioning effect was observed in the electrodermal activity or startle responses. There was no discernible correlation between the psychophysiological response measures. These results highlight the potential value of ocular response measures as sensitive indices for representing appetitive conditioning.

Reduced parietofrontal effective connectivity during a working-memory task in people with high delusional ideation

Background: Working-memory impairment is a core cognitive dysfunction in people with schizophrenia and people at mental high risk. Recent imaging studies on working memory have suggested that abnormalities in prefrontal activation and in connectivity between the frontal and parietal regions could be neural underpinnings of the different stages of psychosis. However, it remains to be explored whether comparable alterations are present in people with subclinical levels of psychosis, as experienced by a small proportion of the general population who neither seek help nor show constraints in daily functioning. Methods: We compared 24 people with subclinical high delusional ideation and 24 people with low delusional ideation. Both groups performed an n-back working-memory task during functional magnetic resonance imaging. We characterized frontoparietal effective connectivity using dynamic causal modelling. Results: Compared to people who had low delusional ideation, people with high delusional ideation showed a significant increase in dorsolateral prefrontal activation during the working-memory task, as well as reduced working-memory-dependent parietofrontal effective connectivity in the left hemisphere. Group differences were not evident at the behavioural level. Limitations: The current experimental design did not distinguish among the working-memory subprocesses; it remains unexplored whether differences in connectivity exist at that level. Conclusion: These findings suggest that alterations in the working-memory network are also present in a nonclinical population with psychotic experiences who do not display cognitive deficits. They also suggest that alterations in working-memory-dependent connectivity show a putative continuity along the spectrum of psychotic symptoms.

Modeling subjective relevance in schizophrenia and its relation to aberrant salience.

In schizophrenia, increased aberrant salience to irrelevant events and reduced learning of relevant information may relate to an underlying deficit in relevance detection. So far, subjective estimates of relevance have not been probed in schizophrenia patients. The mechanisms underlying belief formation about relevance and their translation into decisions are unclear. Using novel computational methods, we investigated relevance detection during implicit learning in 42 schizophrenia patients and 42 healthy individuals. Participants underwent functional magnetic resonance imaging while detecting the outcomes in a learning task. These were preceded by cues differing in color and shape, which were either relevant or irrelevant for outcome prediction. We provided a novel definition of relevance based on Bayesian precision and modeled reaction times as a function of relevance weighted unsigned prediction errors (UPE). For aberrant salience, we assessed responses to subjectively irrelevant cue manifestations. Participants learned the contingencies and slowed down their responses following unexpected events. Model selection revealed that individuals inferred the relevance of cue features and used it for behavioral adaption to the relevant cue feature. Relevance weighted UPEs correlated with dorsal anterior cingulate cortex activation and hippocampus deactivation. In patients, the aberrant salience bias to subjectively task-irrelevant information was increased and correlated with decreased striatal UPE activation and increased negative symptoms. This study shows that relevance estimates based on Bayesian precision can be inferred from observed behavior. This underscores the importance of relevance detection as an underlying mechanism for behavioral adaptation in complex environments and enhances the understanding of aberrant salience in schizophrenia.

Aberrant Salience Is Related to Dysfunctional Self-Referential Processing in Psychosis.

BACKGROUND: A dysfunctional differentiation between self-relevant and irrelevant information may affect the perception of environmental stimuli as abnormally salient. The aberrant salience hypothesis assumes that positive symptoms arise from an attribution of salience to irrelevant stimuli accompanied by the feeling of self-relevance. Self-referential processing relies on the activation of cortical midline structures which was demonstrated to be impaired in psychosis. We investigated the neural correlates of self-referential processing, aberrant salience attribution, and the relationship between these 2 measures across the psychosis continuum. METHODS: Twenty-nine schizophrenia patients, 24 healthy individuals with subclinical delusional ideation, and 50 healthy individuals participated in this study. Aberrant salience was assessed behaviorally in terms of reaction times to task irrelevant cues. Participants performed a self-reference task during fMRI in which they had to apply neutral trait words to them or to a public figure. The correlation between self-referential processing and aberrant salience attribution was tested. RESULTS: Schizophrenia patients displayed increased aberrant salience attribution compared with healthy controls and individuals with subclinical delusional ideation, while the latter exhibited intermediate aberrant salience scores. In the self-reference task, schizophrenia patients showed reduced activation in the ventromedial prefrontal cortex (vmPFC), but individuals with subclinical delusional ideation did not differ from healthy controls. In schizophrenia patients, vmPFC activation correlated negatively with implicit aberrant salience attribution. CONCLUSIONS: Higher aberrant salience attribution in schizophrenia patients is related to reduced vmPFC activation during self-referential judgments suggesting that aberrant relevance coding is reflected in decreased neural self-referential processing as well as in aberrant salience attribution.

Validating the construct of aberrant salience in schizophrenia - Behavioral evidence for an automatic process.

Suspecting significance behind ordinary events is a common feature in psychosis and it is assumed to occur due to aberrant salience attribution. The Salience Attribution Test (SAT; Roiser et al., 2009) measures aberrant salience as a bias towards one out of two equally reinforced cue features as opposed to adaptive salience towards features indicating high reinforcement. This is the first study to validate the latent constructs involved in salience attribution in patients. Forty-nine schizophrenia patients and forty-four healthy individuals completed the SAT, a novel implicit salience paradigm (ISP), a reversal learning task and a neuropsychological test battery. First, groups were compared on raw measures. Second and within patients, these were correlated and then used for a principal component analysis (PCA). Third, sum scores matching the correlation and component pattern were correlated with psychopathology. Compared to healthy individuals, patients exhibited more implicit aberrant salience in the SAT and ISP and less implicit and explicit adaptive salience attribution in the SAT. Implicit aberrant salience from the SAT and ISP positively correlated with each other and negatively with reversal learning. Whereas explicit aberrant salience was associated with cognition, implicit and explicit adaptive salience were positively correlated. A similar pattern emerged in the PCA and implicit aberrant salience was associated with negative symptoms. Taken together, implicit aberrant salience from the SAT and ISP seems to reflect an automatic process that is independent from deficient salience ascription to relevant events. Its positive correlation with negative symptoms might reflect motivational deficits present in chronic schizophrenia patients.

Aberrant Salience Is Related to Reduced Reinforcement Learning Signals and Elevated Dopamine Synthesis Capacity in Healthy Adults.

The striatum is known to play a key role in reinforcement learning, specifically in the encoding of teaching signals such as reward prediction errors (RPEs). It has been proposed that aberrant salience attribution is associated with impaired coding of RPE and heightened dopamine turnover in the striatum, and might be linked to the development of psychotic symptoms. However, the relationship of aberrant salience attribution, RPE coding, and dopamine synthesis capacity has not been directly investigated. Here we assessed the association between a behavioral measure of aberrant salience attribution, the salience attribution test, to neural correlates of RPEs measured via functional magnetic resonance imaging while healthy participants (n = 58) performed an instrumental learning task. A subset of participants (n = 27) also underwent positron emission tomography with the radiotracer [(18)F]fluoro-l-DOPA to quantify striatal presynaptic dopamine synthesis capacity. Individual variability in aberrant salience measures related negatively to ventral striatal and prefrontal RPE signals and in an exploratory analysis was found to be positively associated with ventral striatal presynaptic dopamine levels. These data provide the first evidence for a specific link between the constructs of aberrant salience attribution, reduced RPE processing, and potentially increased presynaptic dopamine function.