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.

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.

Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure.

Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach. We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.61% female). Participants underwent structural magnetic resonance imaging at age 14, binge drinking assessments at ages 14 and 22, and psychometric questionnaire assessments at ages 14 and 22. We derived structural brain features from T1-weighted magnetic resonance and diffusion tensor imaging. Using Machine Learning (ML), we predicted binge drinking (age 22) from brain structure (age 14) and used counterbalancing with oversampling to systematically control for 110 + variables from a wide range of social, personality, and other psychometric characteristics potentially associated with binge drinking. We evaluated if controlling for any variable resulted in a significant reduction in ML prediction accuracy. Sensation-seeking (-13.98 ± 1.68%), assessed via the Substance Use Risk Profile Scale at age 14, and uncontrolled eating (-13.98 ± 3.28%), assessed via the Three-Factor-Eating-Questionnaire at age 22, led to significant reductions in mean balanced prediction accuracy upon controlling for them. Thus, sensation-seeking and binge eating could partially explain the prediction of future binge drinking from adolescent brain structure. Our findings suggest that binge drinking and binge eating at age 22 share common neurobiological precursors discovered by the ML model. These neurobiological precursors seem to be associated with sensation-seeking at age 14. Our results facilitate early detection of increased risk for binge drinking and inform future clinical research in trans-diagnostic prevention approaches for adolescent alcohol misuse.

Elevated Amygdala Responses During De Novo Pavlovian Conditioning in Alcohol Use Disorder Are Associated With Pavlovian-to-Instrumental Transfer and Relapse Latency.

Background: Contemporary learning theories of drug addiction ascribe a key role to Pavlovian learning mechanisms in the development, maintenance, and relapse of addiction. In fact, cue-reactivity research has demonstrated the power of alcohol-associated cues to activate the brain's reward system, which has been linked to craving and subsequent relapse. However, whether de novo Pavlovian conditioning is altered in alcohol use disorder (AUD) has rarely been investigated. Methods: To characterize de novo Pavlovian conditioning in AUD, 62 detoxified patients with AUD and 63 matched healthy control participants completed a Pavlovian learning task as part of a Pavlovian-to-instrumental transfer paradigm during a functional magnetic resonance imaging session. Patients were followed up for 12 months to assess drinking behavior and relapse status. Results: While patients and healthy controls did not differ in their ability to explicitly acquire the contingencies between conditioned and unconditioned stimuli, patients with AUD displayed significantly stronger amygdala responses toward Pavlovian cues, an effect primarily driven by stronger blood oxygen level-dependent differentiation during learning from reward compared with punishment. Moreover, in patients compared with controls, differential amygdala responses during conditioning were positively related to the ability of Pavlovian stimuli to influence ongoing instrumental choice behavior measured during a subsequent Pavlovian-to-instrumental transfer test. Finally, patients who relapsed within the 12-month follow-up period showed an inverse association between amygdala activity during conditioning and relapse latency. Conclusions: We provide evidence of altered neural correlates of de novo Pavlovian conditioning in patients with AUD, especially for appetitive stimuli. Thus, heightened processing of Pavlovian cues might constitute a behaviorally relevant mechanism in alcohol addiction.

Influence of noninvasive brain stimulation on connectivity and local activation: a combined tDCS and fMRI study.

The effect of transcranial direct current stimulation (tDCS) on neurobiological mechanisms underlying executive function in the human brain remains elusive. This study aims at examining the effect of anodal and cathodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) in comparison with sham stimulation on resting-state connectivity as well as functional activation and working memory performance. We hypothesized perturbed fronto-parietal resting-state connectivity during stimulation and altered working memory performance combined with modified functional working memory-related activation. We applied tDCS with 1 mA for 21 min over the DLPFC inside an fMRI scanner. During stimulation, resting-state fMRI was acquired and task-dependent fMRI during working memory task performance was acquired directly after stimulation. N = 36 healthy subjects were studied in a within-subject design with three different experimental conditions (anodal, cathodal and sham) in a double-blind design. Seed-based functional connectivity analyses and dynamic causal modeling were conducted for the resting-state fMRI data. We found a significant stimulation by region interaction in the seed-based ROI-to-ROI resting-state connectivity, but no effect on effective connectivity. We also did not find an effect of stimulation on task-dependent signal alterations in working memory activation in our regions of interest and no effect on working memory performance parameters. We found effects on measures of seed-based resting-state connectivity, while measures of effective connectivity and task-based connectivity did not show any stimulation effect. We could not replicate previous findings of tDCS stimulation effects on behavioral outcomes. We critically discuss possible methodological limitations and implications for future studies.

Alcohol Approach Bias Is Associated With Both Behavioral and Neural Pavlovian-to-Instrumental Transfer Effects in Alcohol-Dependent Patients.

Background: Even after qualified detoxification, alcohol-dependent (AD) patients may relapse to drinking alcohol despite their decision to abstain. Two mechanisms may play important roles. First, the impact of environmental cues on instrumental behavior (i.e., Pavlovian-to-instrumental transfer [PIT] effect), which was found to be stronger in prospectively relapsing AD patients than in abstaining patients. Second, an automatic approach bias toward alcohol stimuli was observed in AD patients, and interventions targeting this bias reduced the relapse risk in some studies. Previous findings suggest a potential behavioral and neurobiological overlap between these two mechanisms. Methods: In this study, we examined the association between alcohol approach bias and both behavioral and neural non-drug-related PIT effects in AD patients after detoxification. A total of 100 AD patients (17 females) performed a PIT task and an alcohol approach/avoidance task. Patients were followed for 6 months. Results: A stronger alcohol approach bias was associated with both a more pronounced behavioral PIT effect and stronger PIT-related neural activity in the right nucleus accumbens. Moreover, the association between alcohol approach bias and behavioral PIT increased with the severity of alcohol dependence and trait impulsivity and was stronger in patients who relapsed during follow-up in the exploratory analysis. Conclusions: These findings indicate partial behavioral and neurobiological overlap between alcohol approach bias and the PIT effect assessed with our tasks. The association was stronger in patients with more severe alcohol dependence.

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.

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.

The Association of Non-Drug-Related Pavlovian-to-Instrumental Transfer Effect in Nucleus Accumbens With Relapse in Alcohol Dependence: A Replication.

BACKGROUND: The Pavlovian-to-instrumental transfer (PIT) paradigm measures the effects of Pavlovian conditioned cues on instrumental behavior in the laboratory. A previous study conducted by our research group observed activity in the left nucleus accumbens (NAcc) elicited by a non-drug-related PIT task across patients with alcohol dependence (AD) and healthy control subjects, and the left NAcc PIT effect differentiated patients who subsequently relapsed from those who remained abstinent. In this study, we aimed to examine whether such effects were present in a larger sample collected at a later date. METHODS: A total of 129 recently detoxified patients with AD (21 females) and 74 healthy, age- and gender-matched control subjects (12 females) performing a PIT task during functional magnetic resonance imaging were examined. After task assessments, patients were followed for 6 months. Forty-seven patients relapsed and 37 remained abstinent. RESULTS: We found a significant behavioral non-drug-related PIT effect and PIT-related activity in the NAcc across all participants. Moreover, subsequent relapsers showed stronger behavioral and left NAcc PIT effects than abstainers. These findings are consistent with our previous findings. CONCLUSIONS: Behavioral non-drug-related PIT and neural PIT correlates are associated with prospective relapse risk in AD. This study replicated previous findings and provides evidence for the clinical relevance of PIT mechanisms to treatment outcome in AD. The observed difference between prospective relapsers and abstainers in the NAcc PIT effect in our study is small overall. Future studies are needed to further elucidate the mechanisms and the possible modulators of neural PIT in relapse in AD.

Goal-Directed and Habitual Control in Human Substance Use: State of the Art and Future Directions.

Theories of addiction posit a deficit in goal-directed behavior and an increased propensity toward habitual actions in individuals with substance use disorders. Control over drug intake is assumed to shift from goal-directed to automatic or habitual motivation as the disorder progresses. Several diagnostic criteria reflect the inability to pursue goals regarding reducing or controlling drug use and performing social or occupational functions. The current review gives an overview of the mechanisms underlying the goal-directed and habitual systems in humans, and the existing paradigms that aim to evaluate them. We further summarize the current state of research on habitual and goal-directed functioning in individuals with substance use disorders. Current evidence of alterations in addiction and substance use are mixed and need further investigation. Increased habitual responding has been observed in more severely affected groups with contingency degradation and some outcome devaluation tasks. Reduced model-based behavior has been mainly observed in alcohol use disorder and related to treatment outcomes. Motor sequence learning tasks might provide a promising new approach to examine the development of habitual behavior. In the final part of the review, we discuss possible implications and further developments regarding the influence of contextual factors, such as state and trait variations, and recent advances in task design.

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.

Anodal tDCS over the medial prefrontal cortex enhances behavioral adaptation after punishments during reversal learning through increased updating of unchosen choice options.

The medial prefrontal cortex (mPFC) is thought to be central for flexible behavioral adaptation. However, the causal relationship between mPFC activity and this behavior is incompletely understood. We investigated whether transcranial direct current stimulation (tDCS) over the mPFC alters flexible behavioral adaptation during reward-based decision-making, targeting Montreal Neurological Institute (MNI) coordinates X = -8, Y = 62, Z = 12, which has previously been associated with impaired behavioral adaptation in alcohol-dependent patients. Healthy human participants (n = 61) received either anodal (n = 30) or cathodal (n = 31) tDCS versus sham tDCS while performing a reversal learning task. To assess the mechanisms of reinforcement learning (RL) underlying our behavioral observations, we applied computational models that varied with respect to the updating of the unchosen choice option. We observed that anodal stimulation over the mPFC induced increased choice switching after punishments compared with sham stimulation, whereas cathodal stimulation showed no effect on participants' behavior compared with sham stimulation. RL revealed increased updating of the unchosen choice option under anodal as compared with sham stimulation, which accounted well for the increased tendency to switch after punishments. Our findings provide a potential model for tDCS interventions in conditions related to flexible behavioral adaptation, such as addiction.

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

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

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD).

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD.

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

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

Stronger Prejudices Are Associated With Decreased Model-Based Control.

Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or "automatic" reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making. Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale. Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices. Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.

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.