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Importance: Antidepressant discontinuation substantially increases the risk of a depression relapse, but the neurobiological mechanisms through which this happens are not known. Amygdala reactivity to negative information is a marker of negative affective processes in depression that is reduced by antidepressant medication, but it is unknown whether amygdala reactivity is sensitive to antidepressant discontinuation or whether any change is related to the risk of relapse after antidepressant discontinuation., Objective: To investigate whether amygdala reactivity to negative facial emotions changes with antidepressant discontinuation and is associated with subsequent relapse., Design, Setting, and Participants: The Antidepressiva Absetzstudie (AIDA) study was a longitudinal, observational study in which adult patients with remitted major depressive disorder (MDD) and currently taking antidepressants underwent 2 task-based functional magnetic resonance imaging (fMRI) measurements of amygdala reactivity. Patients were randomized to discontinuing antidepressants either before or after the second fMRI measurement. Relapse was monitored over a 6-month follow-up period. Study recruitment took place from June 2015 to January 2018. Data were collected between July 1, 2015, and January 31, 2019, and statistical analyses were conducted between June 2021 and December 2023. The study took place in a university setting in Zurich, Switzerland, and Berlin, Germany. Of 123 recruited patients, 83 were included in analyses. Of 66 recruited healthy control individuals matched for age, sex, and education, 53 were included in analyses., Exposure: Discontinuation of antidepressant medication., Outcomes: Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months after discontinuation., Results: Among patients with MDD, the mean (SD) age was 35.42 (11.41) years, and 62 (75%) were women. Among control individuals, the mean (SD) age was 33.57 (10.70) years, and 37 (70%) were women. Amygdala reactivity of patients with remitted MDD and taking medication did not initially differ from that of control individuals (t125.136 = 0.33; P = .74). An increase in amygdala reactivity after antidepressant discontinuation was associated with depression relapse (3-way interaction between group [12W (waited) vs 1W2 (discontinued)], time point [MA1 (first scan) vs MA2 (second scan)], and relapse: β, 18.9; 95% CI, 0.8-37.1; P = .04). Amygdala reactivity change was associated with shorter times to relapse (hazard ratio, 1.05; 95% CI, 1.01-1.09; P = .01) and predictive of relapse (leave-one-out cross-validation balanced accuracy, 67%; 95% posterior predictive interval, 53-80; P = .02)., Conclusions and Relevance: An increase in amygdala reactivity was associated with risk of relapse after antidepressant discontinuation and may represent a functional neuroimaging marker that could inform clinical decisions around antidepressant discontinuation.

Current nosology claims to separate mental disorders into distinct categories that do not overlap with each other. This nosological separation is not based on underlying pathophysiology but on convention-based clustering of qualitative symptoms of disorders which are typically measured subjectively. Yet, clinical heterogeneity and diagnostic overlap in disease symptoms and dimensions within and across different diagnostic categories of mental disorders is huge. While diagnostic categories provide the basis for general clinical management, they do not describe the underlying neurobiology that gives rise to individual symptomatic presentations. The ability to incorporate neurobiology into the diagnostic framework and to stratify patients accordingly will be a critical step forward for the development of new treatments for mental disorders. Furthermore, it will also allow physicians to provide patients with a better understanding of their illness's complexities and management. To realize this ambition, a paradigm shift is needed to build an understanding of how neuropsychiatric conditions can be defined more precisely using quantitative (multimodal) biological processes and markers and thus to significantly improve treatment success. The ECNP New Frontiers Meeting 2024 set out to develop a consensus roadmap for building a new diagnostic framework for mental disorders by discussing its rationale, outlook, and consequences with all stakeholders involved. This framework would instantiate a set of principles and procedures by which research could continuously improve precision diagnostics while moving away from traditional nosology. In this meeting report, the speakers' summaries from their presentations are combined to address three key elements for generating such a roadmap, namely, the application of innovative technologies, understanding the biology of mental illness, and translating biological understanding into new approaches. In general, the meeting indicated a crucial need for a biology-informed framework to establish more precise diagnosis and treatment for mental disorders to facilitate bringing the right treatment to the right patient at the right time., Competing Interests: Declaration of competing interest MJHK, DHM, MH, and BC declare no conflict of interest. SH is member of the scientific advisory boards for Johnson and Johnson (formerly Janssen) and F-Prime Capital, and he is member of the Boards of Directors for Voyager Therapeutics, Cyclerion Therapeutics, and Vesalius Therapeutics. LMW discloses the following patents in which she contributed, namely, US Patent App. 16/921,388, 16/368,774, 10,702,232 B2, 15/997,631, 10,285,658, 15/830,338, 10,034,645. LJdP declared consultancy and paid presentations for Boeringer-Ingelheim, paid presentation for Viatris – all outside the current work. LJdP is also the Co-chair of the ECNP Immuno-NeuroPsychiatry Network. CML is SAB for Myriad Neuroscience. Speaker & Consultancy fees from UCB and SYNLAB. AE receives salary and equity from Alto Neuroscience, and equity from Akili Interactive. PAL has received honoraria for talks presented at educational meetings organised by Boehringer-Ingelheim outside of the presented work. QJMH has obtained fees and options for consultancies for Aya Technologies and Alto Neuroscience. HM is a full-time employee of Boehringer Ingelheim Pharma GmbH., (Copyright © 2024. Published by Elsevier B.V.)

Background: Mood disorders involve a complex interplay between multifaceted internal emotional states, and complex external inputs. Dynamical systems theory suggests that this interplay between aspects of moods and environmental stimuli may hence determine key psychopathological features of mood disorders, including the stability of mood states, the response to external inputs, how controllable mood states are, and what interventions are most likely to be effective. However, a comprehensive computational approach to all these aspects has not yet been undertaken., Methods: Here, we argue that the combination of ecological momentary assessments (EMA) with a well-established dynamical systems framework-the humble Kalman filter-enables a comprehensive account of all these aspects. We first introduce the key features of the Kalman filter and optimal control theory and their relationship to aspects of psychopathology. We then examine the psychometric and inferential properties of combining EMA data with Kalman filtering across realistic scenarios. Finally, we apply the Kalman filter to a series of EMA datasets comprising over 700 participants with and without symptoms of depression., Results: The results show a naive Kalman filter approach performs favourably compared to the standard vector autoregressive approach frequently employed, capturing key aspects of the data better. Furthermore, it suggests that the depressed state involves alterations to interactions between moods; alterations to how moods responds to external inputs; and as a result an alteration in how controllable mood states are. We replicate these findings qualitatively across datasets and explore an extension to optimal control theory to guide therapeutic interventions., Conclusions: Mood dynamics are richly and profoundly altered in depressed states. The humble Kalman filter is a well-established, rich framework to characterise mood dynamics. Its application to EMA data is valid; straightforward; and likely to result in substantial novel insights both into mechanisms and treatments., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: QJMH has obtained a research grant from Koa Health, and obtained fees and options for consultancies for Aya Technologies and Alto Neuroscience. All other authors report no conflicts of interest., (Copyright: © 2024 Malamud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED, with one including additional psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. These data showcase a new methodology to analyse data from the CANTAB IED task, as well as suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms., (© 2024. The Author(s).)

Background: One in 3 patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of major depressive disorder. However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by functional magnetic resonance imaging has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor., Methods: Patients (n = 56) who had remitted from a depressive episode while taking antidepressants underwent electroencephalography (EEG) recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a 6-month follow-up period. Thirty five healthy control participants were also tested. Current source density of the EEG power in the alpha band (8-13 Hz) was extracted and alpha asymmetry was computed by comparing the power across 2 hemispheres at frontal electrodes (F5 and F6)., Results: Sad mood induction was robust across all groups. Reactivity of alpha asymmetry to sad mood did not distinguish healthy control participants from patients with remitted major depressive disorder on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in alpha asymmetry compared with patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy and a positive predictive value of 0.75)., Conclusions: A simple EEG-based measure of emotional reactivity may have potential to contribute to clinical prediction models of antidepressant discontinuation. Given the very small sample size, this finding must be interpreted with caution and requires replication in a larger study., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Selective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological treatments for depression and anxiety. However, little is known about how pharmacological action is related to cognitive and affective processes. Here, we examine whether specific reinforcement learning processes mediate the treatment effects of SSRIs., Methods: The PANDA trial was a multicentre, double-blind, randomized clinical trial in UK primary care comparing the SSRI sertraline with placebo for depression and anxiety. Participants ( N = 655) performed an affective Go/NoGo task three times during the trial and computational models were used to infer reinforcement learning processes., Results: There was poor task performance: only 54% of the task runs were informative, with more informative task runs in the placebo than in the active group. There was no evidence for the preregistered hypothesis that Pavlovian inhibition was affected by sertraline. Exploratory analyses revealed that in the sertraline group, early increases in Pavlovian inhibition were associated with improvements in depression after 12 weeks. Furthermore, sertraline increased how fast participants learned from losses and faster learning from losses was associated with more severe generalized anxiety symptoms., Conclusions: The study findings indicate a relationship between aversive reinforcement learning mechanisms and aspects of depression, anxiety, and SSRI treatment, but these relationships did not align with the initial hypotheses. Poor task performance limits the interpretability and likely generalizability of the findings, and highlights the critical importance of developing acceptable and reliable tasks for use in clinical studies., Funding: This article presents research supported by NIHR Program Grants for Applied Research (RP-PG-0610-10048), the NIHR BRC, and UCL, with additional support from IMPRS COMP2PSYCH (JM, QH) and a Wellcome Trust grant (QH).

Current theories suggest individuals with methamphetamine use disorder (iMUDs) have difficulty considering long-term outcomes in decision-making, which could contribute to risk of relapse. Aversive interoceptive states (e.g., stress, withdrawal) are also known to increase this risk. The present study analyzed computational mechanisms of planning in iMUDs, and examined the potential impact of an aversive interoceptive state induction. A group of 40 iMUDs and 49 healthy participants completed two runs of a multi-step planning task, with and without an anxiogenic breathing resistance manipulation. Computational modeling revealed that iMUDs had selective difficulty identifying the best overall plan when this required enduring negative short-term outcomes - a mechanism referred to as aversive pruning. Increases in reported craving before and after the induction also predicted greater aversive pruning in iMUDs. These results highlight a novel mechanism that could promote poor choice in recovering iMUDs and create vulnerability to relapse., Competing Interests: Conflict of interest or competing financial interests The authors have no conflicts of interest to report.

Anhedonia is a reduction in enjoyment, motivation, or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed 'consummatory anhedonia', in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement learning (RL) account of consummatory anhedonia with a reconceptualisation of subjective hedonic experience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia., Competing Interests: Declaration of interests Q.H. has received options and consultancy fees from Aya Health, Alto Neuroscience, and a research grant from Koa Health. M.B. has acted as a consultant for Janssen Research, P1vital Ltd, Boehringer, and CHDR and was previously a paid employee of P1vital Ltd. The remaining author has no interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

When making choices, individuals differ from one another, as well as from normativity, in how they weigh different types of information. One explanation for this relates to idiosyncratic preferences in what information individuals represent when evaluating choice options. Here, we test this explanation with a simple risky-decision making task, combined with magnetoencephalography (MEG). We examine the relationship between individual differences in behavioral markers of information weighting and neural representation of stimuli pertinent to incorporating that information. We find that the extent to which individuals (N = 19) behaviorally weight probability versus reward information is related to how preferentially they neurally represent stimuli most informative for making probability and reward comparisons. These results are further validated in an additional behavioral experiment (N = 88) that measures stimulus representation as the latency of perceptual detection following priming. Overall, the results suggest that differences in the information individuals consider during choice relate to their risk-taking tendencies., (© 2024. The Author(s).)

Psychological therapies are among the most effective treatments for common mental health problems-however, we still know relatively little about how exactly they improve symptoms. Here, we demonstrate the power of combining theory with computational methods to parse effects of different components of cognitive-behavioral therapies onto underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the effects of brief components of behavioral and cognitive therapies on different cognitive processes, using well-validated behavioral measures and associated computational models. A goal setting intervention, based on behavioral activation therapy activities, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, a cognitive restructuring intervention, based on cognitive therapy materials, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. The effects of each intervention were specific to these respective measures. Our approach provides a basis for beginning to understand how different elements of common psychotherapy programs may work.

Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings. In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach-which we refer to as "cognitive microscopy"-that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (© 2023 The Authors.)

Pavlovian cues can influence ongoing instrumental behaviour via Pavlovian-to-instrumental transfer (PIT) processes. While appetitive Pavlovian cues tend to promote instrumental approach, they are detrimental when avoidance behaviour is required, and vice versa for aversive cues. We recently reported that susceptibility to interference between Pavlovian and instrumental control assessed via a PIT task was associated with risky alcohol use at age 18. We now investigated whether such susceptibility also predicts drinking trajectories until age 24, based on AUDIT (Alcohol Use Disorders Identification Test) consumption and binge drinking (gramme alcohol/drinking occasion) scores. The interference PIT effect, assessed at ages 18 and 21 during fMRI, was characterized by increased error rates (ER) and enhanced neural responses in the ventral striatum (VS), the lateral and dorsomedial prefrontal cortices (dmPFC) during conflict, that is, when an instrumental approach was required in the presence of an aversive Pavlovian cue or vice versa. We found that a stronger VS response during conflict at age 18 was associated with a higher starting point of both drinking trajectories but predicted a decrease in binge drinking. At age 21, high ER and enhanced neural responses in the dmPFC were associated with increasing AUDIT-C scores over the next 3 years until age 24. Overall, susceptibility to interference between Pavlovian and instrumental control might be viewed as a predisposing mechanism towards hazardous alcohol use during young adulthood, and the identified high-risk group may profit from targeted interventions., (© 2023 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Background: Violent offenders with psychopathic tendencies are characterized by instrumental, i.e., planned, callous, and unemotional (aggressive) behavior and have been shown to exhibit abnormal aversive processing. However, the consequences of abnormal aversive processing for instrumental action and associated neural mechanisms are unclear., Materials and Methods: Here we address this issue by using event-related functional magnetic resonance imaging (fMRI) in 15 violent offenders with high psychopathic tendencies and 18 matched controls during the performance of an aversive Pavlovian-to-instrumental transfer paradigm. This paradigm allowed us to assess the degree to which aversive Pavlovian cues affect instrumental action and associated neural signaling., Results: Psychopathic tendency scores were associated with an attenuation of aversive Pavlovian inhibition of instrumental action. Moreover, exploratory analyses revealed an anomalous positive association between aversive inhibition of action and aversive inhibition of BOLD signal in the caudate nucleus of violent offenders with psychopathic tendencies. In addition, psychopathic tendency also correlated positively with amygdala reactivity during aversive versus neutral cues in Pavlovian training., Conclusion: These findings strengthen the hypothesis that psychopathic tendencies in violent offenders are related to abnormal impact of aversive processing on instrumental behavior. The neural effects raise the possibility that this reflects deficient transfer of aversive Pavlovian inhibitory biases onto neural systems that implement instrumental action, including the caudate nucleus., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Geurts, von Borries, Huys, Bulten, Verkes and Cools.)

Background: Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms., Objective: To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation., Method: The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models., Results: Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia., Conclusions: In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response., Trial Registry Name: Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov., (Copyright: © 2022 The Author(s).)

Borderline personality disorder (BPD) is a prevalent, devastating, and heterogeneous psychiatric disorder. Treatment success is highly variable within this patient group. A cognitive neuroscientific approach to BPD might contribute to precision psychiatry by identifying neurocognitive factors that predict who will benefit from a specific treatment. Here, we build on observations that BPD is accompanied by the enhanced impact of the aversive effect on behavior and abnormal neural signaling in the amygdala. We assessed whether BPD is accompanied by abnormal aversive regulation of instrumental behavior and associated neural signaling, in a manner that is predictive of symptom reduction after therapy. We tested a clinical sample of 15 female patients with BPD, awaiting dialectical behavior therapy (DBT), and 16 matched healthy controls using fMRI and an aversive Pavlovian-to-instrumental transfer (PIT) task that assesses how instrumental behaviors are influenced by aversive Pavlovian stimuli. Patients were assessed 1 year after the start of DBT to quantify changes in BPD symptom severity. At baseline, behavioral aversive PIT and associated neural signaling did not differ between groups. However, the BOLD signal in the amygdala measured during aversive PIT was associated with symptom reduction at 1-year follow-up: higher PIT-related aversive amygdala signaling before treatment was associated with reduced clinical improvement at follow-up. Thus, within the evaluated group of BPD patients, the BOLD signal in the amygdala before treatment was related to clinical symptom reduction 1 year after the start of treatment. The results suggest that less PIT-related responsiveness of the amygdala increases the chances of treatment success. We note that the relatively small sample size is a limitation of this study and that replication is warranted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Geurts, Van den Heuvel, Huys, Verkes and Cools.)

The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making., (© 2022. The Author(s).)

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., (© 2022 The Authors.)

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses)., (© 2023 Published by Elsevier Inc on behalf of Society of Biological Psychiatry.)

Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources toward goals that were less abundant. In general, participants ( n = 192) were less flexible in avoiding punishment than in pursuing reward. Thus, when instructed to pursue reward, participants often persisted in avoiding features that had previously been associated with punishment, even though at decision time these features were unambiguously benign. In a similar vein, participants showed no significant downregulation of avoidance when punishment avoidance goals were less abundant in the task. Importantly, we show preliminary evidence that individuals with chronic worry may have difficulty disengaging from punishment avoidance when instructed to seek reward. Taken together, the findings demonstrate that people avoid punishment less flexibly than they pursue reward. Future studies should test in larger samples whether a difficulty to disengage from punishment avoidance contributes to chronic worry., Competing Interests: PS, ER, QH, RD, EE No competing interests declared, (© 2022, Sharp et al.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sebold, Chen, Önal, Kuitunen-Paul, Mojtahedzadeh, Garbusow, Nebe, Wittchen, Huys, Schlagenhauf, Rapp, Smolka and Heinz.)

Anhedonia - a common feature of depression and other neuropsychiatric disorders - encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide an explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multi-dimensional drive-reduction settings through a trade-off between different rewards or needs. We then generalise this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment., (© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Over the past three decades, functional magnetic resonance imaging (fMRI) has become crucial to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (N = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (N = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared with full in-person psychiatric screening), recruited at least partly from convenience samples (compared with community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias., (© The Author(s) 2021. Published by Oxford University Press.)

This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions., (Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model's uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible., Competing Interests: The authors have declared that no competing interests exist.

Pavlovian-to-instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high-risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18-year-old male adults. Participants (N = 191) were instructed to 'collect good shells' and 'leave bad shells' during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high-risk and low-risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high-risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high-risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High-risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom-up), decreased lPFC response (top-down), and their altered interplay may contribute to poor interference control in the high-risk drinkers., (© 2020 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Background: A shift from goal-directed toward habitual control has been associated with alcohol dependence. Whether such a shift predisposes to risky drinking is not yet clear. We investigated how goal-directed and habitual control at age 18 predict alcohol use trajectories over the course of 3 years., Methods: Goal-directed and habitual control, as informed by model-based (MB) and model-free (MF) learning, were assessed with a two-step sequential decision-making task during functional magnetic resonance imaging in 146 healthy 18-year-old men. Three-year alcohol use developmental trajectories were based on either a consumption score from the self-reported Alcohol Use Disorders Identification Test (assessed every 6 months) or an interview-based binge drinking score (grams of alcohol/occasion; assessed every year). We applied a latent growth curve model to examine how MB and MF control predicted the drinking trajectory., Results: Drinking behavior was best characterized by a linear trajectory. MB behavioral control was negatively associated with the development of the binge drinking score; MF reward prediction error blood oxygen level-dependent signals in the ventromedial prefrontal cortex and the ventral striatum predicted a higher starting point and steeper increase of the Alcohol Use Disorders Identification Test consumption score over time, respectively., Conclusions: We found that MB behavioral control was associated with the binge drinking trajectory, while the MF reward prediction error signal was closely linked to the consumption score development. These findings support the idea that unbalanced MB and MF control might be an important individual vulnerability in predisposing to risky drinking behavior., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression., Methods: Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression., Results: Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression., Conclusions: Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.

Background: Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people 'prune' (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals., Methods: Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms., Results: Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety., Conclusions: We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm in 39 healthy human participants (23 female) after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that taVNS acutely impaired decision-making, p = .041. Computational reinforcement learning models identified the cause of this as a reduction in the learning rate through taVNS (∆α = -0.092, p boot  = .002), particularly after punishment (∆α Pun = -0.081, p boot  = .012 vs. ∆α Rew =-0.031, p boot  = .22). However, taVNS had no effect on go biases, Pavlovian response biases or response time. Hence, taVNS appeared to influence learning rather than action execution. These results highlight a novel role of vagal afferent input in modulating reinforcement learning by tuning the learning rate according to homeostatic needs., (Copyright © 2020. Published by Elsevier B.V.)

Importance: Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario., Objectives: To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation., Design, Setting, and Participants: This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018., Exposure: Discontinuation of antidepressants., Main Outcomes and Measures: Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model., Results: A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1,98 = 33.970; P < .001). Computational modeling identified a mean (SD) deviation from standard drift-diffusion models that was more prominent for patients than controls (patients, 0.67 [1.56]; controls, -0.71 [1.93]; Cohen d = 0.82; P < .001). Patients also showed higher mean (SD) effort sensitivity than controls (patients, 0.31 [0.92]; controls, -0.08 [1.03]; Cohen d = 0.51; P = .05). Relapsers differed from nonrelapsers in terms of the evidence required to make a decision for the low-effort choice (mean [SD]: relapsers, 1.36 [0.35]; nonrelapsers, 1.17 [0.26]; Cohen d = 0.65; P = .02). Group differences generally did not reach significance in the smaller replication sample (27 patients and 21 controls), but decision time prediction models from the main sample generalized to the replication sample (validation accuracy = 0.71; P = .03)., Conclusions and Relevance: This study found that the decision to invest effort was associated with prospective relapse risk after antidepressant discontinuation and may represent a persistent disease process in asymptomatic remitted major depressive disorder. Markers based on effort-related decision-making could potentially inform clinical decisions associated with antidepressant discontinuation.

We discuss opportunities in applying the resource-rationality framework toward answering questions in emotion and mental health research. These opportunities rely on characterization of individual differences in cognitive strategies; an endeavor that may be at odds with the normative approach outlined in the target article. We consider ways individual differences might enter the framework and the translational opportunities offered by each.

Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals-called sign-trackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli 'wanted'. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.

Human decisions can be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control. Previous work suggests that the balance between the two decision systems is impaired in psychiatric disorders such as compulsion and addiction, via overreliance on MF control. However, little is known whether the balance can be altered through task training. Here, 20 healthy participants performed a well-established two-step task that differentiates MB from MF control, across five training sessions. We used computational modelling and functional near-infrared spectroscopy to assess changes in decision-making and brain hemodynamic over time. Mixed-effects modelling revealed overall no substantial changes in MF and MB behavior across training. Although our behavioral and brain findings show task-induced changes in learning rates, these parameters have no direct relation to either MF or MB control or the balance between the two systems, and thus do not support the assumption of training effects on MF or MB strategies. Our findings indicate that training on the two-step paradigm in its current form does not support a shift in the balance between MF and MB control. We discuss these results with respect to implications for restoring the balance between MF and MB control in psychiatric conditions., Competing Interests: The authors have declared that no competing interests exist.

In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men ( n = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers ( b = 0.09, SE = 0.03, z = 2.7, p < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala ( t = 3.25, p SVC = 0.04, x = 26, y = -6, z = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption ( r s = 0.17, p = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.

The influence of Pavlovian conditioned stimuli on ongoing behavior may contribute to explaining how alcohol cues stimulate drug seeking and intake. Using a Pavlovian-instrumental transfer task, we investigated the effects of alcohol-related cues on approach behavior (i.e., instrumental response behavior) and its neural correlates, and related both to the relapse after detoxification in alcohol-dependent patients. Thirty-one recently detoxified alcohol-dependent patients and 24 healthy controls underwent instrumental training, where approach or non-approach towards initially neutral stimuli was reinforced by monetary incentives. Approach behavior was tested during extinction with either alcohol-related or neutral stimuli (as Pavlovian cues) presented in the background during functional magnetic resonance imaging (fMRI). Patients were subsequently followed up for 6 months. We observed that alcohol-related background stimuli inhibited the approach behavior in detoxified alcohol-dependent patients (t = - 3.86, p < .001), but not in healthy controls (t = - 0.92, p = .36). This behavioral inhibition was associated with neural activation in the nucleus accumbens (NAcc) (t (30)  = 2.06, p < .05). Interestingly, both the effects were only present in subsequent abstainers, but not relapsers and in those with mild but not severe dependence. Our data show that alcohol-related cues can acquire inhibitory behavioral features typical of aversive stimuli despite being accompanied by a stronger NAcc activation, suggesting salience attribution. The fact that these findings are restricted to abstinence and milder illness suggests that they may be potential resilience factors.Clinical trial: LeAD study, http://www.lead-studie.de , NCT01679145.

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants' behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty., (© 2018 Cognitive Science Society, Inc.)

Background: Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here, we examined whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that obsessive and compulsive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN activity and tested if real-time fMRI (rtfMRI) neurofeedback (NFB) can improve self-regulation of the VTA/SN., Methods: Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with non-drug reward imagery alone, or combined with rtfMRI NFB., Results: On a group level, HC and CU were able to activate the dopaminergic midbrain and other reward regions with reward imagery. In CU, the individual ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of reward imagery but did not result in transfer effects at the end of the session., Conclusion: CU can voluntary activate their reward system with non-drug reward imagery and improve this ability with rtfMRI NFB. Combining mental imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the use of rtfMRI NFB in the treatment of CUD., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Depression is a debilitating condition with a high prevalence. Depressed patients have been shown to be diminished in their ability to integrate their reinforcement history to adjust future behaviour during instrumental reward learning tasks. Here, we tested whether such impairments could also be observed in a Pavlovian conditioning task. We recruited and analysed 32 subjects, 15 with depression and 17 healthy controls, to study behavioural group differences in learning and decision-making. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few passive observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants' behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Depressed subjects showed lower memory of observed rewards and had an impaired ability to use internal value estimations to guide decision-making in our task.

Adaptive coding of information is a fundamental principle of brain functioning. It allows for efficient representation over a large range of inputs and thereby alleviates the limited coding range of neurons. In the present study, we investigated for the first time potential alterations in context-dependent reward adaptation and its association with symptom dimensions in the schizophrenia spectrum. We studied 27 patients with first-episode psychosis, 26 individuals with schizotypal personality traits and 25 healthy controls. We used functional MRI in combination with a variant of the monetary incentive delay task and assessed adaptive reward coding in two reward conditions with different reward ranges. Compared to healthy controls, patients with first-episode psychosis and healthy individuals with schizotypal personality traits showed a deficit in increasing the blood oxygen level-dependent response slope in the right caudate for the low reward range compared to the high reward range. In other words, the two groups showed inefficient neural adaptation to the current reward context. In addition, we found impaired adaptive coding of reward in the caudate nucleus and putamen to be associated with total symptom severity across the schizophrenia spectrum. Symptom severity was more strongly associated with neural deficits in adaptive coding than with the neural coding of absolute reward outcomes. Deficits in adaptive coding were prominent across the schizophrenia spectrum and even detectable in unmedicated (healthy) individuals with schizotypal personality traits. Furthermore, the association between total symptom severity and impaired adaptive coding in the right caudate and putamen suggests a dimensional mechanism underlying imprecise neural adaptation. Our findings support the idea that impaired adaptive coding may be a general information-processing deficit explaining disturbances within the schizophrenia spectrum over and above a simple model of blunted absolute reward signals.

The dynamic modulation of instrumental behaviour by conditioned Pavlovian cues is an important process in decision-making. Patients with major depressive disorder (MDD) are known to exhibit mood-congruent biases in information processing, which may occur due to Pavlovian influences, but this hypothesis has never been tested directly in an unmedicated sample. To address this we tested unmedicated MDD patients and healthy volunteers on a computerized Pavlovian-Instrumental Transfer (PIT) task designed to separately examine instrumental approach and withdrawal actions in the context of Pavlovian appetitive and aversive cues. This design allowed us to directly measure the degree to which Pavlovian cues influence instrumental responding. Depressed patients were profoundly influenced by aversive Pavlovian stimuli, to a significantly greater degree than healthy volunteers. This was the case for instrumental behaviour both in the approach condition (in which aversive Pavlovian cues inhibited 'go' responses), and in the withdrawal condition (in which aversive Pavlovian cues facilitated 'go' responses). Exaggerated aversive PIT provides a potential cognitive mechanism for biased emotion processing in major depression. This finding also has wider significance for the understanding of disrupted motivational processing in neuropsychiatric disorders.