Impaired flexible reward learning in ADHD patients is associated with blunted reinforcement sensitivity and neural signals in ventral striatum and parietal cortex.

Reward-based learning and decision-making are prime candidates to understand symptoms of attention deficit hyperactivity disorder (ADHD). However, only limited evidence is available regarding the neurocomputational underpinnings of the alterations seen in ADHD. This concerns flexible behavioral adaption in dynamically changing environments, which is challenging for individuals with ADHD. One previous study points to elevated choice switching in adolescent ADHD, which was accompanied by disrupted learning signals in medial prefrontal cortex. Here, we investigated young adults with ADHD (n = 17) as compared to age- and sex-matched controls (n = 17) using a probabilistic reversal learning experiment during functional magnetic resonance imaging (fMRI). The task requires continuous learning to guide flexible behavioral adaptation to changing reward contingencies. To disentangle the neurocomputational underpinnings of the behavioral data, we used reinforcement learning (RL) models, which informed the analysis of fMRI data. ADHD patients performed worse than controls particularly in trials before reversals, i.e., when reward contingencies were stable. This pattern resulted from 'noisy' choice switching regardless of previous feedback. RL modelling showed decreased reinforcement sensitivity and enhanced learning rates for negative feedback in ADHD patients. At the neural level, this was reflected in a diminished representation of choice probability in the left posterior parietal cortex in ADHD. Moreover, modelling showed a marginal reduction of learning about the unchosen option, which was paralleled by a marginal reduction in learning signals incorporating the unchosen option in the left ventral striatum. Taken together, we show that impaired flexible behavior in ADHD is due to excessive choice switching ('hyper-flexibility'), which can be detrimental or beneficial depending on the learning environment. Computationally, this resulted from blunted sensitivity to reinforcement of which we detected neural correlates in the attention-control network, specifically in the parietal cortex. These neurocomputational findings remain preliminary due to the relatively small sample size.

A cognitive-computational account of mood swings in adolescence.

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

Clinical high risk state of major depressive episodes: Assessment of prodromal phase, its occurrence, duration and symptom patterns by the instrument the DEpression Early Prediction-INventory (DEEP-IN).

BACKGROUND: To decrease the incidence of major depressive episodes, indicated prevention that targets clinical high-risk individuals with first detectable signs that forecast mental disorder is a highly relevant topic of preventive psychiatry. Still little is known about the prodrome of MDE. The aim of the current study was to identify the occurrence of a clinical high-risk state of depression, its duration and symptom constellation. METHODS: Seventy-three patients with a diagnosed affective disorder in partial remission were assessed with our newly developed semi-structured extensive clinical instrument, the DEpression Early Prediction-INventory (DEEP-IN). Within DEEP-IN the course of prodromal symptoms was explored by using a life-chart method. RESULTS: The significant majority of patients (93.2 %) reported a prodromal phase. The mean duration was 7.9 months (SD = 12.5). Within the group with an identified prodromal phase, psychopathological (95.6 %) as well as somatic symptoms (88.2 %) were reported. Somatic symptoms showed a moderate-to-strong effect of sex with higher prevalence in females than in males (97.6 % vs 73.1 %; V = 0.370). LIMITATIONS: This feasibility study had only a small sample size. CONCLUSIONS: The majority of patients with affective disorders reported a clinical prodromal phase with both psychopathological and somatic symptoms that developed months before the onset of the depressive episode. The development of structured instruments for the assessment of depressive risk states is a promising approach for indicated prevention of depression in the future.

Associations of Menstrual Cycle and Progesterone-to-Estradiol Ratio With Alcohol Consumption in Alcohol Use Disorder: A Sex-Separated Multicenter Longitudinal Study.

OBJECTIVE: Alcohol use disorder (AUD) constitutes a critical public health issue and has sex-specific characteristics. Initial evidence suggests that progesterone and estradiol might reduce or increase alcohol intake, respectively. However, there is a need for a better understanding of how the menstrual cycle in females and the ratio of progesterone to estradiol in females and males influence alcohol use patterns in individuals with AUD. METHODS: In this sex-separated multicenter longitudinal study, the authors analyzed 12-month data on real-life alcohol use (from 21,460 smartphone entries), menstrual cycle, and serum progesterone-to-estradiol ratios (from 667 blood samples at four individual study visits) in 74 naturally cycling females and 278 males with AUD between 2020 and 2022, using generalized and general linear mixed modeling. RESULTS: Menstrual cycle phases were significantly associated with binge drinking and progesterone-to-estradiol ratio. During the late luteal phase, females showed a lower predicted binge drinking probability of 13% and a higher predicted marginal mean of progesterone-to-estradiol ratio of 95 compared with during the menstrual, follicular, and ovulatory phases (binge drinking probability and odds ratios vs. late luteal phase, respectively: 17%, odds ratio=1.340, 95% CI=1.031, 1.742; 19%, odds ratio=1.523, 95% CI=1.190, 1.949; and 20%, odds ratio=1.683, 95% CI=1.285, 2.206; difference in progesterone-to-estradiol ratios, respectively: -61, 95% CI=-105.492, -16.095; -78, 95% CI=-119.322, -37.039; and -71, 95% CI=-114.568, -27.534). In males, a higher progesterone-to-estradiol ratio was related to lower probabilities of binge drinking and of any alcohol use, with a 10-unit increase in the hormone ratio resulting in odds ratios of 0.918 (95% CI=0.843, 0.999) and 0.914 (95% CI=0.845, 0.988), respectively. CONCLUSIONS: These ecologically valid findings suggest that high progesterone-to-estradiol ratios can have a protective effect against problematic alcohol use in females and males with AUD, highlighting the progesterone-to-estradiol ratio as a promising treatment target. Moreover, the results indicate that females with AUD may benefit from menstrual cycle phase-tailored treatments.

Working memory gating in obesity: Insights from a case-control fMRI study.

Computational models and neurophysiological data propose that a 'gating mechanism' coordinates distractor-resistant maintenance and flexible updating of working memory contents: While maintenance of information is mainly implemented in the prefrontal cortex, updating of information is signaled by phasic increases in dopamine in the striatum. Previous literature demonstrates structural and functional alterations in these brain areas, as well as differential dopamine transmission among individuals with obesity, suggesting potential impairments in these processes. To test this hypothesis, we conducted an observational case-control fMRI study, dividing participants into groups with and without obesity based on their BMI. We probed maintenance and updating of working memory contents using a modified delayed match to sample task and investigated the effects of SNPs related to the dopaminergic system. While the task elicited the anticipated brain responses, our findings revealed no evidence for group differences in these two processes, neither at the neural level nor behaviorally. However, depending on Taq1A genotype, which affects dopamine receptor density in the striatum, participants with obesity performed worse on the task. In conclusion, this study does not support the existence of overall obesity-related differences in working memory gating. Instead, we propose that potentially subtle alterations may manifest specifically in individuals with a 'vulnerable' genotype.

Diminished reinforcement sensitivity in adolescence is associated with enhanced response switching and reduced coding of choice probability in the medial frontal pole.

Precisely charting the maturation of core neurocognitive functions such as reinforcement learning (RL) and flexible adaptation to changing action-outcome contingencies is key for developmental neuroscience and adjacent fields like developmental psychiatry. However, research in this area is both sparse and conflicted, especially regarding potentially asymmetric development of learning for different motives (obtain wins vs avoid losses) and learning from valenced feedback (positive vs negative). In the current study, we investigated the development of RL from adolescence to adulthood, using a probabilistic reversal learning task modified to experimentally separate motivational context and feedback valence, in a sample of 95 healthy participants between 12 and 45. We show that adolescence is characterized by enhanced novelty seeking and response shifting especially after negative feedback, which leads to poorer returns when reward contingencies are stable. Computationally, this is accounted for by reduced impact of positive feedback on behavior. We also show, using fMRI, that activity of the medial frontopolar cortex reflecting choice probability is attenuated in adolescence. We argue that this can be interpreted as reflecting diminished confidence in upcoming choices. Interestingly, we find no age-related differences between learning in win and loss contexts.

Cortical Grey Matter Mediates Increases in Model-Based Control and Learning from Positive Feedback from Adolescence to Adulthood.

Cognition and brain structure undergo significant maturation from adolescence into adulthood. Model-based (MB) control is known to increase across development, which is mediated by cognitive abilities. Here, we asked two questions unaddressed in previous developmental studies. First, what are the brain structural correlates of age-related increases in MB control? Second, how are age-related increases in MB control from adolescence to adulthood influenced by motivational context? A human developmental sample (n = 103; age, 12-50, male/female, 55:48) completed structural MRI and an established task to capture MB control. The task was modified with respect to outcome valence by including (1) reward and punishment blocks to manipulate the motivational context and (2) an additional choice test to assess learning from positive versus negative feedback. After replicating that an age-dependent increase in MB control is mediated by cognitive abilities, we demonstrate first-time evidence that gray matter density (GMD) in the parietal cortex mediates the increase of MB control with age. Although motivational context did not relate to age-related changes in MB control, learning from positive feedback improved with age. Meanwhile, negative feedback learning showed no age effects. We present a first report that an age-related increase in positive feedback learning was mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. Our findings indicate that brain maturation, putatively reflected in lower GMD, in distinct and partially overlapping brain regions could lead to a more efficient brain organization and might thus be a key developmental step toward age-related increases in planning and value-based choice.SIGNIFICANCE STATEMENT Changes in model-based decision-making are paralleled by extensive maturation in cognition and brain structure across development. Still, to date the neuroanatomical underpinnings of these changes remain unclear. Here, we demonstrate for the first time that parietal GMD mediates age-dependent increases in model-based control. Age-related increases in positive feedback learning were mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. A manipulation of motivational context did not have an impact on age-related changes in model-based control. These findings highlight that brain maturation in distinct and overlapping cortical regions constitutes a key developmental step toward improved value-based choices.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder.

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

Computational Theories of Alcohol Use Disorder: Mapping Learning and Choice Mechanisms on Symptoms.

Alcohol use disorder (AUD) is characterized by a combination of symptoms including excessive craving, loss of control, and progressive neglect of alternative pleasures. A mechanistic understanding of what drives these symptoms is needed to improve diagnostic stratification and to develop new treatment and prevention strategies for AUD. To date, there is no consensus regarding a unifying mechanistic framework that accounts for the different symptoms of AUD. Reinforcement learning (RL) and economic choice theories may be key to elucidating the underlying processes of symptom development and maintenance in AUD. These algorithms may account for the different behavioral and physiological phenomena and are suited to dissect mechanisms linked to different symptoms of AUD. We here review different RL and economic choice models and how they map onto three symptoms of AUD: (1) cue-induced craving, (2) neglect of alternative rewards, and (3) consumption despite adverse consequences. For each symptom and theory, we describe findings from animal and human studies. In humans, we focus on empirical studies that investigated RL models in the context of treatment outcome in AUD. The review indicates important gaps to be addressed in the future by highlighting the challenges in transferring findings from RL and economic choice studies to clinical application. We also critically evaluate the potential and pitfalls of a symptom-oriented approach and highlight the importance of elucidating the role of learning and decision-making processes across diagnostic boundaries.

Patterns of Alcohol Consumption Among Individuals With Alcohol Use Disorder During the COVID-19 Pandemic and Lockdowns in Germany.

Importance: Alcohol consumption (AC) leads to death and disability worldwide. Ongoing discussions on potential negative effects of the COVID-19 pandemic on AC need to be informed by real-world evidence. Objective: To examine whether lockdown measures are associated with AC and consumption-related temporal and psychological within-person mechanisms. Design, Setting, and Participants: This quantitative, intensive, longitudinal cohort study recruited 1743 participants from 3 sites from February 20, 2020, to February 28, 2021. Data were provided before and within the second lockdown of the COVID-19 pandemic in Germany: before lockdown (October 2 to November 1, 2020); light lockdown (November 2 to December 15, 2020); and hard lockdown (December 16, 2020, to February 28, 2021). Main Outcomes and Measures: Daily ratings of AC (main outcome) captured during 3 lockdown phases (main variable) and temporal (weekends and holidays) and psychological (social isolation and drinking intention) correlates. Results: Of the 1743 screened participants, 189 (119 [63.0%] male; median [IQR] age, 37 [27.5-52.0] years) with at least 2 alcohol use disorder (AUD) criteria according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) yet without the need for medically supervised alcohol withdrawal were included. These individuals provided 14 694 smartphone ratings from October 2020 through February 2021. Multilevel modeling revealed significantly higher AC (grams of alcohol per day) on weekend days vs weekdays (ß = 11.39; 95% CI, 10.00-12.77; P < .001). Alcohol consumption was above the overall average on Christmas (ß = 26.82; 95% CI, 21.87-31.77; P < .001) and New Year's Eve (ß = 66.88; 95% CI, 59.22-74.54; P < .001). During the hard lockdown, perceived social isolation was significantly higher (ß = 0.12; 95% CI, 0.06-0.15; P < .001), but AC was significantly lower (ß = -5.45; 95% CI, -8.00 to -2.90; P = .001). Independent of lockdown, intention to drink less alcohol was associated with lower AC (ß = -11.10; 95% CI, -13.63 to -8.58; P < .001). Notably, differences in AC between weekend and weekdays decreased both during the hard lockdown (ß = -6.14; 95% CI, -9.96 to -2.31; P = .002) and in participants with severe AUD (ß = -6.26; 95% CI, -10.18 to -2.34; P = .002). Conclusions and Relevance: This 5-month cohort study found no immediate negative associations of lockdown measures with overall AC. Rather, weekend-weekday and holiday AC patterns exceeded lockdown effects. Differences in AC between weekend days and weekdays evinced that weekend drinking cycles decreased as a function of AUD severity and lockdown measures, indicating a potential mechanism of losing and regaining control. This finding suggests that temporal patterns and drinking intention constitute promising targets for prevention and intervention, even in high-risk individuals.

Responsivity of the Striatal Dopamine System to Methylphenidate-A Within-Subject I-123-ß-CIT-SPECT Study in Male Children and Adolescents With Attention-Deficit/Hyperactivity Disorder.

Background: Methylphenidate (MPH) is the first-line pharmacological treatment of attention-deficit/hyperactivity disorder (ADHD). MPH binds to the dopamine (DA) transporter (DAT), which has high density in the striatum. Assessments of the striatal dopamine transporter by single positron emission computed tomography (SPECT) in childhood and adolescent patients are rare but can provide insight on how the effects of MPH affect DAT availability. The aim of our within-subject study was to investigate the effect of MPH on DAT availability and how responsivity to MPH in DAT availability is linked to clinical symptoms and cognitive functioning. Methods: Thirteen adolescent male patients (9-16 years) with a diagnosis of ADHD according to the DSM-IV and long-term stimulant medication (for at least 6 months) with MPH were assessed twice within 7 days using SPECT after application of I-123-ß-CIT to examine DAT binding potential (DAT BP). SPECT measures took place in an on- and off-MPH status balanced for order across participants. A virtual reality continuous performance test was performed at each time point. Further clinical symptoms were assessed for baseline off-MPH. Results: On-MPH status was associated with a highly significant change (-29.9%) of striatal DAT BP as compared to off-MPH (t = -4.12, p = 0.002). A more pronounced change in striatal DAT BP was associated with higher off-MPH attentional and externalizing symptom ratings (Pearson r = 0.68, p = 0.01). Striatal DAT BP off-MPH, but not on-MPH, was associated with higher symptom ratings (Pearson r = 0.56, p = 0.04). Conclusion: Our findings corroborate previous reports from mainly adult samples that MPH changes striatal DAT BP availability and suggest higher off-MPH DAT BP, likely reflecting low baseline DA levels, as a marker of symptom severity.

Distributed networks for auditory memory differentially contribute to recall precision.

Re-directing attention to objects in working memory can enhance their representational fidelity. However, how this attentional enhancement of memory representations is implemented across distinct, sensory and cognitive-control brain network is unspecified. The present fMRI experiment leverages psychophysical modelling and multivariate auditory-pattern decoding as behavioral and neural proxies of mnemonic fidelity. Listeners performed an auditory syllable pitch-discrimination task and received retro-active cues to selectively attend to a to-be-probed syllable in memory. Accompanied by increased neural activation in fronto-parietal and cingulo-opercular networks, valid retro-cues yielded faster and more perceptually sensitive responses in recalling acoustic detail of memorized syllables. Information about the cued auditory object was decodable from hemodynamic response patterns in superior temporal sulcus (STS), fronto-parietal, and sensorimotor regions. However, among these regions retaining auditory memory objects, neural fidelity in the left STS and its enhancement through attention-to-memory best predicted individuals' gain in auditory memory recall precision. Our results demonstrate how functionally discrete brain regions differentially contribute to the attentional enhancement of memory representations.

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

INTRODUCTION: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. METHOD: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. RESULTS: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. CONCLUSION: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

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.

Emotional maltreatment and neglect impact neural activation upon exclusion in early and mid-adolescence: An event-related fMRI study.

Child maltreatment gives rise to atypical patterns of social functioning with peers which might be particularly pronounced in early adolescence when peer influence typically peaks. Yet, few neuroimaging studies in adolescents use peer interaction paradigms to parse neural correlates of distinct maltreatment exposures. This fMRI study examines effects of abuse, neglect, and emotional maltreatment (EM) among 98 youth (n = 58 maltreated; n = 40 matched controls) using an event-related Cyberball paradigm affording assessment of both social exclusion and inclusion across early and mid-adolescence (≤13.5 years, n = 50; >13.5 years, n = 48). Younger adolescents showed increased activation to social exclusion versus inclusion in regions implicated in mentalizing (e.g., superior temporal gyrus). Individual exposure-specific analyses suggested that neglect and EM coincided with less reduction of activation to social exclusion relative to inclusion in the dorsal anterior cingulate cortex/pre-supplementary motor area (dACC/pre-SMA) among younger versus older adolescents. Integrative follow-up analyses showed that EM accounted for this dACC/pre-SMA activation pattern over and above other exposures. Moreover, age-independent results within respective exposure groups revealed that greater magnitude of neglect predicted blunted exclusion-related activity in the parahippocampal gyrus, while EM predicted increased activation to social exclusion in the precuneus/posterior cingulate cortex.

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.

Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference.

Dopamine is implicated in representing model-free (MF) reward prediction errors a as well as influencing model-based (MB) credit assignment and choice. Putative cooperative interactions between MB and MF systems include a guidance of MF credit assignment by MB inference. Here, we used a double-blind, placebo-controlled, within-subjects design to test an hypothesis that enhancing dopamine levels boosts the guidance of MF credit assignment by MB inference. In line with this, we found that levodopa enhanced guidance of MF credit assignment by MB inference, without impacting MF and MB influences directly. This drug effect correlated negatively with a dopamine-dependent change in purely MB credit assignment, possibly reflecting a trade-off between these two MB components of behavioural control. Our findings of a dopamine boost in MB inference guidance of MF learning highlight a novel DA influence on MB-MF cooperative interactions.

Prediction Along a Developmental Perspective in Psychiatry: How Far Might We Go?

Most mental disorders originate in childhood, and once symptoms present, a variety of psychosocial and cognitive maladjustments may arise. Although early childhood problems are generally associated with later mental health impairments and psychopathology, pluripotent transdiagnostic trajectories may manifest. Possible predictors range from behavioral and neurobiological mechanisms, genetic predispositions, environmental and social factors, and psychopathological comorbidity. They may manifest in altered neurodevelopmental trajectories and need to be validated capitalizing on large-scale multi-modal epidemiological longitudinal cohorts. Moreover, clinical and etiological variability between patients with the same disorders represents a major obstacle to develop effective treatments. Hence, in order to achieve stratification of patient samples opening the avenue of adapting and optimizing treatment for the individual, there is a need to integrate data from multi-dimensionally phenotyped clinical cohorts and cross-validate them with epidemiological cohort data. In the present review, we discuss these aspects in the context of externalizing and internalizing disorders summarizing the current state of knowledge, obstacles, and pitfalls. Although a large number of studies have already increased our understanding on neuropsychobiological mechanisms of mental disorders, it became also clear that this knowledge might only be the tip of the Eisberg and that a large proportion still remains unknown. We discuss prediction strategies and how the integration of different factors and methods may provide useful contributions to research and at the same time may inform prevention and intervention.

Loss of control over eating: A systematic review of task based research into impulsive and compulsive processes in binge eating.

Recurring episodes of excessive food intake in binge eating disorder can be understood through the lens of behavioral control systems: patients repeat maladaptive behaviors against their explicit intent. Self-report measures show enhanced impulsivity and compulsivity in binge eating (BE) but are agnostic as to the processes that might lead to impulsive and compulsive behavior in the moment. Task-based neurocognitive investigations can tap into those processes. In this systematic review, we synthesize neurocognitive research on behavioral impulsivity and compulsivity in BE in humans and animals, published between 2010-2020. Findings on impulsivity are heterogeneous. Findings on compulsivity are sparse but comparatively consistent, indicating an imbalance of goal-directed and habitual control as well as deficits in reversal learning. We urge researchers to address heterogeneity related to mood states and the temporal dynamics of symptoms, to systematically differentiate contributions of body weight and BE, and to ascertain the validity and reliability of tasks. Moreover, we propose to further scrutinize the compulsivity findings to unravel the computational mechanisms of a potential reinforcement learning deficit.

Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic.

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.