The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

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.

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.

Common and differential variables of anxiety and depression in adolescence: a nation-wide smartphone-based survey.

BACKGROUND: Mental health in adolescence is critical in its own right and a predictor of later symptoms of anxiety and depression. To address these mental health challenges, it is crucial to understand the variables linked to anxiety and depression in adolescence. METHODS: Here, we analyzed data of 278 adolescents that were collected in a nation-wide survey provided via a smartphone-based application during the COVID-19 pandemic. We used an elastic net regression machine-learning approach to classify individuals with clinically relevant self-reported symptoms of depression or anxiety. We then identified the most important variables with a combination of permutation feature importance calculation and sequential logistic regressions. RESULTS: 40.30% of participants reported clinically relevant anxiety symptoms, and 37.69% reported depressive symptoms. Both machine-learning models performed well in classifying participants with depressive (AUROC = 0.77) or anxiety (AUROC = 0.83) symptoms and were significantly better than the no-information rate. Feature importance analyses revealed that anxiety and depression in adolescence are commonly related to sleep disturbances (anxiety OR = 2.12, depression OR = 1.80). Differentiating between symptoms, self-reported depression increased with decreasing life satisfaction (OR = 0.43), whereas self-reported anxiety was related to worries about the health of family and friends (OR = 1.98) as well as impulsivity (OR = 2.01). CONCLUSION: Our results show that app-based self-reports provide information that can classify symptoms of anxiety and depression in adolescence and thus offer new insights into symptom patterns related to adolescent mental health issues. These findings underscore the potentials of health apps in reaching large cohorts of adolescence and optimize diagnostic and treatment.

Neurocomputational mechanisms underlying differential reinforcement learning from wins and losses in obesity with and without binge eating.

BACKGROUND: Binge Eating Disorder (BED) is thought of as a disorder of cognitive control but evidence regarding its neurocognitive mechanisms is inconclusive. Key limitations in prior research are a lack of consistent separation between effects of BED and obesity, and a disregard for self-report evidence suggesting that neurocognitive alterations may emerge primarily in loss- or harm-avoidance contexts. METHODS: Addressing these gaps, this longitudinal study investigated behavioral flexibility and its underlying neuro-computational processes in reward-seeking and loss-avoidance contexts. Obese participants with BED (BED), without BED (OB), and healthy normal-weight participants (NW) (Ntotal=96) performed a probabilistic reversal learning task during functional imaging, with different blocks focused on obtaining wins or avoiding losses. They were reinvited for a 6-months follow-up. RESULTS: Analyses informed by computational models of reinforcement learning showed that unlike BED, OB performed worse in the win than the loss condition. Computationally, this was explained by differential learning sensitivities in the win vs loss conditions between groups. In the brain, this was echoed in differential neural learning signals in the ventromedial prefrontal cortex (vmPFC) per condition. The differences were subtle, but scaled with BED symptoms, such that more severe BED symptoms were associated with increasing bias towards improved learning from wins vs losses. Across conditions, OB switched more between choice options than NW. This was reflected in diminished representation of choice certainty in the vmPFC. CONCLUSIONS: Our study highlights the importance of distinguishing between obesity with and without BED to identify unique neuro-computational alterations underlying different styles of maladaptive eating behavior.

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.

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.

Belief Updating in Subclinical and Clinical Delusions.

Background and Hypothesis: Current frameworks propose that delusions result from aberrant belief updating due to altered prediction error (PE) signaling and misestimation of environmental volatility. We aimed to investigate whether behavioral and neural signatures of belief updating are specifically related to the presence of delusions or generally associated with manifest schizophrenia. Methods: Our cross-sectional design includes human participants (n[female/male] = 66[25/41]), stratified into four groups: healthy participants with minimal (n = 22) or strong delusional-like ideation (n = 18), and participants with diagnosed schizophrenia with minimal (n = 13) or strong delusions (n = 13), resulting in a 2 × 2 design, which allows to test for the effects of delusion and diagnosis. Participants performed a reversal learning task with stable and volatile task contingencies during fMRI scanning. We formalized learning with a hierarchical Gaussian filter model and conducted model-based fMRI analysis regarding beliefs of outcome uncertainty and volatility, precision-weighted PEs of the outcome- and the volatility-belief. Results: Patients with schizophrenia as compared to healthy controls showed lower accuracy and heightened choice switching, while delusional ideation did not affect these measures. Participants with delusions showed increased precision-weighted PE-related neural activation in fronto-striatal regions. People with diagnosed schizophrenia overestimated environmental volatility and showed an attenuated neural representation of volatility in the anterior insula, medial frontal and angular gyrus. Conclusions: Delusional beliefs are associated with altered striatal PE-signals. Juxtaposing, the potentially unsettling belief that the environment is constantly changing and weaker neural encoding of this subjective volatility seems to be associated with manifest schizophrenia, but not with the presence of delusional ideation.