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.

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.

Comparing Discounting of Potentially Real Rewards and Losses by Means of Functional Magnetic Resonance Imaging.

Aim: Delay discounting (DD) has often been investigated in the context of decision making whereby individuals attribute decreasing value to rewards in the distant future. Less is known about DD in the context of negative consequences. The aim of this pilot study was to identify commonalities and differences between reward and loss discounting on the behavioral as well as the neural level by means of computational modeling and functional Magnetic Resonance Imaging (fMRI). We furthermore compared the neural activation between anticipation of rewards and losses. Method: We conducted a study combining an intertemporal choice task for potentially real rewards and losses (decision-making) with a monetary incentive/loss delay task (reward/loss anticipation). Thirty healthy participants (age 18-35, 14 female) completed the study. In each trial, participants had to choose between a smaller immediate loss/win and a larger loss/win at a fixed delay of two weeks. Task-related brain activation was measured with fMRI. Results: Hyperbolic discounting parameters of loss and reward conditions were correlated (r = 0.56). During decision-making, BOLD activation was observed in the parietal and prefrontal cortex, with no differences between reward and loss conditions. During reward and loss anticipation, dissociable activation was observed in the striatum, the anterior insula and the anterior cingulate cortex. Conclusion: We observed behavior concurrent with DD in both the reward and loss condition, with evidence for similar behavioral and neural patterns in the two conditions. Intertemporal decision-making recruited the fronto-parietal network, whilst reward and loss anticipation were related to activation in the salience network. The interpretation of these findings may be limited to short delays and small monetary outcomes.

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.

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting.

Background: The tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale. Method: The proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm. Results: The developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting. Conclusion: The present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models.

Introduction: Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments. Methods: These ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error. Results: The proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning. Discussion: Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.