Portion size affects food selection in an immersive virtual reality buffet and is related to measured intake in laboratory meals varying in portion size.

A crucial step for validating the utility of an immersive virtual reality (iVR) buffet to study eating behavior is to determine whether variations in food characteristics such as portion size (PS) are relevant predictors of food selection in an iVR buffet. We tested whether manipulating PS in an iVR buffet affects the weight of food selected, and whether this response to PS is similar to participants' measured intake when PS varies at laboratory meals. In a randomized crossover design, 91 adults (18-71 y; 64 females; BMI = 25.3 ± 5.7) used their iVR remote to select lunch and dinner portions from an iVR buffet before consuming a standardized lab meal at two visits separated by one week. The PS in the iVR buffet and lab meals varied between a standard PS and a large PS. This design enabled comparisons of PS effects between iVR and lab settings, despite the scale difference in food weight between the environments. Portion size significantly affected food selection and food intake (p < 0.001). Subjects selected an additional 350 g in iVR and consumed an additional 154 g of food in the lab meals when offered the large portion compared to the small portion. The effect of PS showed a similar percentage increase in iVR (36.5%) and lab meals (39.2%). There was no significant difference in the effect of PS between iVR and lab meals after accounting for scale differences in food weight between the environments. The response to PS was not influenced by subject characteristics such as body mass index, sex, or age. These results demonstrate the utility of iVR for replicating real-world eating behaviors and enhancing our understanding of the intricate dynamics of food-related behaviors in a variety of contexts.

Associations Between Traumatic Stress, Brain Volumes and Post-traumatic Stress Disorder Symptoms in Children: Data from the ABCD Study.

Reduced volumes in brain regions of interest (ROIs), primarily from adult samples, are associated with posttraumatic stress disorder (PTSD). We extended this work to children using data from the Adolescent Brain Cognitive Development (ABCD) Study® (N = 11,848; Mage = 9.92). Structural equation modeling and an elastic-net (EN) machine-learning approach were used to identify potential effects of traumatic events (TEs) on PTSD symptoms (PTSDsx) directly, and indirectly via the volumes 300 subcortical and cortical ROIs. We then estimated the genetic and environmental variation in the phenotypes. TEs were directly associated with PTSDsx (r = 0.92) in children, but their indirect effects (r < 0.0004)-via the volumes of EN-identified subcortical and cortical ROIs-were negligible at this age. Additive genetic factors explained a modest proportion of the variance in TEs (23.4%) and PTSDsx (21.3%), and accounted for most of the variance of EN-identified volumes of four of the five subcortical (52.4-61.8%) three of the nine cortical ROIs (46.4-53.3%) and cerebral white matter in the left hemisphere (57.4%). Environmental factors explained most of the variance in TEs (C = 61.6%, E = 15.1%), PTSDsx (residual-C = 18.4%, residual-E = 21.8%), right lateral ventricle (C = 15.2%, E = 43.1%) and six of the nine EN-identified cortical ROIs (C = 4.0-13.6%, E = 56.7-74.8%). There is negligible evidence that the volumes of brain ROIs are associated with the indirect effects of TEs on PTSDsx at this age. Overall, environmental factors accounted for more of the variation in TEs and PTSDsx. Whereas additive genetic factors accounted for most of the variability in the volumes of a minority of cortical and in most of subcortical ROIs.

Using association rules mining to characterize loss of control eating in childhood.

Childhood loss of control (LOC)-eating, the perceived inability to stop or control eating, is associated with increased risk for binge-eating disorder and obesity. However, the correlates of LOC-eating in childhood remain unclear. A secondary analysis of 177, 7-12-year-old children from five laboratory feeding studies was performed to investigate potential family (e.g., frequency of meals together, feeding practices), parental (e.g., education, weight status), and child (e.g., weight status, appetite traits) correlates of LOC-eating. Association rules mining (ARM1), a data-driven approach, was used to examine all characteristics that were common across studies to identify which were associated with LOC-eating. Results showed LOC-eating was characterized by a combination of child appetitive behaviors and parental feeding practices. In particular, LOC-eating was associated with low parental pressure to eat in combination with a high propensity to want to eat all the time and frequent refusal or dislike of novel foods. This pattern of both food approach (i.e., wanting to eat all the time) and avoidant behaviors (i.e., food fussiness) highlights the need for more research to characterize the complex patterns of appetitive traits associated with LOC-eating. In contrast, the absence of LOC-eating was associated with a low propensity to want to eat all the time, greater family income, and infrequent emotional overeating. Therefore, propensity to want to eat all the time, a single question from the Children's Eating Behavior Questionnaire, characterized both the presence and absence of LOC-eating, highlighting the need for more research to determine if this question captures clinically relevant individual differences. Future studies addressing these questions will advance our understanding of pediatric LOC-eating and may lead to interventions to reduce risk for more severe eating disorder symptomology.

Effectiveness and Utility of Mobile Device Assessment of Subjective Craving during Residential Opioid Dependence Treatment.

Background: Craving is a dynamic state that is both theoretically and empirically linked to relapse in addiction. Static measures cannot adequately capture the dynamic nature of craving, and research has shown that these measures are limited in their capacity to link craving to treatment outcomes. Methods: The current study reports on assessments of craving collected 4x-day across 12 days from 73 patients in residential treatment for opioid dependence. Analyses investigated whether the within-person assessments yielded expected across- and within-day variability, whether levels of craving changed across and within days, and, finally, whether individual differences in craving variability predicted post-residential treatment relapse. Results: Preliminary analyses found acceptable levels of data entry compliance and reliability. Consistent with expectations, craving varied both between (46%) and within persons, with most within-person variance (over 40%) existing within days. Other patterns that emerged indicated that, on average, craving declined across the 12-days of assessment, and was generally strongest at mid-day. Analyses also found that patients' person-level craving variability predicted post-treatment relapse, above and beyond their mean levels of craving. Conclusion: Analyses support the reliability, sensitivity, and potential utility of the 4x-day, 12-day assessment protocol for measuring craving during residential treatment.

What can be learned from couple research: Examining emotional co-regulation processes in face-to-face interactions.

A crucial component of successful counseling and psychotherapy is the dyadic emotion co-regulation process between patient and therapist that unfolds moment to moment during therapy sessions. The major reason for the disappointing progress in understanding this process is the lack of appropriate methods to assess subjectively experienced emotions continuously during therapy sessions without disturbing the natural flow of the interaction. The resulting inability has forced the field to focus on patients' overall emotion ratings at the end of each session with limited predictive value of the dyadic interplay between patient and therapist's emotional states within each session. The current tutorial demonstrates how couple research-confronted with a comparable problem-has overcome this issue by (i) developing a video-based retrospective self-report assessment method for individuals' continuous state emotions without undermining the dyadic interaction and (ii) using a validated statistical tool to analyze the dynamical process during a dyadic interaction. We show how to assess emotion data continuously, and how to unravel self-regulation and co-regulation processes using a Latent Differential Equation Modeling approach. Finally, we discuss how this approach can be applied in counseling psychology and psychotherapy to test basic theoretical assumptions about the co-creation of emotions despite the conceptual differences between couple dyads and therapist-patient dyads. The present method aims to inspire future research activities examining systematic real-time processes between patients and therapists. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Ecological momentary assessment of using food to soothe during infancy in the INSIGHT trial.

BACKGROUND: Use of food to soothe infant distress has been linked to greater weight in observational studies. We used ecological momentary assessment to capture detailed patterns of food to soothe and evaluate if a responsive parenting intervention reduced parents' use of food to soothe. METHODS: Primiparous mother-newborn dyads were randomized to a responsive parenting intervention designed for obesity prevention or a safety control group. Responsive parenting curriculum included guidance on using alternative soothing strategies (e.g., swaddling), rather than feeding, as the first response to infant fussiness. After the initial intervention visit 3 weeks after delivery, mothers (n = 157) were surveyed for two 5-8 day bursts at infant ages 3 and 8 weeks. Surveys were sent via text message every 4 h between 10:00 AM-10:00 PM, with 2 surveys sent at 8:00 AM asking about nighttime hours. Infant fusses and feeds were reported for each 4-h interval. Food to soothe was defined as "Fed First" and "Not Fed First" in response to a fussy event. Use of food to soothe was modeled using random-intercept logistic regression. RESULTS: The control group had greater odds of having Fed First, compared to the responsive parenting group at ages 3 and 8 weeks (3 weeks: OR = 1.9; 95% CI = 1.4-2.7; p < 0.01; 8 weeks: OR = 1.4; 95% CI = 1.0-2.1; p = 0.053). More responsive parenting mothers reported using a responsive parenting intervention strategy first, before feeding, than controls at ages 3 and 8 weeks (3 weeks: 58.1% vs. 41.9%; 8 weeks: 57.1% vs. 42.9%, respectively; p < 0.01 for both). At both ages combined, fewer fusses from responsive parenting infants were soothed best by feeding compared to controls (49.5% vs. 61.0%, respectively; p < 0.01). For both study groups combined, parents had greater odds of having Fed First during the nighttime compared to the daytime at both ages (3 weeks: OR = 1.6, 95% CI = 1.4-1.8; p < 0.01; 8 weeks: OR = 2.1; 95% CI = 1.7-2.6; p < 0.01). CONCLUSIONS: INSIGHT's responsive parenting intervention reduced use of food to soothe and increased use of alternative soothing strategies in response to infant fussiness. Education on responsive parenting behaviors around fussing and feeding during early infancy has the potential to improve later self-regulation and weight gain trajectory. TRIAL REGISTRATION: NCT01167270 . Registered July 21, 2010.

Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

Teams on the same wavelength perform better: Inter-brain phase synchronization constitutes a neural substrate for social facilitation.

Working together feels easier with some people than with others. We asked participants to perform a visual search task either alone or with a partner while simultaneously measuring each participant's EEG. Local phase synchronization and inter-brain phase synchronization were generally higher when subjects jointly attended to a visual search task than when they attended to the same task individually. Some participants searched the visual display more efficiently and made faster decisions when working as a team, whereas other dyads did not benefit from working together. These inter-team differences in behavioral performance gain in the visual search task were reliably associated with inter-team differences in local and inter-brain phase synchronization. Our results suggest that phase synchronization constitutes a neural correlate of social facilitation, and may help to explain why some teams perform better than others.

Metacognitive mechanisms underlying lucid dreaming.

Lucid dreaming is a state of awareness that one is dreaming, without leaving the sleep state. Dream reports show that self-reflection and volitional control are more pronounced in lucid compared with nonlucid dreams. Mostly on these grounds, lucid dreaming has been associated with metacognition. However, the link to lucid dreaming at the neural level has not yet been explored. We sought for relationships between the neural correlates of lucid dreaming and thought monitoring. Human participants completed a questionnaire assessing lucid dreaming ability, and underwent structural and functional MRI. We split participants based on their reported dream lucidity. Participants in the high-lucidity group showed greater gray matter volume in the frontopolar cortex (BA9/10) compared with those in the low-lucidity group. Further, differences in brain structure were mirrored by differences in brain function. The BA9/10 regions identified through structural analyses showed increases in blood oxygen level-dependent signal during thought monitoring in both groups, and more strongly in the high-lucidity group. Our results reveal shared neural systems between lucid dreaming and metacognitive function, in particular in the domain of thought monitoring. This finding contributes to our understanding of the mechanisms enabling higher-order consciousness in dreams.

Identifying users of traditional and Internet-based resources for meal ideas: An association rule learning approach.

Increasing home cooking while decreasing the consumption of food prepared away from home is a commonly recommended weight management strategy, however research on where individuals obtain ideas about meals to cook at home is limited. This study examined the characteristics of individuals who reported using traditional and Internet-based resources for meal ideas. 583 participants who were ≥50% responsible for household meal planning were recruited to approximate the 2014 United States Census distribution on sex, age, race/ethnicity, and household income. Participants reported demographic characteristics, home cooking frequency, and their use of 4 traditional resources for meal ideas (e.g., cookbooks), and 7 Internet-based resources for meal ideas (e.g., Pinterest) in an online survey. Independent samples t-tests compared home cooking frequency by resource use. Association rule learning identified those demographic characteristics that were significantly associated with resource use. Family and friends (71%), food community websites (45%), and cookbooks (41%) were the most common resources reported. Cookbook users reported preparing more meals at home per week (M = 9.65, SD = 5.28) compared to non-cookbook users (M = 8.11, SD = 4.93; t = -3.55, p < 0.001). Resource use was generally higher among parents and varied systematically with demographic characteristics. Findings suggest that home cooking interventions may benefit by modifying resources used by their target population.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE).

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant's personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual's data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.

Efficient Hessian computation using sparse matrix derivatives in RAM notation.

This article proposes a new, more efficient method to compute the minus two log likelihood, its gradient, and the Hessian for structural equation models (SEMs) in reticular action model (RAM) notation. The method exploits the beneficial aspect of RAM notation that the matrix derivatives used in RAM are sparse. For an SEM with K variables, P parameters, and P' entries in the symmetrical or asymmetrical matrix of the RAM notation filled with parameters, the asymptotical run time of the algorithm is O(P ' K (2) + P (2) K (2) + K (3)). The naive implementation and numerical implementations are both O(P (2) K (3)), so that for typical applications of SEM, the proposed algorithm is asymptotically K times faster than the best previously known algorithm. A simulation comparison with a numerical algorithm shows that the asymptotical efficiency is transferred to an applied computational advantage that is crucial for the application of maximum likelihood estimation, even in small, but especially in moderate or large, SEMs.

Validation of computational models to characterize cumulative intake curves from video-coded meals.

Introduction: Observational coding of eating behaviors (e.g., bites, eating rate) captures behavioral characteristics but is limited in its ability to capture dynamic patterns (e.g., temporal changes) across a meal. While the Universal Eating Monitor captures dynamic patterns of eating through cumulative intake curves, it is not commonly used in children due to strict behavioral protocols. Therefore, the objective of this study was to test the ability of computational models to characterize cumulative intake curves from video-coded meals without the use of continuous meal weight measurement. Methods: Cumulative intake curves were estimated using Kisslieff's Quadratic model and Thomas's logistic ordinary differential equation (LODE) model. To test if cumulative intake curves could be characterized from video-coded meals, three different types of data were simulated: (1) Constant Bite: simplified cumulative intake data; (2) Variable Bite: continuously measured meal weight data; and (3) Bite Measurement Error: video-coded meals that require the use of average bite size rather than measured bite size. Results: Performance did not differ by condition, which was assessed by examining model parameter recovery, goodness of fit, and prediction error. Therefore, the additional error incurred by using average bite size as one would with video-coded meals did not impact the ability to accurately estimate cumulative intake curves. While the Quadratic and LODE models were comparable in their ability to characterize cumulative intake curves, the LODE model parameters were more distinct than the Quadradic model. Greater distinctness suggests the LODE model may be more sensitive to individual differences in cumulative intake curves. Discussion: Characterizing cumulative intake curves from video-coded meals expands our ability to capture dynamic patterns of eating behaviors in populations that are less amenable to strict protocols such as children and individuals with disordered eating. This will improve our ability to identify patterns of eating behavior associated with overconsumption and provide new opportunities for treatment.

Triangulating on developmental models with a combination of experimental and nonexperimental estimates.

Plausible competing developmental models show similar or identical structural equation modeling model fit indices, despite making very different causal predictions. One way to help address this problem is incorporating outside information into selecting among models. This study attempted to select among developmental models of children's early mathematical skills by incorporating information about the extent to which models forecast the longitudinal pattern of causal impacts of early math interventions. We tested for the usefulness and validity of the approach by applying it to data from three randomized controlled trials of early math interventions with longitudinal follow-up assessments in the United States (Ns = 1,375, 591, 744; baseline age 4.3, 6.5, 4.4; 17%-69% Black). We found that, across data sets, (a) some models consistently outperformed other models at forecasting later experimental impacts, (b) traditional statistical fit indices were not strongly related to causal fit as indexed by models' accuracy at forecasting later experimental impacts, and (c) models showed consistent patterns of similarity and discrepancy between statistical fit and models' effectiveness at forecasting experimental impacts. We highlight the importance of triangulation and call for more comparisons of experimental and nonexperimental estimates for choosing among developmental models. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Health behavior changes, such as prevention of suicidal thoughts and behaviors, smoking, drug use, and alcohol use; and the promotion of mental health, sleep, and physical activities, and decreases in sedentary behavior, are difficult to sustain. The ILHBN is a cooperative agreement network funded jointly by seven participating units within the National Institutes of Health to collaboratively study how factors that occur in individuals' everyday life and in their natural environment influence the success of positive health behavior changes. This article discusses how information collected using smartphones, wearables, and other devices can provide helpful active and passive reflections of the participants' extent of risk and resources at the moment for an extended period of time. However, successful engagement and retention of participants also require tailored adaptations of study designs, measurement tools, measurement intervals, study span, and device choices that create hurdles in integrating (harmonizing) data from multiple studies. We describe some of the challenges, opportunities, and solutions that emerged from harmonizing intensive longitudinal data under heterogeneous study and participant characteristics within the ILHBN, and share some tools and recommendations to facilitate future data harmonization efforts.

Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives.

The Measurement Model of Derivatives (MMOD; Estabrook, 2015) provides the opportunity to evaluate and refine measurement scales used in longitudinal studies to clarify their theoretical distinctions and relationship to academic achievement. We demonstrate this using three teacher-rated scales of child self-regulatory behavior obtained from the Early Childhood Longitudinal Study Kindergarten Class of 2010-11 (ECLS-K:2011; Tourangeau et al., 2019). Data-driven factor structures were generated using a training sample (N = 2821), then compared using the MMOD to the theoretical measurement structure on a holdout sample (N = 2822). Finally, to externally validate their utility, the best-fitting data-driven measurement structure was compared to the theoretical structure in their ability to predict academic achievement on a validation sample (N = 5643). We discuss theoretical implications for self-regulation, as well as the MMODs applicability to other educational data sets.

Do I look like I'm sure?: Partial metacognitive access to the low-level aspects of one's own facial expressions.

As humans we communicate important information through fine nuances in our facial expressions, but because conscious motor representations are noisy, we might not be able to report these fine movements. Here we measured the precision of the explicit metacognitive information that young adults have about their own facial expressions. Participants imitated pictures of themselves making facial expressions and triggered a camera to take a picture of them while doing so. They then rated how well they thought they imitated each expression. We defined metacognitive access to facial expressions as the relationship between objective performance (how well the two pictures matched) and subjective performance ratings. As a group, participants' metacognitive confidence ratings were only about four times less precise than their own similarity ratings. In turn, machine learning analyses revealed that participants' performance ratings were based on idiosyncratic subsets of features. We conclude that metacognitive access to one's own facial expressions is only partial.

Subjective and objective difficulty of emotional facial expression perception from dynamic stimuli.

This study aimed to discover predictors of subjective and objective difficulty in emotion perception from dynamic facial expressions. We used a multidimensional emotion perception framework, in which observers rated the perceived emotion along a number of dimensions instead of choosing from traditionally-used discrete categories of emotions. Data were collected online from 441 participants who rated facial expression stimuli in a novel paradigm designed to separately measure subjective (self-reported) and objective (deviation from the population consensus) difficulty. We targeted person-specific (sex and age of observers and actors) and stimulus-specific (valence and arousal values) predictors of those difficulty scores. Our findings suggest that increasing age of actors makes emotion perception more difficult for observers, and that perception difficulty is underestimated by men in comparison to women, and by younger and older adults in comparison to middle-aged adults. The results also yielded an increase in the objective difficulty measure for female observers and female actors. Stimulus-specific factors-valence and arousal-exhibited quadratic relationships with subjective and objective difficulties: Very positive and very negative stimuli were linked to reduced subjective and objective difficulty, whereas stimuli of very low and high arousal were linked to decreased subjective but increased objective difficulty. Exploratory analyses revealed low relevance of person-specific variables for the prediction of difficulty but highlighted the importance of valence in emotion perception, in line with functional accounts of emotions. Our findings highlight the need to complement traditional emotion recognition paradigms with novel designs, like the one presented here, to grasp the "big picture" of human emotion perception.

Using factorial mediation analysis to better understand the effects of interventions.

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Prefrontal cortical response to natural rewards and self-reported anhedonia are associated with greater craving among recently withdrawn patients in residential treatment for opioid use disorder.

Both anhedonia and craving are common among patients with opioid use disorder (OUD), and are associated with vulnerability to relapse. Although these constructs are theoretically linked relatively few studies have examined them together. In the current study, recently withdrawn patients (N = 71) in residential treatment for prescription OUD underwent a cue reactivity paradigm while being monitored with functional near-infrared spectroscopy (fNIRS). Patients also self-reported symptoms of anhedonia via the Snaith-Hamilton Pleasure Scale (SHAPS), while smartphone-based ecological momentary assessments (EMA) were used to measure craving levels. On average, lower right prefrontal cortex (PFC) activity in response to positive social stimuli was associated with higher craving (ß = - 2.87; S.E. = 1.23; p = 0.02). Self-reported anhedonia moderated the association between PFC activity and craving (ß = - 1.02; S.E. = 0.48; p = 0.04), such that patients with two or more anhedonic symptoms had a significant and stronger negative association between PFC activation to hedonically positive images and craving, compared to patients with fewer than two anhedonic symptoms, among whom the association was not significant. This finding provides evidence that higher levels of anhedonia among patients in residential treatment for OUD are associated with a stronger link between lower PFC response to positive social experiences and higher levels of craving, potentially increasing overall vulnerability to relapse.