The dynamical signature of anhedonia in major depressive disorder: positive emotion dynamics, reactivity, and recovery

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

From anticipation to action: A RCT on mental imagery exercises in daily life as a motivational amplifier for individuals with depressive symptoms

Encouraging engagement in rewarding or pleasant activities is one of the most important treatment goals for depression. Mental imagery exercises have been shown to increase the motivation for planned behaviour in the lab but it is unclear whether this is also the case in daily life. Therefore, we aimed to investigate the effect of mental imagery exercises on motivation and behaviour in daily life. Participants with depressive symptoms (N = 59) were randomly assigned to a group receiving mental imagery (MI) exercises or a control group receiving relaxation (RE) exercises via study phones. We employed an experience sampling design with 10 assessments per day for 10 days (three days baseline, four days with two exercises per day and three days post-intervention). Data was analysed using t-tests and multilevel linear regression analyses. As predicted, MI exercises enhanced motivation and reward anticipation during the intervention phase compared to RE. However, MI did not enhance active behaviour or strengthen the temporal association from reward anticipation (t-1) to active behaviour (t). Mental imagery exercises can act as a motivational amplifier but its effects on behaviour and real-life reward processes remain to be elucidated.

Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing

BACKGROUND

Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach.

METHODS

Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks.

RESULTS

Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005).

CONCLUSIONS

We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.

Molecular signatures of premature aging in Major Depression and Substance Use Disorders

Major depressive disorder (MDD) and substance-use disorders (SUDs) often lead to premature aging, increasing vulnerability to cognitive decline and other forms of dementia. This study utilized advanced systems bioinformatics to identify aging "signatures" in MDD and SUDs and evaluated the potential for known lifespan-extending drugs to target and reverse these signatures. The results suggest that inhibiting the transcriptional activation of FOS gene family members holds promise in mitigating premature aging in MDD and SUDs. Conversely, antidepressant drugs activating the PI3K/Akt/mTOR pathway, a common mechanism in rapid-acting antidepressants, may accelerate aging in MDD patients, making them unsuitable for those with comorbid aging-related conditions like dementia and Alzheimer's disease. Additionally, this innovative approach identifies potential anti-aging interventions for MDD patients, such as Deferoxamine, Resveratrol, Estradiol valerate, and natural compounds like zinc acetate, genistein, and ascorbic acid, regardless of comorbid anxiety disorders. These findings illuminate the premature aging effects of MDD and SUDs and offer insights into treatment strategies for patients with comorbid aging-related conditions, including dementia and Alzheimer's disease.

Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e.,s 2 , s , ln( s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA-S 2 , EWMA-ln(S 2 ), and EWMA- X ¯ -S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA-S 2 and EWMA-ln(S 2 ); the performance difference with EWMA- X ¯ -S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.

Cognitive improvement in patients with major depressive disorder after personalised multi domain training in the CERT-D study

The CERT-D program offers a new treatment approach addressing disturbed cognitive and psychosocial functioning in major depressive disorder (MDD). The current analysis of a randomised controlled trial (RCT) comprises two objectives: Firstly, evaluating the program's efficacy of a personalised versus standard treatment and secondly, assessing the treatment's persistence longitudinally. Participants (N = 112) were randomised into a personalised or standard treatment group. Both groups received 8 weeks of cognitive training, followed by a three-month follow-up without additional training. The type of personalised training was determined by pre-treatment impairments in the domains of cognition, emotion-processing and social-cognition. Standard training addressed all three domains equivalent. Performance in these domains was assessed repeatedly during RCT and follow-up. Treatment comparisons during the RCT-period showed benefits of personalised versus standard treatment in certain aspects of social-cognition. Conversely, no benefits in the remaining domains were found, contradicting a general advantage of personalisation. Exploratory follow-up analysis on persistence of the program's effects indicated sustained intervention outcomes across the entire sample. A subsequent comparison of clinical outcomes between personalised versus standard treatment over a three-month follow-up period showed similar results. First evidence suggests that existing therapies for MDD could benefit from an adjunct administration of the CERT-D program.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Effect of Daily Life Reward Loop Functioning on the Course of Depression

Engagement in activities increases positive affect (Reward Path 1), which subsequently reinforces motivation (Reward Path 2), and hence future engagement in activities (Reward Path 3). Strong connections between these three reward loop components are considered adaptive, and might be disturbed in depression. Although some ecological nomentary assessment (EMA) studies have investigated the cross-sectional association between separate reward paths and individuals' level of depression, no EMA study has looked into the association between individuals' reward loop strength and depressive symptom course. The present EMA study assessed reward loop functioning (5x/day, 28 days) of 46 outpatients starting depression treatment at secondary mental health services and monitored with the Inventory of Depressive Symptomatology-Self-Report (IDS-SR) during a 7-month period. Results of multilevel regression analyses showed significant within-person associations for Reward Path 1 (b = 0.21, p < .001), Reward Path 2 (b = 0.43, p < .001), and Reward Path 3 (b = 0.20, p < .001). Stronger average reward loops (i.e., within-person mean of all reward paths) did not relate to participants' improvement in depressive symptoms over time. Path-specific results revealed that Reward Paths 1 and 2 may have partly opposite effects on depressive symptom course. Together, our findings suggest that reward processes in daily life might be best studied separately and that further investigation is warranted to explore under what circumstances strong paths are adaptive or not.

Stress to inflammation and anhedonia: Mechanistic insights from preclinical and clinical models

Anhedonia, as evidenced by impaired pleasurable response to reward, reduced reward motivation, and/or deficits in reward-related learning, is a common feature of depression. Such deficits in reward processing are also an important clinical target as a risk factor for depression onset. Unfortunately, reward-related deficits remain difficult to treat. To address this gap and inform the development of effective prevention and treatment strategies, it is critical to understand the mechanisms that drive impairments in reward function. Stress-induced inflammation is a plausible mechanism of reward deficits. The purpose of this paper is to review evidence for two components of this psychobiological pathway: 1) the effects of stress on reward function; and 2) the effects of inflammation on reward function. Within these two areas, we draw upon preclinical and clinical models, distinguish between acute and chronic effects of stress and inflammation, and address specific domains of reward dysregulation. By addressing these contextual factors, the review reveals a nuanced literature which might be targeted for additional scientific inquiry to inform the development of precise interventions.

Increased affective reactivity among depressed individuals can be explained by floor effects: An experience sampling study

Experience sampling studies into daily-life affective reactivity indicate that depressed individuals react more strongly to both positive and negative stimuli than non-depressed individuals, particularly on negative affect (NA). Given the different mean levels of both positive affect (PA) and NA between patients and controls, such findings may be influenced by floor/ceiling effects, leading to violations of the normality and homoscedasticity assumptions underlying the used statistical models. Affect distributions in prior studies suggest that this may have particularly influenced NA-reactivity findings. Here, we investigated the influence of floor/ceiling effects on the observed PA- and NA-reactivity to both positive and negative events. Data came from 346 depressed, non-depressed, and remitted participants from the Netherlands Study of Depression and Anxiety (NESDA). In PA-reactivity analyses, no floor/ceiling effects and assumption violations were observed, and PA-reactivity to positive events, but not negative events, was significantly increased in the depressed and remitted groups versus the non-depressed group. However, NA-scores exhibited a floor effect in the non-depressed group and naively estimated models violated model assumptions. When these violations were accounted for in subsequent analyses, group differences in NA-reactivity that had been present in the naive models were no longer observed. In conclusion, we found increased PA-reactivity to positive events but no evidence of increased NA-reactivity in depressed individuals when accounting for violations of assumptions. The results indicate that affective-reactivity results are very sensitive to modeling choices and that previously observed increased NA-reactivity in depressed individuals may (partially) reflect unaddressed assumption violations resulting from floor effects in NA.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Some Recommendations on the Use of Daily Life Methods in Affective Science

Real-world emotions are often more vivid, personally meaningful, and consequential than those evoked in the lab. Therefore, studying emotions in daily life is essential to test theories, discover new phenomena, and understand healthy emotional functioning; in short, to move affective science forward. The past decades have seen a surge of research using daily diary, experience sampling, or ecological momentary assessment methods to study emotional phenomena in daily life. In this paper, we will share some of the insights we have gained from our collective experience applying such daily life methods to study everyday affective processes. We highlight what we see as important considerations and caveats involved in using these methods and formulate recommendations to improve their use in future research. These insights focus on the importance of (i) theory and hypothesis-testing; (ii) measurement; (iii) timescale; and (iv) context, when studying emotions in their natural habitat.

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BACKGROUND

Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.

METHODS

Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.

RESULTS

Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles.

CONCLUSIONS

The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Transactional links between children daily emotions and internalizing symptoms: a six-wave ecological momentary assessment study

BACKGROUND

This study explored the cross-sectional and longitudinal associations between everyday emotion dimensions and internalizing symptoms during the transition to early adolescence. We tested associations between children's intensity and instability of daily negative emotions (NE), positive emotions (PE), and daily NE differentiation (NED) with children's self-reported and their mothers' report of children's internalizing symptoms, across six waves, each wave separated by six months.

METHODS

The sample included 199 ethnically diverse mother [M_age at baseline = 40.1 years (SD = 6.1] and child [M_age at baseline = 10.1 (SD = 0.90), 51% girls] dyads, who participated in six 7-day waves of ecological momentary assessment (EMA). During each wave, children reported on PE (i.e. happy and joyful) and NE (i.e. mad, sad, and stressed) up to eight random times per day through smartphone-based EMA. Children and mothers reported on children's internalizing symptoms at each wave. We used random-intercept cross-lagged panel models (RI-CLPMs) to test within- and between-person effects.

RESULTS

At the within-person level, increased NE and decreased PE intensity, more unstable NE and PE, and decreased NED at any given wave were positively associated with children's self-reported internalizing symptoms but not with mother-reported child symptoms. However, emotion dimensions did not predict child-reported nor mother-reported child symptoms at the next wave. At the between-person level, higher average NE, more unstable PE and NE, and lower NED were positively associated with average child-reported and mother-reported child internalizing symptoms.

CONCLUSIONS

This study suggests that emotional intensity, instability, and differentiation could be conceptualized as manifestations of internalizing symptoms but not as risk factors for its progression, or residual manifestations of it, among typical children.

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study

BACKGROUND

Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA.

OBJECTIVE

This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA.

METHODS

Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models.

RESULTS

The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not.

CONCLUSIONS

The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02542891, https://clinicaltrials.gov/ct2/show/NCT02542891; German Clinical Trials Register, DRKS00006866, https://tinyurl.com/ybja3xz7; Netherlands Trials Register, NTR4962, https://www.trialregister.nl/trial/4838; ClinicalTrials.Gov, NCT02389660, https://clinicaltrials.gov/ct2/show/NCT02389660; ClinicalTrials.gov, NCT02361684, https://clinicaltrials.gov/ct2/show/NCT02361684; ClinicalTrials.gov, NCT02449447, https://clinicaltrials.gov/ct2/show/NCT02449447; ClinicalTrials.gov, NCT02410616, https://clinicaltrials.gov/ct2/show/NCT02410616; ISRCTN registry, ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725.

Efficient estimation of bounded gradient-drift diffusion models for affect on CPU and GPU

Computational modeling plays an important role in a gamut of research fields. In affect research, continuous-time stochastic models are becoming increasingly popular. Recently, a non-linear, continuous-time, stochastic model has been introduced for affect dynamics, called the Affective Ising Model (AIM). The drawback of non-linear models like the AIM is that they generally come with serious computational challenges for parameter estimation and related statistical analyses. The likelihood function of the AIM does not have a closed form expression. Consequently, simulation based or numerical methods have to be considered in order to evaluate the likelihood function. Additionally, the likelihood function can have multiple local minima. Consequently, a global optimization heuristic is required and such heuristics generally require a large number of likelihood function evaluations. In this paper, a Julia software package is introduced that is dedicated to fitting the AIM. The package includes an implementation of a numeric algorithm for fast computations of the likelihood function, which can be run both on graphics processing units (GPU) and central processing units (CPU). The numerical method introduced in this paper is compared to the more traditional Euler-Maruyama method for solving stochastic differential equations. Furthermore, the estimation software is tested by means of a recovery study and estimation times are reported for benchmarks that were run on several computing devices (two different GPUs and three different CPUs). According to these results, a single parameter estimation can be obtained in less than thirty seconds using a mainstream NVIDIA GPU.

I feel good? Anhedonia might not mean "without pleasure" for people treated for opioid use disorder

Anhedonia is usually defined as partial or total loss of the capacity for pleasure. People with anhedonia in the context of major depressive disorder may have an unexpected capacity for event-related mood brightening, observable when mood is assessed dynamically (with smartphone-based ecological momentary assessment [EMA]) rather than only statically via questionnaire. We used EMA to monitor mood and pleasant events for 4 weeks in 54 people being treated with opioid agonist medication for opioid-use disorder (OUD), which is also associated with anhedonia, said to manifest especially as loss of pleasure from nondrug reward. We compared OUD patients' EMA reports with those of 47 demographically similar controls. Background positive mood was lower in OUD patients than in controls, as we hypothesized (Cohen ds = .85 to 1.32, 95% CIs [.66, 1.55]), although, contrary to our hypothesis, background negative mood was also lower (ds = .82 to .85, 95% CIs [.73, .94]). As hypothesized, instances of nondrug pleasure were as frequent in OUD patients as in controls-and were not rated much less pleasurable (d = .18, 95% CI [-.03, .35]). Event-related mood brightening occurred in both abstinent and nonabstinent OUD patients (ds = .18 to .37, CIs [-.01, .57]) and controls (ds = .04 to .60, CIs [-.17, .79]), brightening before each event began earlier for controls than OUD patients, but faded similarly postevent across groups. Our findings add to the evidence that anhedonia does not rule out reactive mood brightening, which, for people with OUD being treated on opioid agonist medication, can be elicited by nondrug activities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

[Positive cognitive neuroscience : Positive valence systems of the Research Domain Criteria initiative]

In diesem Artikel werden die Domäne „positive Valenzsysteme“ (PVS) der Research-Domain-Criteria(RDoC)-Matrix sowie ihre Subkonstrukte dargestellt und erläutert. Unter PVS fallen im Wesentlichen verschiedene Formen und Prozesse der Belohnungsverarbeitung. Diese werden in der Psychiatrie schon seit Jahrzehnten im Bereich von Sucht, Schizophrenie und Depression untersucht und letztere sind daher nicht Gegenstand dieses Artikels. Hier soll vielmehr die heuristische Fruchtbarkeit der RDoC-Systematik für das Verständnis anderer Erkrankungen und Konstrukte dargestellt werden und zwar für das transdiagnostische Konstrukt der Anhedonie sowie für die Autismusspektrumstörung und die Gruppe der Essstörungen. Weiterhin wird gezeigt, wie die PVS-Domäne auch klinisch den Blick über die traditionelle Psychopathologie erweitert und wie sie die Entwicklung neuer behavioraler Messinstrumente angeregt hat. Abschließend wird auf Limitationen und mögliche zukünftige Erweiterungen des Ansatzes eingegangen.

Discovering different profiles in the dynamics of depression based on real-time monitoring of mood: a first exploration

BACKGROUND

Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons.

METHODS

After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1-10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants.

RESULTS

Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively).

CONCLUSIONS

The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons.

The Short-Term Psychological Impact of the COVID-19 Pandemic in Psychiatric Patients: Evidence for Differential Emotion and Symptom Trajectories in Belgium

The spread of COVID-19 and the implementation of various containment strategies across the world have seriously disrupted people's everyday life, and it is especially uncertain what the psychological impact of this pandemic will be for vulnerable individuals, such as psychiatric (ex-)patients. Governments fear that this virus outbreak may prelude a major mental health crisis, and psychiatrists launch critical calls to flatten an upcoming mental ill-health surge. Here, we aim to add nuance to the idea that we are heading towards a mental health pandemic and that psychiatric populations will unavoidably (re)develop psychopathology. Despite being subjected to the same challenges posed by COVID-19, we argue that people with a history of psychiatric illness will psychologically deal with this adversity in different ways. To showcase the short-term differential impact of COVID-19 on patients' mental health, we present the day-to-day emotion and symptom trajectories of different psychiatric patients that took part in an experience sampling study before, during, and after the start of the first wave of the COVID-19 pandemic in March 2020 and associated lockdown measures in Belgium. Piecewise regression models show that not all patients' psychological well-being is affected to a similar degree. As such, we argue that emphasizing human resilience, also among the more vulnerable in society, may be opportune in these unsettling times.

[Anhedonia in depression: neurobiological and genetic aspects]

Anhedonia is indeed a pathogenetically important clinical phenotype and a promising endophenotype for depressive symptoms with a very high contribution of biological and genetic factors. Neurobiological mechanisms of anhedonia are impaired functioning of the reward system of the brain, which is confirmed by many neuroimaging, genetic and experimental studies. Anhedonia has a trans-diagnoctic character and should be understood as a complex phenomenon, and it is important to correctly evaluate it within the framework of a particular research paradigm. It seems optimal to form several complementary research strategies that evaluate the most important «facets» of anhedonia, regardless of the nosological form of the disease, within the framework of one study using various methods to search for adequate biomarkers of anhedonia severity (genetic, neuroimaging, biochemical). Given the high-quality organization of such comprehensive studies based on the correct methodology of evidence-based medicine, it is likely that significant biomarker systems will be available in the near future, which, if replicated in independent samples, can be used to personalize the diagnosis and treatment of depression.

A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy

Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between observations will lead to an advantage for the OU in terms of predictive accuracy. In this paper, this is claim is being investigated by comparing the predictive accuracy of the OU model to that of the VAR(1) model on typical ESM data obtained in the context of affect research. It is shown that the VAR(1) model outperforms the OU model for the majority of the time series, even though time intervals in the data are unequally spaced. Accounting for measurement error does not change the result. Deleting large abrupt changes on short time intervals (that may be caused by externally driven events) does however lead to a significant improvement for the OU model. This suggests that processes in psychology may be continuously evolving, but that there are factors, like external events, which can disrupt the continuous flow.

Anhedonia, positive affect dysregulation, and risk and maintenance of binge-eating disorder

Low positive affect has been identified as an antecedent of binge-eating episodes among individuals with binge-eating disorder (BED), yet positive affect has received far less attention in eating disorders research than its counterpart, negative affect. In this article, we argue that the low levels of positive affect which occur with anhedonia (i.e., loss of interest or pleasure in activities) may contribute to the onset and maintenance of BED. We introduce a theoretical model in which anhedonia increases the risk for BED through its interrelationships with dysregulated eating and weight gain, and we describe potential direct (e.g., reward-related processes) as well as indirect (e.g., influences on depressive symptoms and physical activity) pathways by which anhedonia may lead to adverse eating- and weight-related outcomes. We also propose a momentary maintenance model in which low positive affect and positive affect dysregulation occurring with anhedonia maintain binge eating directly and indirectly through maladaptive health behaviors, such as decreased physical activity, less healthy eating, and fewer social interactions, which in turn maintain anhedonia. We draw upon outside literature to present evidence that aligns with the proposed risk and maintenance models and conclude by outlining avenues for future research-including methodological/measurement, theoretical, and clinical research directions.

Negative mood affects the expression of negative but not positive emotions in mice

Whether and to what extent animals experience emotions is crucial for understanding their decisions and behaviour, and underpins a range of scientific fields, including animal behaviour, neuroscience, evolutionary biology and animal welfare science. However, research has predominantly focused on alleviating negative emotions in animals, with the expression of positive emotions left largely unexplored. Therefore, little is known about positive emotions in animals and how their expression is mediated. We used tail handling to induce a negative mood in laboratory mice and found that while being more anxious and depressed increased their expression of a discrete negative emotion (disappointment), meaning that they were less resilient to negative events, their capacity to express a discrete positive emotion (elation) was unaffected relative to control mice. Therefore, we show not only that mice have discrete positive emotions, but that they do so regardless of their current mood state. Our findings are the first to suggest that the expression of discrete positive and negative emotions in animals is not equally affected by long-term mood state. Our results also demonstrate that repeated negative events can have a cumulative effect to reduce resilience in laboratory animals, which has significant implications for animal welfare.

The Affective Ising Model: A computational account of human affect dynamics

The human affect system is responsible for producing the positive and negative feelings that color and guide our lives. At the same time, when disrupted, its workings lie at the basis of the occurrence of mood disorder. Understanding the functioning and dynamics of the affect system is therefore crucial to understand the feelings that people experience on a daily basis, their dynamics across time, and how they can become dysregulated in mood disorder. In this paper, a nonlinear stochastic model for the dynamics of positive and negative affect is proposed called the Affective Ising Model (AIM). It incorporates principles of statistical mechanics, is inspired by neurophysiological and behavioral evidence about auto-excitation and mutual inhibition of the positive and negative affect dimensions, and is intended to better explain empirical phenomena such as skewness, multimodality, and non-linear relations of positive and negative affect. The AIM is applied to two large experience sampling studies on the occurrence of positive and negative affect in daily life in both normality and mood disorder. It is examined to what extent the model is able to reproduce the aforementioned non-Gaussian features observed in the data, using two sightly different continuous-time vector autoregressive (VAR) models as benchmarks. The predictive performance of the models is also compared by means of leave-one-out cross-validation. The results indicate that the AIM is better at reproducing non-Gaussian features while their performance is comparable for strictly Gaussian features. The predictive performance of the AIM is also shown to be better for the majority of the affect time series. The potential and limitations of the AIM as a computational model approximating the workings of the human affect system are discussed.

Feelings color and guide our lives. Understanding their dynamics is a crucial step on the way to eventually understanding mood disorders such as depression. In this paper, we propose a model for the dynamics of positive and negative affect, called the Affective Ising Model (AIM). Starting from a neurobiologically inspired yet abstract microscopic representation of how affect is generated, the model predicts the presence of a number of nonlinear phenomena in the dynamics of positive and negative affect. These nonlinear phenomena include skewed distributions, bimodality (people’s affect can fluctuate around one of two possible states) and a V-shaped relation between positive and negative affect. These nonlinear signature features have been empirically established, but have thus far not been integrated into a single computation model. The AIM can be used in the future to explain both normal and dysfunctional affect.

Recovery from daily-life stressors in early and chronic psychosis

Initial affective and psychotic reactivity to daily stressors is altered in psychosis, and most notably in early psychosis. In addition to altered initial stress reactivity, results from studies using Experience Sampling Methodology (ESM) and psychophysiological measures indicate that impaired recovery from mild stressors may also be a risk factor for mental illness. The current ESM study investigated affective recovery from daily stressors in chronic psychosis patients (CP; n = 162), individuals at early stages of psychosis (EP; n = 127), and healthy volunteers (HV; n = 220) assessing fluctuations in negative affect (NA), tension, and suspiciousness ten times a day on six consecutive days. Recovery was operationalized for all three variables as the return to baseline (i.e., level at t_-1) following the first stressful event of a day (i.e., t₀). The EP group showed a delayed recovery of NA (t₁-t₃: B = 0.185; p = .007 and B = 0.228; p = .002) and suspiciousness (t₁: B = 0.223; p = .010 and B = 0.291; p = .002) compared to HV and CP, respectively. Delayed recovery was detected for tension as well (t₁-t₂: EP > HV: B = 0.242; p = .040 and EP > CP: B = 0.284; p = .023), but contrary to both other momentary states, this effect disappeared when controlling for subsequent stressful events. There were no significant differences in recovery between HV and CP. These results suggest that in EP, stressful daily events have longer-lasting effects on overall negative affect and subclinical psychotic-like experiences. Future studies should incorporate physiological and endocrine measures in order to integrate recovery patterns of the different stress systems.

Diminished Anticipatory and Consummatory Pleasure in Dysphoria: Evidence From an Experience Sampling Study

Anhedonia, the experience of diminished pleasure, is a core feature of major depressive disorder and is often present long before the diagnosis of depression. Most previous studies have investigated anhedonia with self-report measures of trait anhedonia or with behavioral paradigms using laboratory stimuli, and the real-time characteristics of hedonic processing in subclinical depression remain under-investigated. We used the experience sampling method to evaluate momentary experience of hedonic feelings in the context of daily life. Dysphoric (n = 49) and non-dysphoric (n = 51) college students completed assessments of their current positive affect (PA), as well as state anticipatory and consummatory pleasure, 3 or 4 times a day every day for 2 weeks. The results showed that dysphoric individuals reported less state anticipatory and consummatory pleasure compared with non-dysphoric individuals. Moreover, significant time-lagged associations between anticipatory pleasure and follow-up consummatory pleasure were found in the whole sample, after adjustment for current PA. The current findings thus hold considerable promise in advancing our understanding of anhedonia as well as the important role of state anticipatory pleasure in relation to depression.