Affective dynamics in bipolar spectrum psychopathology: Modeling inertia, reactivity, variability, and instability in daily life

An initial test of the contrast avoidance model in bipolar spectrum disorders

The Contrast Avoidance Model suggests that individuals sensitive to negative emotional shifts use prior increases in negative affect to prevent further escalation in response to adverse situations, while the heightened negative affect amplifies positive emotional contrasts when encountering unexpected positive events. Individuals with bipolar spectrum disorders (BSDs), characterized by shifts between (hypo)manic and depressive episodes, may undergo more salient emotional contrasts. Drawing from the Contrast Avoidance Model, the shifts from depression to (hypo)mania can be conceptualized as positive emotional contrasts, potentially heightening the perceived pleasure during (hypo)manic episodes. On the other hand, the shifts from (hypo)manic to depressive episodes can be viewed as negative emotional contrasts, contributing to the challenges associated with depressive states. Despite the intriguing potential of this interplay, the link between the Contrast Avoidance Model and BSDs has never been empirically tested. Our study addressed this gap by examining group differences in contrast avoidance traits between individuals with BSDs, unipolar depression, and healthy controls in a large cohort study (N = 536). Results indicated that individuals with BSDs exhibited significantly higher scores in the total, and Discomfort with Negative Emotional Shifts and Avoidance of Negative Emotional Contrasts/Enhancement of Positive Emotional Contrasts factors, as well as separate item scores on the Contrast Avoidance Questionnaire-General Emotion (CAQ-GE), compared to those with unipolar depression and healthy controls. Although marginal, the BD II subtype demonstrated a stronger inclination to avoid negative emotional contrasts compared to BD I. These findings suggest that contrast avoidance may be a psychological mechanism implicated in BSDs.

Limited evidence of autocorrelation signaling upcoming affective episodes: a 12-month e-diary study in patients with bipolar disorder

BACKGROUND

Increased autocorrelation (AR) of system-specific measures has been suggested as a predictor for critical transitions in complex systems. Increased AR of mood scores has been reported to anticipate depressive episodes in major depressive disorder, while other studies found AR increases to be associated with depressive episodes themselves. Data on AR in patients with bipolar disorders (BD) is limited and inconclusive.

METHODS

Patients with BD reported their current mood via daily e-diaries for 12 months. Current affective status (euthymic, prodromal, depressed, (hypo)manic) was assessed in 26 bi-weekly expert interviews. Exploratory analyses tested whether self-reported current mood and AR of the same item could differentiate between prodromal phases or affective episodes and euthymia.

RESULTS

A total of 29 depressive and 20 (hypo)manic episodes were observed in 29 participants with BD. Self-reported current mood was significantly decreased during the two weeks prior to a depressive episode (early prodromal, late prodromal), but not changed prior to manic episodes. The AR was neither a significant predictor for the early or late prodromal phase of depression nor for the early prodromal phase of (hypo)mania. Decreased AR was found in the late prodromal phase of (hypo)mania. Increased AR was mainly found during depressive episodes.

CONCLUSIONS

AR changes might not be better at predicting depressive episodes than simple self-report measures on current mood in patients with BD. Increased AR was mostly found during depressive episodes. Potentially, changes in AR might anticipate (hypo)manic episodes.

Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

Craving dynamics and related cerebral substrates predict timing of use in alcohol, tobacco, and cannabis use disorders

Background

Patients treated for Substance Use Disorders exhibit highly fluctuating patterns of craving that could reveal novel prognostic markers of use. Accordingly, we 1) measured fluctuations within intensively repeated measures of craving and 2) linked fluctuations of craving to connectivity indices within resting-state (rs) brain regions to assess their relation to use among patients undergoing treatment for Alcohol, Tobacco and Cannabis Use Disorders.

Method

Participants -64 individuals with SUD for tobacco, alcohol, or cannabis and 35 healthy controls-completed a week of Ecological Momentary Assessment (EMA) during which they reported craving intensity and substance use five times daily. Before EMA, a subsample of 50 patients, and 34 healthy controls also completed resting-state (rs)-MRI acquisitions. Craving temporal dynamics within each day were characterized using Standard Deviation (SD), Auto-Correlation Factor (ACF), and Mean Successive Square Difference (MSSD). Absolute Difference (AD) in craving between assessments was a prospective prediction measure.

Results

Within-day, higher MSSD predicted greater substance use while controlling for mean craving. Prospectively higher AD predicted later increased substance use independently of previous use or craving level. Moreover, MSSD was linked to strength in five functional neural connections, most involving frontotemporal systems. Cerebello-thalamic and thalamo-frontal connectivity were also linked to substance use and distinguished the SUD from the controls.

Conclusion

To the best of our knowledge, this is the first study to indicate that instability in craving may be a trigger for use in several SUD types, beyond the known effect of craving intensity.

Moment-to-moment affective dynamics in schizophrenia and bipolar disorder

BACKGROUND

Affective disturbances in schizophrenia and bipolar disorder may represent a transdiagnostic etiological process as well as a target of intervention. Hypotheses on similarities and differences in various parameters of affective dynamics (intensity, successive/acute changes, variability, and reactivity to stress) between the two disorders were tested.

METHODS

Experience sampling method was used to assess dynamics of positive and negative affect, 10 times a day over 6 consecutive days. Patients with schizophrenia (n = 46) and patients with bipolar disorder (n = 46) were compared against age-matched healthy controls (n = 46).

RESULTS

Compared to controls, the schizophrenia group had significantly more intense momentary negative affect, a lower likelihood of acute changes in positive affect, and reduced within-person variability of positive affect. The bipolar disorder group was not significantly different from either the schizophrenia group or the healthy control group on any affect indexes. Within the schizophrenia group, level of depression was associated with weaker reactivity to stress for negative affect. Within the bipolar disorder group, level of depression was associated with lower positive affect.

CONCLUSIONS

Patients with schizophrenia endured a more stable and negative affective state than healthy individuals, and were less likely to be uplifted in response to happenings in daily life. There is little evidence that these affective constructs characterize the psychopathology of bipolar disorder; such investigation may have been limited by the heterogeneity within group. Our findings supported the clinical importance of assessing multiple facets of affective dynamics beyond the mean levels of intensity.

Transdiagnostic Associations With Interpersonal and Affective Variability in Borderline Personality Pathology

Emotional and behavioral variability are unifying characteristics of borderline personality disorder (BPD). Ambulatory assessment (AA) has been used to quantify this variability in terms of the categorical BPD diagnosis, but evidence suggests that BPD instead reflects general personality pathology. This study aimed to clarify the conceptualization of BPD by mapping indices of variability in affect, interpersonal behavior, and perceptions of others onto general and specific dimensions of personality pathology. A sample of participants who met diagnostic criteria for BPD (n = 129) and healthy controls (n = 47) reported on their daily interactions during a 21-day AA protocol. Multilevel SEM was used to examine associations between shared and specific variance in maladaptive traits with dynamic patterns of functioning. The authors found that variability is an indicator of shared trait variance and Negative Affectivity, not any other specific traits, reinforcing the idea that BPD is best understood as general personality pathology.

Emotional awareness, affective dysregulation, and bipolar spectrum psychopathology: A path analysis

Affective dysregulation is present in those with subsyndromal symptoms of hypomania and mania and prospectively predicts the development of bipolar spectrum disorders. A crucial, understudied area related to the experience and regulation of emotion in this population is emotional awareness - emotional clarity (Clarity) and attention to emotion (Attention). We examined whether scores on the Hypomanic Personality Scale (HPS) were associated with deficits in emotional awareness and whether these deficits were associated with heightened intensity and instability of negative (NA) and positive affect (PA). Young adults (n=233), oversampled for high HPS scores completed self-reports and 14 days of experience sampling assessing high and low arousal NA and PA. HPS scores were associated with low Clarity and unassociated with Attention. High HPS scores were associated with greater high and low arousal NA intensity and instability only for those at low and mean levels of Attention. In contrast, there was a significant indirect association between HPS scores and intensity of high and low arousal NA and PA, as well as instability of high arousal NA, through low Clarity. Results highlighted that individual differences exist in the extent to which facets of emotional awareness differentially link scores on the HPS to emotional outcomes.

Emotion Network Analysis During COVID-19 Quarantine - A Longitudinal Study

Introduction: The coronavirus disease 2019 (COVID-19) emergency has imposed important challenges in the lives of individuals, particularly since the restriction of free movement. In Spain, this mandatory home confinement started on March 14, 2020. In this scenario, some calls have been made to better understand the exact impact of the quarantine on the emotional status of individuals across time. Materials and Methods: On the first day that the Spanish government imposed the quarantine, our team launched an online longitudinal study to monitor emotional responses to the COVID-19 emergency over time. For 2 weeks, 187 people have responded to a daily diary on emotion functioning. An emotion network analysis was performed to study the network structure of 30 mood states and its changes during the first 2 weeks of the quarantine. Results: The emotional network showed critical changes in the interactions of emotions over time. An analysis of mean emotional levels did not show statistically significant changes in mood over time. Interestingly, two different network patterns were found when the sample was divided between those with favorable responses and those with unfavorable responses. Discussion: This new approach to the study of longitudinal changes of the mood state network of the population reveals different adaptation strategies reflected on the sample's emotional network. This network approach can help identify most fragile individuals (more vulnerable to external stressors) before they develop clear and identifiable psychopathology and also help identify anti-fragile individuals (those who improve their functioning in the face of external stressors). This is one of the first studies to apply an emotional network approach to study the psychological effects of pandemics and might offer some clues to psychologists and health administrators to help people cope with and adjust to this critical situation.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.

Emotion dynamics concurrently and prospectively predict mood psychopathology

INTRODUCTION

Altered emotion dynamics may represent a transdiagnostic risk factor for mood psychopathology. The present study examined whether altered emotion dynamics were associated with bipolar and depressive psychopathology concurrently and at a three-year follow-up.

METHODS

At baseline (n = 138), participants completed diagnostic interviews, questionnaires, and seven days of experience sampling assessments. Four emotion dynamics were computed for negative affect (NA) and positive affect (PA) - within-person variance (variability), mean square of successive differences and probability of acute change (instability), and autocorrelation (inertia). At the three-year follow-up, participants (n = 108) were re-assessed via interviews and questionnaires.

RESULTS

NA variability was associated with bipolar spectrum disorders at baseline and follow-up. NA instability predicted depressive symptoms and hypomanic personality at baseline, and bipolar spectrum disorders at the follow-up. NA inertia did not predict diagnoses or symptoms at either assessment. PA inertia predicted hyperthymic temperament at baseline but not follow-up. Notably, NA variability and instability predicted the development of new bipolar spectrum disorders at the follow-up.

LIMITATIONS

Consistent with the recruitment strategy and young age of the participants, only 50% had developed diagnosable psychopathology by the time of the follow-up assessment.

CONCLUSIONS

The present study provided a unique demonstration that altered emotion dynamics differentially predicted bipolar and depressive psychopathology concurrently and prospectively. Emotion dynamics are important to both digital phenotyping and mobile-based interventions as emotional instability offers a measurable risk factor that is identifiable prior to illness onset.