Emotion and Emotion Preferences in Daily Life: The Role of Anxiety

An integrative analysis of potential mechanisms of reduced positive affect in daily life in depression: an ESM study

Decreased levels of positive affect (PA) are a hallmark of depression. Current models propose as potential main mechanisms a dysfunctional use of emotion regulation (ER) strategies (i.e. dampening, positive rumination), and a maladaptive activation of pro-hedonic goals. However, the role of these mechanisms in PA in daily life remains understudied. We used a 10-day ESM design to assess how these mechanisms influence each other and contribute to depressive symptomatology-related low momentary PA in 139 individuals. Higher depressive symptoms were associated with lower PA, pro-hedonic goals, more frequent use of positive rumination, and higher use of dampening. Further, experiencing higher levels of PA predicted lower following time point use of dampening in individuals with higher symptoms. Finally, using positive rumination was more beneficial (i.e. predicted higher PA increases one moment later) for individuals with higher symptomatology. Our findings suggest that moment-to-moment changes in PA daily life are affected by, and have an effect on, both pro-hedonic goals and the use of dampening and positive rumination, highlighting specific cognitive-affective mechanisms that should be considered when designing interventions aimed at improving low PA characterising depression symptomatology.

Internet-based identification of anxiety in university students using text and facial emotion analysis

Background

Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students.

Objective

The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data.

Methods

The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety.

Results

Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %).

Conclusions

The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.

Valuing Negative Affect Weakens Affect-Health Linkages: Similarities and Differences Across Affect Valuation Measures

Negative affect (NA) has been robustly linked to poorer psychological health, including greater depressive symptoms, personal burnout, and perceived stress. These associations, known as affect-health links, have been postulated by our research team to vary with different levels of negative affect valuation (NAV), such that people who evaluate NA states as more pleasant, helpful, appropriate, and/or meaningful may show weaker affect-health links. Another affect valuation construct is ideal NA, which is the degree to which people ideally want to experience NA states (i.e., desirability of affective states). The current study extends previous research by examining these two different measures of affect valuation (NAV and ideal NA) and comparing the extent to which they moderate affect-health links for psychological health and functioning. Participants from the Health and Daily Experiences (HEADE) study (N = 162 comprising of 56 younger adults and 106 older adults) completed questionnaires in a laboratory setting and ecological momentary assessments of NA 6 times a day for 7 consecutive days (i.e., trait NA). The results demonstrated that the two affect valuation constructs were distinct and showed different patterns of buffering effects. NAV attenuated the association between trait NA and depressive symptoms, personal burnout, and intolerance of uncertainty. Ideal NA attenuated affect-health links for depressive symptoms and perceived stress. These findings point to the importance of sharpening the distinctions between various affect valuation constructs to elucidate their unique contributions to attenuating affect-health links.

Structural connectome-based prediction of trait anxiety

Neurobiological research on anxiety has shown that trait-anxious individuals may be characterized by weaker structural connectivity of the amygdala-prefrontal circuitry, representing a reduced capacity for efficient communication between the two brain regions. However, comparison of available studies has been inconsistent, possibly related to factors such as aging that influences both trait anxiety and structural connectivity of the brain. To help clarify the nature of brain-anxiety relationship, we applied a connectome-based predictive modeling framework on 148 diffusion-weighted imaging data from the Leipzig Study for Mind-Body Emotion Interactions dataset and identified multivariate patterns of whole-brain structural connectivity that predicted trait anxiety. Results showed that networks predictive of trait anxiety differed across age groups. Specifically, an isolated negative network, which shared overlapping features with the amygdala-prefrontal circuitry, was found in younger adults (20-30 years of age), whereas a widespread positive network highlighted by frontotemporal and frontolimbic connectivity was identified when both younger and older adults (20-80 years of age) were examined. No predictive network was observed when only older adults (30-80 years of age) were considered. Our findings highlight an important age-dependent effect on the structural connectome-based prediction of trait anxiety, supporting ongoing efforts to develop potential neural biomarkers of anxiety.