Change in Endogenous Pain Modulation Depending on Emotional States in Healthy Subjects: A Randomized Controlled Trial

INTRODUCTION

Chronic pain is a public health issue, leading to substantial healthcare costs and diminished quality of life for sufferers. While the role of anxiety in pain modulation has been extensively studied, the effects of other emotional states on the body's pain control mechanisms remain less understood. This study sought to explore how different emotions (happiness, anger, sadness, and interest) affect conditioned pain modulation (CPM) and the wind-up phenomenon in healthy adults.

METHODS

This randomized controlled, cross-over trial involved 28 healthy participants aged 18-60. Participants watched video clips designed to induce specific emotions: happiness, anger, sadness, and interest. Emotional states were assessed using a 7-point Likert scale. Pain modulation was measured using CPM and the wind-up phenomenon. CPM was assessed with a hot water bath as the conditioning stimulus and pressure pain tolerance as the test stimulus. Wind-up was measured using pinprick needle stimulators and a visual analog scale. Data were analyzed using paired t tests to compare pre- and post-emotion induction values.

RESULTS

Significant changes in emotional self-assessment values were observed for all emotions. Happiness increased CPM (4.6 ± 11.4, p = 0.04277), while sadness - 9.9 ± 23.1, p = 0.03211) and anger - 9.1 ± 23.3, p = 0.04804) decreased it. Interest did not significantly alter CPM (- 5.1 ± 25.8, p = 0.31042). No significant effects were found for the wind-up phenomenon across any emotional states.

CONCLUSION

This study shows that emotional states significantly affect the body's ability to modulate pain. Positive emotions like happiness enhance pain inhibition, while negative emotions such as sadness and anger impair it. These findings suggest that emotional modulation techniques could be integrated into pain management strategies to improve patient outcomes. Further research should explore a broader range of emotions and include objective measures to validate these results.

Depressive symptoms and loneliness among early adolescents: a psychometric network analysis approach

BACKGROUND

Previous studies demonstrate a high prevalence of depression and loneliness among adolescents. Although they often co-occur, the relationship between symptoms of depression and loneliness remains poorly understood. This study investigates: (a) the symptoms of depression that are connected to loneliness; (b) the role played by loneliness in the network of depression symptoms; and (c) whether the method used to measure loneliness (single-item direct or multi-item indirect) affects the relationship of loneliness with depressive symptoms.

METHODS

Participants were 496 Polish adolescents (50.8% girls) aged 11 to 13, who completed: (a) the 10-item Major Depressive Disorder subscale of the Revised Child Anxiety and Depression Scale; (b) the 11-item De Jong Gierveld Loneliness Scale (indirect loneliness), and (c) a single direct question evaluating loneliness: 'I'm lonely'. Networks were estimated using a Gaussian Graphical Model.

RESULTS

Loneliness shows a direct relationship with three affective symptoms of depression: sadness, worthlessness, and anhedonia, which mediate relationships with somatic symptoms. In contrast to previous studies, loneliness has the lowest level of centrality among all elements of the network. The method used to assess loneliness did not significantly affect the connections between loneliness and depressive symptoms.

CONCLUSIONS

Loneliness and depression overlap since they are formed by the same cognitive biases and deficits in emotion regulation but differ in the level of generality. In loneliness, they have an interpersonal context, while symptoms of depression can be intrapersonal. This helps us to understand why cognitive interventions, as compared to those which are social, are more effective in reducing loneliness.

Amelioration of modified chronic unpredictable stress using Celastrus paniculatus seed oil alone and in combination with fluoxetine

The various stressors in chronic unpredictable stress (CUS) triggers depressive behavior, impairs learning, and decision-making abilities. The present study investigated the effects of Celastrus paniculatus seed oil (CPSO) alone and in combination with fluoxetine (FLU) in modified CUS (mCUS) induced depression in mice. In this study, adult albino mice were subjected to a modified version of CUS protocol having six different stressors and were applied daily consistently for 15 days. The post-treatment with CPSO (50 and 100 mg/kg) and FLU (10 mg/kg) alone and in combination from day 16th to 36th. Group I: normal control; group II: diseased control (mCUS subjected group); group III: CPSO (50 mg/kg); group IV: CPSO (100 mg/kg); group V: CPSO (50 mg/kg)+FLU (10 mg/kg); group VI: CPSO (100 mg/kg)+FLU (10 mg/kg); group VII: FLU (10 mg/kg); group VIII: FLU (20 mg/kg). During experimentation, various behavioral, biochemical, oxidative stress, inflammatory, and neurotransmitters level were checked. The CUS treated mice exhibited increased escaped latency, decreased number of open arm entries, increased immobility time, decreased percentage of sucrose consumption, and number of the boxes crossed as compared to the normal group. The post-treatment with the CPSO 50 + FLU 10, CPSO 100 + FLU 10, FLU 10 significantly (p < 0.05) attenuated behavioral, biochemical, inflammation, corticosteroid, and neurotransmitters level as compared to CPSO 50, CPSO 100, and FLU 20 alone. CPSO along with FLU appreciably achieved anti-depressant effect via lowering stress, inflammation, corticosteroid level, and restoration of neurotransmitters level in mCUS induced depression mice model.

Predicting Academic Performance: Analysis of Students' Mental Health Condition from Social Media Interactions

Social media have become an indispensable part of peoples' daily lives. Research suggests that interactions on social media partly exhibit individuals' personality, sentiment, and behavior. In this study, we examine the association between students' mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students' psychological attributes and mental health issues will be predicted from their social media interactions. Then, students' academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students' Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users' social media usage and their psychological attributes and mental health status and (ii) users' psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students' Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.

When and Why Are Emotions Disturbed? Suggestions Based on Theory and Data From Emotion Research

Diagnosing emotion disturbances should be informed by current knowledge about normal emotion processes. I identify four major functions of emotion as well as sources for potential dysfunctions and suggest that emotions should only be diagnosed as pathological when they are clearly dysfunctional, which requires considering eliciting events, realistic person-specific appraisal patterns, and adaptive responses or action tendencies. Evidence from actuarial research on the reported length of naturally occurring emotion episodes (including potential determinants) illustrates appropriateness criteria for the clinical evaluation of emotion duration—an essential element in the DSM-5 symptom catalogue for major depression episodes, especially in bereavement. The need for more actuarial evidence on normal emotion responses and its consideration by the clinical community is highlighted.

The Appraisal Bias Model of Cognitive Vulnerability to Depression

Models of cognitive vulnerability claim that depressive symptoms arise as a result of an interaction between negative affect and cognitive reactions, in the form of dysfunctional attitudes and negative inferential style. We present a model that complements this approach by focusing on the appraisal processes that elicit and differentiate everyday episodes of emotional experience, arguing that individual differences in appraisal patterns can foster negative emotional experiences related to depression (e.g., sadness and despair). In particular, dispositional appraisal biases facilitating the elicitation of these emotions more frequently and more intensely. This, in turn, is likely to have a negative influence on cognitive processing and emotion regulation in general.