Emotional Network Research Articles

The Establishment and Optimization of Public Emotion Network Communication Model using Deep Learning

This work aims to construct and optimize the public emotion network communication model under the artificial intelligence (AI) environment and provide a new research direction for exploring public emotion network communication with intelligent human–computer interaction (HCI). The main challenge of this research is the lack of interactivity of intelligent human–computer expression design, and the accuracy of emotion recognition is not high. First, the theoretical analysis of network public opinion is made, and the network public opinion calculation method and emotion recognition method based on deep learning are proposed. On this basis, the public emotion network communication model is established, and the model’s communication mechanism and rules are described in detail. Next, the emotion calculation of human–computer expression interaction and the emotion expression mode of the humanoid robot are proposed, which provides an experimental basis for HCI and public emotion network communication analysis. According to the data of email network and microblog network in the real network, the network degree distribution map is established. The statistical results show that email and microblog networks belong to power law distribution, and the aggregation coefficient is relatively high. Microblog network degree changes slowly, and more points are scattered in the middle area. In a practical sense, when the positive and negative emotions in the public emotion network communication model are input into the bionic robot, the robot can show happy and lost expressions. The results show that compared with the traditional convolutional neural network (CNN) model or recurrent neural network (RNN) model, the RNN–CNN structure model of RNN combined with CNN reduces the waiting time by about 20% and improves the algorithm accuracy by at least 3.1%. The public emotion network communication model based on deep learning and intelligent HCI can accurately reflect the public emotion state in the network, which provides a practical basis for the application of AI technology in network public opinion judgment.

Age Estimation and Gender Recognition Technique Using Deep Learning

Abstract: Using a neural convolution network, this research presents an automatic system for recognising age and gender based on a person's face. In recent years, one of the most effective research subjects has been collecting information from a person's face. The human face has facial features such as eyes, ears, nose, chin, and other aspects that can be examined based on our requirements. The detection of age and gender begins with facial recognition. Detection is the process of identifying many features based on a single input. Our system makes use of OpenCV and includes a few deep convolutional emotional networks that have been trained to detect faces from inserted images and predict their age and gender using either a webcam or an image. Not only are deep neural convolutional networks costly, but they are also difficult to implement. Keywords: Age Estimation, Gender Detection, Deep Learning, Webcam, Convolutional neural network, OpenCV.

The dysregulation of top-down control in individuals with high test anxiety: A resting state fMRI study

BackgroundIndividuals with high test anxiety (HTA) have deficits in attentional control and in stress responses when faced with tests. However, little is known about the underlying neural mechanism. Anterior cingulate cortex (ACC) is involved in both attention and emotion networks, therefore this study examined the neural functional dysregulation in test anxiety from the perspective of functional connectivity (FC) using bilateral ACC as the regions of interest. MethodsFifty-one participants were divided into HTA (n = 23) and low test anxiety (LTA, n = 28) group according to their Test Anxiety Scale (TAS) scores. Brain imaging data in resting, preparing, and recovering phases of a modified social evaluative threat task were collected, and emotional changes were assessed. ResultsCompared with the LTA group, the HTA group exhibited significantly lower FCs between the ACC and superior frontal gyrus (SFG) in all 3 phases, significantly lower FCs between the ACC and inferior parietal gyrus (IPG), and significantly higher FCs between the ACC superior temporal gyrus (STG) in the preparing phase. Moreover, in the HTA group, the resting state IPG-ACC FC was associated with their TAS score, the preparing state STG-ACC FC was associated with the increased anxiety. Conclusions: Individuals with HTA have general prefrontal control deficits. When facing a test, they tend to recruit more resources to deal with high emotional interference. The dysregulated control of the ACC by the frontal-parietal network may underlie the pathophysiology of test anxiety.

Effects of a 6-Month Aerobic Exercise Intervention on Mood and Amygdala Functional Plasticity in Young Untrained Subjects.

Acute exercise has beneficial effects on mood and is known to induce modulations in functional connectivity (FC) within the emotional network. However, the long-term effects of exercise on affective brain circuits remain largely unknown. Here, we investigated the effects of 6 months of regular exercise on mood, amygdala structure, and functional connectivity. This study comprised N = 18 healthy sedentary subjects assigned to an intervention group (IG; 23.9 ± 3.9 years; 3 trainings/week) and N = 10 subjects assigned to a passive control group (CG; 23.7 ± 4.2 years). At baseline and every two months, performance diagnostics, mood questionnaires, and structural and resting-state-fMRI were conducted. Amygdala-nuclei segmentation and amygdala-to-whole-brain FC analysis were performed. Linear mixed effects models and correlation analyses were conducted between FC, relVO2max, and mood scores. Data showed increases in relVO2max exclusively in the IG. Stronger anticorrelation in amygdala-precuneus FC was found, along with a stronger positive correlation in the amygdala-temporal pole FC in the IG after 4 and 6 months, while mood and amygdala volume did not reveal significant interactions. The relVO2max/amygdala-temporal pole FC correlated positively, and the amygdala-precuneus/amygdala-temporal pole FC correlated negatively. Findings suggest that exercise induced long-term modulations of the amygdala FC with the precuneus and temporal pole, shedding light on potential mechanisms by which exercise has positive influences on mood-related networks, typically altered in affective disorders.

Social Exclusion in the Context of Social Identity of the Transgender Community in India: An Inclusive Approach

Transgender community are the most vulnerable and stigmatized groups in any society. Due to their gender identity, they are rejected by their families and communities and are often socially marginalized. The COVID-19 pandemic has diminished transgender individuals’ access to the critical emotional and instrumental social support networks that are vital to their well-being. Exclusion of transgender people made their day to day life more challenging. Consequently, many rely on Government, peer networks and voluntary organizations for social support (e.g., Government supported centres, Community LGBTQ centers, Meetup groups). The framework of the present paper will be focusing on the social exclusion of transgender community during this pandemic times and to deliberate the inclusive measures to face the pandemic situations. Key words: Social exclusion, Transgender, Social identity, pandemic, social inclusion

Comment: Old Wine in New Bags—Suri and Gross's Connectionist Theory of Emotion is Another Type of Network Theory

Suri and Gross's 2022 connectionist emotion theory can be considered as one version of a family of theories known as network theories of emotion. It presents similarities and differences with older versions of network theories. Like previous network theories and several other traditional emotion theories, however, the connectionist theory remains a reactive theory. The class of reactive theories can be meaningfully contrasted with a class of instrumental theories of which the goal-directed theory is a representative example. Although the latter theory does not deny the existence of emotion networks in memory, it does not grant them many causal powers, thereby seriously restricting their explanatory territory. Future research efforts may help disambiguate between both classes of theories.

Anticipatory anxiety of seizures is associated with ictal emotional distress and amygdala onset seizures

Anxiety disorders are a frequent psychiatric condition in patients with epilepsy. Anticipatory anxiety of seizures (AAS) is described as a daily persistent fear or excessive worry of having a seizure. AAS seems to be related to "subjective ictal anxiety" reported by patients. The current study aimed to assess the association between objective ictal features and the presence of AAS. Ninety-one patients with drug-resistant focal epilepsy underwent a standardized psychiatric assessment, specific for epilepsy, and presurgical long-term video-electroencephalography (EEG) or stereo-EEG (SEEG). We compared seizure semiology and epilepsy features of patients with AAS (n=41) to those of patients without AAS (n=50). We analyzed emotional and motor behavior ictal signs as well as ictal consciousness. We further assessed amygdala ictal involvement in seizures recorded with SEEG (n=28). AAS was significantly associated with the presence of ictal emotional distress; negative emotional behavior (p<.01) and negative emotion were explicitly reported to the examiner during recording (p=.015), regardless of the ictal level of consciousness. Among the patients recorded with SEEG, we found a significant involvement of amygdala within the seizure onset zone (p<.01) for patients with AAS. Higher risk of developing AAS is associated with seizures expressing negative emotional symptoms, independently of ictal consciousness level. Persistent interictal fear of seizures might be viewed as the consequence of a reinforcement of the emotional networks secondary to amygdala involvement in seizures of temporal origin. Physicians should screen for AAS not only to assess the impact of epilepsy on daily life, but also as an interictal biomarker of ictal semiology and emotional network involvement at seizure onset.

Ventral hippocampal shock encoding modulates the expression of trace cued fear

The hippocampus is crucial for associative fear learning when the anticipation of threat requires temporal or contextual binding of predictive stimuli as in trace and contextual fear conditioning. Compared with the dorsal hippocampus, far less is known about the contribution of the ventral hippocampus to fear learning. The ventral hippocampus, which is highly interconnected with defensive and emotional networks, has a prominent role in both innate and learned affective behaviors including anxiety, fear, and reward. Lesions or temporary inactivation of the ventral hippocampus impair both cued and contextual fear learning, but whether the ventral hippocampal role in learning is driven by affective processing, associative encoding, or both is not clear. Here, we used trace fear conditioning in mixed sex cohorts to assess the contribution of shock-encoding to the acquisition of cued and contextual fear memories. Trace conditioning requires subjects to associate an auditory conditional stimulus (CS) with a shock unconditional stimulus (UCS) that are separated in time by a 20-s trace interval. We first recorded neuronal activity in the ventral hippocampus during trace fear conditioning and found that ventral CA1 predominantly encoded the shock reinforcer. Potentiated firing to the CS was evident at testing, but no encoding of the trace interval was observed. We then tested the necessity of shock encoding for conditional fear acquisition by optogenetically silencing ventral hippocampal activity during the UCS on each trial of training. Contrary to our predictions, preventing hippocampal shock-evoked firing did not impair associative fear. Instead, it led to a more prolonged expression of CS freezing across test trials, an effect observed in males, but not females. Contextual fear learning was largely intact, although a subset of animals in each sex were differentially affected by shock-silencing. Taken together, the results show that shock encoding in the ventral hippocampus modulates the expression of learned fear in a sex-specific manner.

Social Media and Social Support: A Framework for Patient Satisfaction in Healthcare

Social media has been a powerful source of social support for health consumers. In the healthcare sector, social media has thrived, building on various dynamic platforms supporting the connection between social relationships, health, and wellbeing. While prior research has shown that social support exerts a positive impact on health outcomes, there is scant literature examining the implications of social support for patient satisfaction, which suggests that there is a profound gap in the extant literature. The objective of this study is to develop and test a theoretical model for understanding the relationship between different dimensions of social support and patient empowerment. The study further investigates the debated relationship between patient empowerment and patient satisfaction. The measurement model indicated an acceptable fit (χ2 = 260.226; df, 107, χ2/df = 2.432, RMSEA = 0.07, GFI = 0.90, IFI = 0.95, TLI = 0.94, and CFI = 0.95). Findings indicate that emotional support (p &lt; 0.001), information support (p &lt; 0.05), and network support (p &lt; 0.001) positively influence the notion of patient empowerment. In turn, patient empowerment positively influences patient satisfaction (p &lt; 0.001). The proposed framework contributes to the health communication literature by introducing a novel framework for patient satisfaction in the social media context, which provides important inputs for healthcare service providers in developing patient empowerment strategies.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes.

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users’ different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users’ interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users’ informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

Mental health threats to children and adolescents during the COVID-19 pandemic according to teachers from the Podkarpackie Voivodeshi

The article is an attempt to systematise the observations of teachers about mental health threats to children and adolescents during the COVID-19 pandemic. The situation of the pandemic can be considered a trigger factor pointing to the problems of the Polish education system like no other – excessive didacticism, insufficient media education, lack of specialists, especially school psychologists, insufficient emotional support networks and marginal treatment of the importance of the teacher-pupil relationship in the education process. The lack of competences of many adults, especially teachers, in the field of creating networks and providing emotional and social support makes the deficits of many pupils in the field of mental, emotional and social development visible. The practical implications in the field of psychological and pedagogical support that are suggested in the summary may turn out to be particularly valuable. The beneficiaries of this support should be not only pupils, but also teachers.

Antidepressant Treatment-Induced State-Dependent Reconfiguration of Emotion Regulation Networks in Major Depressive Disorder.

Deficits in emotion regulation are the main clinical features, common risk factors, and treatment-related targets for major depressive disorder (MDD). The neural bases of emotion regulation are moving beyond specific functions and emphasizing instead the integrative functions of spatially distributed brain areas that work together as large-scale brain networks, but it is still unclear whether the dynamic interactions among these emotion networks would be the target of clinical intervention for MDD. Data were collected from 70 MDD patients and 43 sex- and age-matched healthy controls. The dynamic functional connectivity (dFC) between emotion regions was estimated via a sliding-window method based on resting-state functional magnetic resonance imaging (R-fMRI). A k-means clustering method was applied to classify all time windows across all participants into several dFC states reflecting recurring functional interaction patterns among emotion regions over time. The results showed that four dFC states were identified in the emotion networks. Their alterations of state-related occurrence proportion were found in MDD and subsequently normalized following 12-week antidepressant treatment. Baseline strong dFC could predict the reduction rate of Hamilton Depression Rating Scale (HAMD) scores. These findings highlighted the state-dependent reconfiguration of emotion regulation networks in MDD patients owing to antidepressant treatment.

Efficacy of acupuncture and its influence on the emotional network in adult insomnia patients: protocol for a randomized controlled clinical trial

IntroductionInsomnia disorder (ID) is characterized by dissatisfaction with the quantity or quality of sleep and is often accompanied by negative emotions such as anxiety and depression. Patients with insomnia become trapped in a vicious circle of bad moods and poor sleep. Resting-state functional magnetic resonance imaging (r-fMRI) studies have shown abnormalities in emotion-related brain networks in patients with ID. And it has been proven that reducing negative emotions improves sleep quality. As a traditional alternative therapy, acupuncture has been demonstrated to be effective not only in improving sleep quality but also in stabilizing emotions; however, the mode of action needs to be further explored. Therefore, a clinical trial was designed to explore the effect of acupuncture in improving sleep and mood and to intuitively investigate the regulation of the emotional network using fMRI.Methods and analysisA total of 60 participants with ID will be randomly allocated to a spirit-regulating group or a control group using non-effective acupoints acupuncture at a ratio of 1:1. All participants will receive 3 acupuncture treatment sessions per week for 4 weeks. In addition, 30 healthy individuals will be included in the healthy group. The primary outcome is the Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes are the Hamilton Anxiety Scale (HAMA), the Hamilton Depression Scale (HAMD), the Hyperarousal Scale (HAS), and the Fatigue Scale-14 (FS-14), r-fMRI data, sleep diary, and actigraphy. The data will be collected prior to treatment, following treatment, and during the 12-week follow-up period; a sleep diary will be kept during the entire process.Ethics and disseminationThis protocol has been approved by the Research Ethical Committee of Beijing Hospital of Traditional Chinese Medicine (Bejing TCM Hospital). The results will be published in peer-reviewed journals or presented at academic conferences.Trial registrationChinese Clinical Trials Register ChiCTR1800015282.Protocol version: Version 1.0. Date: Dec.2020

Depicting the Emotion Flow: Super-Spreaders of Emotional Messages on Weibo During the COVID-19 Pandemic

This study collected 2 million posts and reposts regarding the early stage of COVID-19 in China on Weibo from 26 December 2019 to 29 February 2020. Emotion analysis and social network analysis were used to examine the flow of emotional messages (emotion flow) by comparing them with the flow of general messages (information flow). Results indicated that both emotional messages and general messages present a multilayer diffusion pattern and follow network step flow models. In our dataset, emotion network has a higher transmission efficiency than information network; officially verified accounts were more likely to become super-spreaders of emotional messages; good emotions were predominant but isolated from other six emotions (joy, sadness, fear, disgust, surprise, anger) in online discussions; finally, government played a vital role in spreading good emotions.

Image Super Resolution using Enhanced Super Resolution Generative Adversarial Network

Aside from enhancing the accuracy and speed of single picture modification utilizing fast and in-depth convolutional emotional networks, one significant challenge remains mostly commonly unaddressed, namely how do we recover soft texture details when we concentrate too much on exceptional improvement features? The resultant evaluations offer greater transmission ratings, but the high frequency data is non-existent and unsatisfactory mostly in sense that now it fails to meet the consistency anticipated in high resolution. The resulting ratings have higher signal-to-audio ratings, but the high frequency data is non-existent and unsatisfactory in the sense that it fails to match the consistency expected in high resolution. Introducing ESRGAN, an Advanced Optical Genetically Modified (GAN) network of high-resolution image (SR). To our knowledge, it is a framework capable of identifying immature real-world images up to 4x points rising. To achieve this, we propose a function of loss of vision that combines the loss of content with the loss of content (Mean Squared Error Loss). Controversial Loss has our solution for many uncooked pictures utilizing a discriminatory network which is taught to distinguish between high resolution images and realistic images. We have built a structure that contains several RRDB blocks (Residual in Residual Dense Block) outside the Batch Normalization layers. Our deep residual network can find realistic image texture in very low sample images. Additionally, we used techniques that included residual measurement and a small implementation to train a deeper model. We also introduced the relativistic GAN as a racist, who learns to judge whether one image is more realistic than another, directing the generator to return a detailed texture. In addition, we have improved vision loss by using features before activation, which provides greater security and thus restoring more precise light and texture.

Progressive increase of high-frequency EEG oscillations during meditation is associated with its trait effects on heart rate and proteomics: a study on the Tibetan Buddhist.

Meditation has been a spiritual and healing practice in the East for thousands of years. However, the neurophysiologic mechanisms underlying its traditional form remain unclear. In this study, we recruited a large sample of monks (n = 73) who practice Tibetan Buddhist meditation and compared with meditation-naive local controls (n = 30). Their electroencephalography (EEG) and electrocardiogram signals were simultaneously recorded and blood samples were collected to investigate the integrative effects of Tibetan Buddhist on brain, heart, and proteomics. We found that the EEG activities in monks shifted to a higher frequency from resting to meditation. Meditation starts with decrease of the (pre)frontal delta activity and increase of the (pre)frontal high beta and gamma activity; while at the deep meditative state, the posterior high-frequency activity was also increased, and could be specified as a biomarker for the deep meditation. The state increase of posterior high-frequency EEG activity was significantly correlated with the trait effects on heart rate and nueropilin-1 in monks, with the source of brain-heart correlation mainly locating in the attention and emotion networks. Our study revealed that the effects of Tibetan Buddhist meditation on brain, heart, and proteomics were highly correlated, demonstrating meditation as an integrative body-mind training.

음식 서사와 문화 정서: 허수경과 마르셀 프루스트 비교

As a sign, each national food closely connects the collective unconscious to unique historical experiences and social norms and thus contributes to the configuration of a distinctive cultural identity. In this respect, this paper compares Marcel Proust’s narrative of the madeleine in In Search of Lost Time and Sukyeong Huh’s narrative of Gukbap in Global Blues and sheds light on how each national food unfolds a specific network of emotions and feelings and connotes each individual style and aesthetic sensibility. Considering that the two writers belong to different symbolic systems in the East and the West, it would be very interesting to examine how differently each writer reveals a unique socio-psychological sentiment in the attempt to restore a sense of ‘being oneself’ in the face of the loss of social presence.

Mental Health Effects of COVID-19 in Older Adults Are Moderated by Existing Mental Health Needs and Emotional Support

Mitigating mental health consequences is one of the priorities for the society to advance, and the aim of this study is to investigate the mental health effects of COVID-19 in older adults and to explore risk and protective factors. Social workers recruited 722 older adults living in the community (mean age 73.6±8.4) from January 2020 to February 2021 in Hong Kong, and interviewed them for basic demographics (age, gender, education, and living status), mental health service use in recent three months (proxy for existing needs), number of people to talk to when feeling down (proxy for emotional support network), and assessed their depression using Patient Health Questionnaire-9 (PHQ-9). Impacts of COVID-19 were indicated by local daily effective reproductive number (Rt) and Nth wave since the start of the pandemic. Generalized linear models (GLM) were applied. Basic demographics were not associated with depression, existing mental health needs (β=2.99, p<.001), Rt (β=1.08, p<.05) and Nth wave (β=0.49, p<.05) were positively associated with depression, while emotional support network was negatively associated with depression (β=-2.47, p<.001). There was also a significant interaction between Rt and Nth wave on depression (β=0.69, p<.05), suggesting ongoing COVID-19 took a toll on older adults’ mental health. Three-way interactions between COVID-19 Rt, Nth wave and existing mental health needs (β=0.25, p<.05) and emotional support network (β=-0.12, p=.07) on depression further indicated that older adults with existing mental health needs warrant more attention, and wider emotional support network may buffer the impact of the pandemic on mental health.

Calcium imaging reveals depressive- and manic-phase-specific brain neural activity patterns in a murine model of bipolar disorder: a pilot study

Brain pathological features during manic/hypomanic and depressive episodes in the same patients with bipolar disorder (BPD) have not been described precisely. The study aimed to investigate depressive and manic-phase-specific brain neural activity patterns of BPD in the same murine model to provide information guiding investigation of the mechanism of phase switching and tailored prevention and treatment for patients with BPD. In vivo two-photon imaging was used to observe brain activity alterations in the depressive and manic phases in the same murine model of BPD. Two-photon imaging showed significantly reduced Ca2+ activity in temporal cortex pyramidal neurons in the depression phase in mice exposed to chronic unpredictable mild stress (CUMS), but not in the manic phase in mice exposed to CUMS and ketamine. Total integrated calcium values correlated significantly with immobility times. Brain Ca2+ hypoactivity was observed in the depression and manic phases in the same mice exposed to CUMS and ketamine relative to naïve controls. The novel object recognition preference ratio correlated negatively with the immobility time in the depression phase and the total distance traveled in the manic phase. With recognition of its limitations, this study revealed brain neural activity impairment indicating that intrinsic emotional network disturbance is a mechanism of BPD and that brain neural activity is associated with cognitive impairment in the depressive and manic phases of this disorder. These findings are consistent with those from macro-imaging studies of patients with BPD. The observed correlation of brain neural activity with the severity of depressive, but not manic, symptoms need to be investigated further.

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.