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Infancy is a sensitive period of development, during which experiences of parental care are particularly important for shaping the developing brain. In a longitudinal study of N = 95 mothers and infants, we examined links between caregiving behavior (maternal sensitivity observed during a mother–infant free‐play) and infants' neural response to emotion (happy, angry, and fearful faces) at 5 and 7 months of age. Neural activity was assessed using functional Near‐Infrared Spectroscopy (fNIRS) in the dorsolateral prefrontal cortex (dlPFC), a region involved in cognitive control and emotion regulation. Maternal sensitivity was positively correlated with infants' neural responses to happy faces in the bilateral dlPFC and was associated with relative increases in such responses from 5 to 7 months. Multilevel analyses revealed caregiving‐related individual differences in infants' neural responses to happy compared to fearful faces in the bilateral dlPFC, as well as other brain regions. We suggest that variability in dlPFC responses to emotion in the developing brain may be one correlate of early experiences of caregiving, with implications for social‐emotional functioning and self‐regulation. Research Highlights: Infancy is a sensitive period of brain development, during which experiences with caregivers are especially important.This study examined links between sensitive maternal care and infants' neural responses to emotion at 5–7 months of age, using functional near‐infrared spectroscopy (fNIRS).Experiences of sensitive care were associated with infants' neural responses to emotion—particularly happy faces—in the dorsolateral prefrontal cortex. [ABSTRACT FROM AUTHOR]

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro‐networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty‐two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole‐brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro‐networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies. [ABSTRACT FROM AUTHOR]

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time. [ABSTRACT FROM AUTHOR]

Affective Touch is characterized by both emotional and arousing dimensions that rely on specific features of a gentle human caress. In this study, we investigated whether and how both the nature of the touching effector (Human hand vs. Artificial hand) and touch type (Dynamic vs. Static) influenced the participants' pupil dilation and their subjective experience during tactile stimulation. We observed that when participants received a dynamic touch, their pupil dilation increased more when the touch was produced by a human compared to an artificial hand. This discrimination was not present for static touch. Also, dynamic touch given by a human hand invoked a supralinear enhancement of pupil dilation indicating that the combination of these two features induced a stronger autonomic activation than the summed effects of each separately. Moreover, this specific type of touch was perceived as the most pleasant compared to all other tactile stimulations. Overall, our results suggest that pupil dilation could reflect the pleasant experience of human-to-human tactile interactions, supporting the notion that the autonomic nervous system is responsive to the emotional and hedonic aspects associated with Affective Touch as a part of a complex and holistic social experience, rather than solely reacting to its low-level sensory properties. [ABSTRACT FROM AUTHOR]

Brain-Computer Interface (BCI)-based affect recognition in humans utilizing physiological signals is typically guided by a supervised method that relies heavily on the presence of appropriate affect representations. In this paper, we assess the effectiveness of contemporary affect representation methodologies for establishing ground truth for reliable affective BCI systems. On the DEAP dataset, two experiments were conducted to compare the observed emotion clusters based on subjective ratings with the clustering of corresponding physiological responses based on EEG characteristics. The outcomes of the two investigations are inconsistent with one another. The findings suggest the subjective use of emotion categories as opposed to their general application in establishing ground truth for supervised approaches and must encourage researchers to examine affect representation techniques critically before considering them as concrete representations of a person's affective state. [ABSTRACT FROM AUTHOR]

Introduction: Although major depression is the disorder most frequently associated with suicidal behavior, it is unclear that major depressive disorder patients may develop suicidal ideation. Basic affective system theory may provide a novel and beneficial viewpoint in this field. The goal of this study was to investigate the basic affective system in relation to suicidal ideation in individuals with depression. Method: The study population comprised 160 participants who had been formally diagnosed with major depressive disorder. Participants were divided into two groups according to whether they had suicide ideation (depression with suicide [DS]) (N = 93) or not (depression with no suicidal ideation [DNS]) (N = 67). The Beck Depression Inventory (BDI), the Suicide Probability Scale (SPS), the Beck Hopelessness Scale (BHS), and the Affective Neuroscience Personality Scale (ANPS) were applied. Statistical Product and Service Solutions (SPSS) 24 and the SPSS macroprocess, which were specifically developed for assessing complex models including serial mediators, were used to analyze the data. Results: The mean age of all participants was 31.1 ± 11 years, and most of them were female (65%). The DS group had a higher family history of psychiatric medication use and suicidal attempts. In addition, BDI, BHS, and SPS total scores were higher, as well as ANGER subscale scores were higher in the DS group. In mediation analysis, the ANGER subscale significantly predicted the presence of suicide ideation. We observed a direct effect of the ANGER subscale score on suicide ideation as well as an indirect effect of the ANGER subscale score on suicidal ideation via depression severity. Conclusion: Higher scores on ANGER are associated with suicidal ideation. Neurobiological correlates, including the ANGER system, may be promising in understanding suicidal behaviors. [ABSTRACT FROM AUTHOR]

Purpose: Research investigating depressive symptoms among cancer patients rarely distinguish between core symptoms of depression (motivational and consummatory anhedonia, and negative affect). This distinction is important as these symptoms may show different trajectories during the course of the illness and require different treatment approaches. The aim of the present study is to investigate fluctuations in core depressive symptoms in patients with colorectal cancer (CRC). It is hypothesized that these core depressive symptoms fluctuate differently during the course of the illness and depend on the phase of the illness (diagnostic, treatment, recovery and palliative phase). Method: This study is based on data from the PROCORE study. PROCORE is a prospective, population-based study aimed to examine the longitudinal impact of CRC and its treatment on patient-reported outcomes. Eligible patients completed self-report questionnaires (i.e. Multifactorial Fatigue Index, Hospital Anxiety and Depression scale, EORTC-C30) after diagnosis, after surgery and at one and two years after diagnosis. Results: In total, 539 patients participated of whom 68 have died until March 1ste 2021. Core depressive symptoms fluctuated differently during the course of the illness with higher levels of motivational anhedonia during treatment and palliative phase (P<.001), consummatory anhedonia at the palliative phase (p <.001) and negative affect at the diagnostic and palliative phase (P<.001). Conclusion: It is important to distinguish between different core depressive symptoms as they fluctuate differently during the course of an illness like CRC. The various depressive symptoms may require a different treatment approach at specific moments during the illness process. [ABSTRACT FROM AUTHOR]

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses. [ABSTRACT FROM AUTHOR]

This paper advocates for considering disgust as a primary emotional system within Panksepp's Affective Neuroscience framework, which has the potential to improve the efficacy of psychotherapy with obsessive-compulsive disorder, hypochondriasis, and emetophobia. In 2007, Toronchuk and Ellis provided comprehensive evidence that DISGUST system, as they defined it, matched all Panksepp's criteria for a primary emotional system. A debate ensued and was not unambiguously resolved. This paper is an attempt to resume this discussion and supplement it with the data that accumulated since then on DISGUST's relationship with the immune system and the role of DISGUST dysregulation in psychopathology. We hope that renewed research interest in DISGUST has the potential to improve clinical efficacy with hard-to-treat conditions. [ABSTRACT FROM AUTHOR]

The law assumes that healthy adults are generally responsible for their actions and have the ability to control their behavior based on rational and moral principles. This contrasts with some recent neuroscientific accounts of action control. Nevertheless, both law and neuroscience acknowledge that strong emotions including fear and anger may "trigger" loss of normal voluntary control over action. Thus, "Loss of Control" is a partial defense for murder under English law, paralleling similar defenses in other legal systems. Here we consider the neuroscientific evidence for such legal classifications of responsibility, particularly focussing on how emotional states modulate voluntary motor control and sense of agency. First, we investigate whether neuroscience could contribute an evidence-base for law in this area. Second, we consider the societal impact of some areas where legal thinking regarding responsibility for action diverges from neuroscientific evidence: should we be guided by normative legal traditions, or by modern understanding of brain functions? In addressing these objectives, we propose a translation exercise between neuroscientific and legal terms, which may assist future interdisciplinary research. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a severe neuropsychiatric disorder characterized by cognitive, affective, and social dysfunction, resulting in hallucinations, delusions, emotional blunting, and disordered thinking. In recent years, proteomics has been increasingly influential in SCZ research. Glycosylation, a key post-translational modification, can alter neuronal stability and normal signaling in the nervous system by affecting protein folding, stability, and cellular signaling. Recent research evidence suggests that abnormal glycosylation patterns exist in different brain regions in autopsy samples from SCZ patients, and that there are significant differences in various glycosylation modification types and glycosylation modifying enzymes. Therefore, this review explores the mechanisms of aberrant modifications of N-glycosylation, O-glycosylation, glycosyltransferases, and polysialic acid in the brains of SCZ patients, emphasizing their roles in neurotransmitter receptor function, synaptic plasticity, and neural adhesion. Additionally, the effects of antipsychotic drugs on glycosylation processes and the potential for glycosylation-targeted therapies are discussed. By integrating these findings, this review aims to provide a comprehensive perspective to further understand the role of aberrant glycosylation modifications in the pathophysiology of SCZ. [ABSTRACT FROM AUTHOR]

Cinema, a modern titan of entertainment, holds power to move people with the artful manipulation of auditory and visual stimuli. Despite this, the mechanisms behind how sensory stimuli elicit emotional responses are unknown. Thus, this study evaluated which brain regions were involved when sensory stimuli evoke auditory- or visual-driven emotions during film viewing. Using functional magnetic resonance imaging (fMRI) decoding techniques, we found that brain activities in the auditory area and insula represent the stimuli that evoke emotional response. The observation of brain activities in these regions could provide further insights to these mechanisms for the improvement of film-making, as well as the development of novel neural techniques in neuroscience. In near feature, such a "neuro-designed" products/ applications might gain in popularity. [ABSTRACT FROM AUTHOR]

This article discusses the relationship between technology, learning, and creativity, specifically focusing on Generative AI (GenAI). The author interviews Dr. Anna Abraham, a neuroscientist and psychologist, who proposes a redefinition of creativity that considers both the creator's perspective and the audience's perspective. Dr. Abraham emphasizes the importance of interdisciplinary collaboration and listening to practitioners to gain a holistic understanding of creativity. She also highlights the role of struggle and frustration in the creative process, which is often overlooked in formal education. While AI tools can aid creativity, they should not replace the personal growth that comes from the creative journey. Dr. Abraham cautions against attributing human-like intentionality to AI systems and suggests that teachers play a critical role in using AI intentionally and preparing students to interact with AI meaningfully. The advancement of AI should be accompanied by policies that address access disparities and ensure consistent standards across schools. Overall, Dr. Abraham's insights emphasize the need to maintain the human elements of creativity while utilizing the benefits of AI in education. [Extracted from the article]

Introduction: This study aims to explore the relationship between affective personality traits and opioid use disorder (OUD), including factors such as motivation to quit, addiction severity, and age of onset of drug use. Methods: This study included 141 patients with opioid addiction (OAP) and 160 age‐ and sex‐matched healthy controls (HC). OAP were interviewed and diagnosed according to DSM‐5 criteria. HC were screened for past or current drug use. Participants completed sociodemographic forms and the Affective Neuroscience Personality Scale (ANPS), and the OAP group also completed the Addiction Profile Index (API). Results: SEEK, PLAY, and SADNESS were identified as different affective personality traits between OAP and HC groups. Addiction severity was positively correlated with SADNESS and ANGER, while the age of onset of drug use was correlated with ANGER. Risk factors for OA include family history of substance abuse, low education, and low PLAY scores, whereas risk factors for earlier substance use onset are childhood trauma and high ANGER scores. Conclusions: This study highlights the importance of understanding affective personality traits in OUD. These findings may deepen our understanding of the underlying mechanisms of OUD. The identification of these affective systems may have implications for the development of personalized prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

All contemporary psychotherapies agree that (failing) emotion regulation is central to psychological disorders and that psychotherapy is about improving emotion regulation. In his research on the "emotion‐laden" complex Jung put an emphasis on the role of failing emotion regulation in contributing to psychological disorders as well as to change in the process of psychotherapy, but he left this field of research and took a very different direction in favour of his archetype concept. Psychodynamic approaches generally argue that changes in emotion regulation are accomplished through corrective emotional experiences in the therapeutic relationship. Insights from affective neurosciences and attachment research have had a major influence on how the therapeutic relationship is constructed in contemporary psychodynamic approaches. There is a lack of similar developments in analytical psychology, which leads to substantial differences between the models of Jungian psychotherapy in contrast to other contemporary psychodynamic approaches. The implications of these differences for the practice of psychotherapy and especially the role of the therapeutic relationship are pointed out. [ABSTRACT FROM AUTHOR]

Music listening or playing can create a feeling of connection with other listeners or performers, with distinctive levels of immersion and absorption. A major question, in this regard, is whether the music does have an ontological status, as an end in itself, or whether it is only a tool for the mediation of something else. In this paper we endorse a mediating perspective, with a focus on the music's potential to increase affiliative bonding between listeners, performers and even the music. Music, then, is hypostasized as "something that touches us" and can be considered a partner of affiliative exchange. It has the potential to move us and to modulate the way we experience the space around us. We therefore elaborate on the tactile dimension of being moved, as well as on the distinction between personal, peripersonal, and extrapersonal space, with a corresponding distinction between first-person, second-person, and third-person perspectives on musical engagement. [ABSTRACT FROM AUTHOR]

Learning can generally be categorised into three domains, which include cognitive (thinking), affective (emotions or feeling) and psychomotor (physical or kinesthetic). In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners' affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students' affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students' frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students' affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors' performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers. [ABSTRACT FROM AUTHOR]

Recent years have seen an increased interest regarding theoretical and empirical associations of adult attachment security and primary affective traits concerning psychiatric disorders. In this study, network analysis technique is applied to dissect the links between both psychodynamic personality constructs and an array of psychopathological symptoms. A total sample of 921 (69.9 % female) participants from the general population was investigated. A regularized cross-sectional partial correlation network between attachment (Experiences in Close Relationships-Revised [ECR-RD8]), primary affective traits (Brief Affective Neuroscience Personality Scales [BANPS-GL]) and psychopathological symptoms (ICD-10-Symptom-Rating Questionnaire [ISR]) was estimated via the EBICglasso algorithm. Node centrality, predictability and bridge centrality were analyzed. To evaluate the stability of the network and evaluate the significance of differences, we employed bootstrap techniques. The network was found to be stable, allowing reliable interpretations. We observed SADNESS, as well as depressive, PTSD and anxiety symptoms as the most influential nodes within the investigated network. Attachment AV and SADNESS were observed as nodes with the highest bridge centrality. The results provide a data-driven in-depth look into the complex dynamics between psychopathological symptoms, attachment security and basic affective traits. Results underscore the critical interconnections between affect, attachment, and psychopathology, advocating for a psychodynamically informed systems approach in psychological research that considers the affective dimensions underlying human mental health. • Network analysis reveals links between psychodynamic and psychiatric parameters. • Attachment styles are confirmed as important in the description of mental illness. • Primary emotions predict attachment deficits in healthy young adults. [ABSTRACT FROM AUTHOR]

Understanding the biological processes that underlie individual differences in emotion regulation and stress responsivity is a key challenge for translational neuroscience. The gene FKBP5 is a core regulator in molecular stress signaling that is implicated in the development of psychiatric disorders. However, it remains unclear how FKBP5 DNA methylation in peripheral blood is related to individual differences in measures of neural structure and function and their relevance to daily-life stress responsivity. Here, we characterized multimodal correlates of FKBP5 DNA methylation by combining epigenetic data with neuroimaging and ambulatory assessment in a sample of 395 healthy individuals. First, we showed that FKBP5 demethylation as a psychiatric risk factor was related to an anxiety-associated reduction of gray matter volume in the ventromedial prefrontal cortex, a brain area that is involved in emotion regulation and mental health risk and resilience. This effect of epigenetic upregulation of FKBP5 on neuronal structure is more pronounced where FKBP5 is epigenetically downregulated at baseline. Leveraging 208 functional magnetic resonance imaging scans during a well-established emotion-processing task, we found that FKBP5 DNA methylation in peripheral blood was associated with functional differences in prefrontal-limbic circuits that modulate affective responsivity to daily stressors, which we measured using ecological momentary assessment in daily life. Overall, we demonstrated how FKBP5 contributes to interindividual differences in neural and real-life affect regulation via structural and functional changes in prefrontal-limbic brain circuits. [ABSTRACT FROM AUTHOR]

Tommaso Costa,1,2 Mario Ferraro,1– 3 Jordi Manuello,1,2 Alessia Camasio,2,3 Andrea Nani,2 Lorenzo Mancuso,2 Franco Cauda,1,2 Peter T Fox,4,5 Donato Liloia1,2 1GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; 2FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy; 3Department of Physics, University of Turin, Turin, Italy; 4Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; 5Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USACorrespondence: Jordi Manuello, FOCUS Laboratory, Department of Psychology, University of Turin, Via Giuseppe Verdi 10, Turin, 10124, Italy, Email jordi.manuello@unito.itAbstract: Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.Keywords: affective mapping, BrainMap, quantitative synthesis, coordinate-based meta-analysis, fMRI, affective neuroscience

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has increased germ aversion, an aversive affective response to a high likelihood of pathogen transmission. While psychological factors are associated with chronic pain, the relationship between germ aversion and chronic pain remains unexplored. This study aimed to examine the relationship between germ aversion and new-onset and prognosis of chronic pain using longitudinal data collected during the COVID-19 pandemic. We conducted web-based surveys of full-time workers at baseline and after three months. Data were collected on demographic characteristics, psychological factors, and chronic pain. Germ aversion was assessed using a modified Perceived Vulnerability to Disease scale. We analyzed responses from 1265 panelists who completed the survey twice. The prevalence of chronic low back pain (CLBP) and chronic neck and shoulder pain (CNSP) was associated with sex, short sleep duration, psychological distress, loneliness, and germ aversion. Stratified analyses showed that germ aversion was a risk factor for CLBP at three months in both individuals with and without CLBP at baseline, and for CNSP at three months in those with CNSP at baseline, even after adjustment for confounders. In conclusion, this preliminary study suggests that high germ aversion is a risk factor for CLBP and CNSP in young and middle-aged workers. [ABSTRACT FROM AUTHOR]

Even before the advent of fMRI, the amygdala occupied a central space in the affective neurosciences. Yet this amygdala-centred view on emotion processing gained even wider acceptance after the inception of fMRI in the early 1990s, a landmark that triggered a goldrush of fMRI studies targeting the amygdala in vivo. Initially, this amygdala fMRI research was mostly confined to task-activation studies measuring the magnitude of the amygdala's response to emotional stimuli. Later, interest began to shift more towards the study of the amygdala's resting-state functional connectivity and task-based psychophysiological interactions. Later still, the test-retest reliability of amygdala fMRI came under closer scrutiny, while at the same time, amygdala-based real-time fMRI neurofeedback gained widespread popularity. Each of these major subdomains of amygdala fMRI research has left its marks on the field of affective neuroscience at large. The purpose of this review is to provide a critical assessment of this literature. By integrating the insights garnered by these research branches, we aim to answer the question: What part (if any) can amygdala fMRI still play within the current landscape of affective neuroscience? Our findings show that serious questions can be raised with regard to both the reliability and validity of amygdala fMRI. These conclusions force us to cast doubt on the continued viability of amygdala fMRI as a core pilar of the affective neurosciences. [ABSTRACT FROM AUTHOR]

Regulatory efforts are hypothesized to affect associations between emotions and physiology (i.e., concordance) to facilitate adaptive functioning. Assessing the role of coping on physiological–emotional concordance during ecologically relevant scenarios can elucidate whether concordance can serve as a biomarker of risk or resilience. The present study assessed self‐reported coping as a moderator of minute‐to‐minute associations between autonomic nervous system activity and emotions (i.e., physiological–emotional concordance) in caregivers (N = 97) and adolescents (N = 97; ages 10–15) during a dyadic conflict task. Models included physiological variables (sympathetic, skin conductance level [SCL]; and parasympathetic, respiratory sinus arrhythmia [RSA]) and their interaction (SCL × RSA) as predictors of emotions, with coping variables as moderators. Caregivers' use of primary control coping (e.g., problem solving and emotional expression) and secondary control coping (e.g., cognitive reappraisal and acceptance) use in response to family stress predicted more positive emotional experiences during the laboratory conflict task. Adolescents' use of secondary control coping moderated the SCL–emotion association, such that increases in momentary SCL were associated with more positive emotion ratings for youth reporting higher secondary control coping. For youth who report more adaptive trait‐level coping skills, momentary changes in SCL may reflect active engagement and attentiveness to facilitate more positive emotional experiences. Findings advance our understanding of the interrelationships between physiological responses and psychological experiences during relevant, interactive scenarios. Autonomic responses are differentially related to affective states depending on the coping strategies that adolescents employ, suggesting that concordance may be associated with intervention targets (i.e., coping skills). [ABSTRACT FROM AUTHOR]

Recent psychology and neuroscience studies have used tactile stimuli in patients, concluding after their experiments that touch is a sense tightly linked to emotions. In parallel, a new way of seeing films, 4D cinema, has added new stimuli to the traditional audiovisual via, including the tactile vibration. In this work, we have studied the brain activity of audience while viewing a scene filmed and directed by us and with an emotional content, under two different conditions: 1) image + sound, 2) image + sound + vibro-tactile stimulation. We have designed a glove where pulse trains are generated in coin motors at specific moments and recorded 35 viewers' electroencephalograms (EEGs) to evaluate the impact of the vibro-tactile stimulation during the film projection. Hotelling's T-squared results show higher brain intensity if the tactile stimulus is received during the viewing than if no tactile stimulus is injected. Condition 1 participants showed activation in left and right orbitofrontal areas, whereas Condition 2 they also showed activities in right superior frontal and right-medial frontal areas. We conclude that the addition of vibrotactile stimulus increases the brain activity in areas linked with attentional processes, while producing a higher intensity in those related to emotional processes. [ABSTRACT FROM AUTHOR]

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research. [ABSTRACT FROM AUTHOR]

Charles Bell was famous for the discovery of the separate motor and sensory roots of the spinal and facial nerves, although in recent years his right to priority has been challenged by historians and scientists. But Charles Bell did discover something even if has not been accorded the status of a scientific fact. Between 1821 and 1823 he unveiled the 'respiratory nervous system', a distinct system of nerves that acted as the 'organ of the passions', which he then elaborated upon in his 1824 Essays on the Anatomy and Philosophy of Expression. As Bell and his allies attempted to claim priority in the spinal and facial nerves, the respiratory nerves were pushed to the background, subordinated to the motor and sensory nerves. This essay, therefore, redefines Charles Bell's major discovery as the 'respiratory nerves', providing a detailed description of their anatomy and physiology and the way in which they underwrote Bell's theory of the emotions. It also demonstrates how his aesthetics were intertwined with his research programme. It then connects the respiratory nerves to Thomas Dixon's assertion that Bell was one of the founders of the modern psychological category of the emotions, providing a deeper and more nuanced genealogy of the emotions, including the impact that Bell had upon William James's seminal article 'What is an Emotion?' [ABSTRACT FROM AUTHOR]

The questionnaire is a widely used instrument in social research for collecting information. One of the most important design decisions is the choice of one (or more) measurement scale(s). A common scale for rating/opinion questions is the Likert-type scale ranging from 0 or 1 (lowest rating/strongest disagreement) to 10 (highest rating/strongest agreement). Its success is based, among other things, on the advantage of being able to treat the scale from different perspectives (nominal, ordinal, or interval). Making the right choice is not an easy task for the researcher, as the perspective selected determines the methodology to be used for analysis and the potential results. This paper proposes a methodological strategy that facilitates this phase by presenting a protocol that allows the simultaneous analysis of two versions of the scale, nominal and interval. The proposed idea is based on the tabulation of the two scales in a mixed data matrix and it is advised to conduct an exploratory analysis through Multiple Factor Analysis. This technique maintains and compares the idiosyncrasies of each scale. The versatility and suitability of the proposal is illustrated by real research on social and affective relationships during the COVID-19 pandemic in Spain. [ABSTRACT FROM AUTHOR]

Pain experiences are often complex with catastrophic cognitions, emotions, and behaviors. Cognitive behavioral therapists share the work of unraveling these complex experiences with their patients. However, the change process underlying the unraveling of the pain experience have not yet been quantified. We used an interrelationship-focused network model to examine the way an undifferentiated conceptualization between cognition and pain experience changed via group cognitive-behavioral therapy (CBT). Overall, 65 participants (77.4% of all patients who entered the intervention) were included in the analysis; they attended the total of 12 weekly group CBT and filled the Short-Form McGill Pain Questionnaire and the pain catastrophizing questionnaire. Before treatment, there were no edges in the partial correlation-based network because of large covariation across items. After treatment, many edges appeared and, particularly strong couplings were found between items within the same subscale. The formative shift from a non-edged pre-treatment network to a mature post-treatment network may indicate that patients were able to conceptualize these symbolic constructs better. These results are probably of interest to clinicians and would be consistent with the fundamental monitoring process of CBT. [ABSTRACT FROM AUTHOR]

The opioid crisis emerged in part due to the overprescribing of opioid analgesics for chronic pain. Although not the only source of the current epidemic of opioid use disorder (OUD), the prescription of opioids for chronic pain remains one vector for the development of opioid misuse and OUD. However, opioid tapering is not appropriate for all patients, and some patients may need to remain on opioid therapy for the long term. To reduce the risk of opioid‐related harm among people with chronic pain and to treat incipient or entrenched addictive behaviours, new interventions are needed. This review discusses the clinical outcomes, biobehavioural mechanisms and implementation considerations for a novel, evidence‐based intervention for chronic pain, opioid use and OUD called Mindfulness‐Oriented Recovery Enhancement (MORE). MORE unites complementary aspects of mindfulness training, cognitive behavioural therapy and principles from positive psychology to simultaneously address addictive behaviour, emotion dysregulation and chronic pain by targeting brain reward and stress systems. MORE has been tested in 13 completed randomized clinical trials, including over 1300 patients, and has demonstrated efficacy against a range of active control conditions for reducing opioid dosing, opioid misuse, illicit drug use, depression, post‐traumatic stress symptoms and chronic pain. Opportunities for implementing MORE include facilitating opioid tapering and promoting safe opioid use in primary care and specialty pain clinics, decreasing opioid misuse and enhancing medication‐assisted treatment for OUD. Given evidence of MORE's efficacy, it is now time to consider disseminating this evidence‐based treatment in the United States, the United Kingdom and worldwide. [ABSTRACT FROM AUTHOR]

Negative and positive urgency are two closely related personality traits that reflect the tendency for an individual to engage in maladaptive risk-taking in response to extreme negative and positive emotions, respectively. However, other prominent emotion theories describe how emotions contribute to adaptive, rather than maladaptive, decision-making. This conceptual review considers how Urgency Theory can be integrated with these broader existing emotion theories. We proceed as follows: a) briefly define what is meant by emotions in science and summarize basic human neuroscience underlying emotions; b) briefly describe select theories and research linking emotions to adaptive decision-making, including brain correlates of this effect; c) review Urgency Theory, including contrasting evidence that emotions lead to maladaptive outcomes and brain correlates of this effect; d) discuss how urgency can be integrated into theories that view emotions as both adaptive and maladaptive for decision-making; and e) propose future directions to advance research in this field. We identified four, not mutually exclusive, viable options to integrate Urgency Theory into existing theories: urgency as model-free emotion regulation, urgency as being driven by incidental emotions, urgency as a reflexive response to emotions, or urgency as an individual difference factor. We conclude that although all four options are viable, individual difference and model-free emotion regulation have the most empirical support to date. Importantly, the other two options are less well-researched. Direct tests comparing these integrations is necessary to determine the most accurate way to integrate urgency with existing emotion theories. We believe that this research can identify mechanisms underlying urgency and help inform future intervention and prevention development to reduce negative effects of urgency across numerous maladaptive behaviors and clinical disorders. [ABSTRACT FROM AUTHOR]

Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes. The mechanisms underlying placebo analgesia are not fully understood. Here the authors show that a placebo treatment inducing analgesia in healthy participants reduced fMRI activity in systems related to evaluative and affective processes, but not systems related to nociceptive pain processing. [ABSTRACT FROM AUTHOR]

Understanding emotions and sensations from a neuroscientific context is essential for unravelling the complex mechanisms underlying human behaviour and cognition. Emotions are created by maintaining a positive or negative relationship of an individual towards the experiences and facts of reality. Sensations are the result of the influence of the material world on our senses or receptors. Recent advancements in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have provided unprecedented insights into the neural circuitry underlying emotions and sensations. Additionally the role of neurotransmitters, neural networks and brain regions are implicated in emotion regulation, sensory processing and their integration. Furthermore, there is impact of emotional and sensory dysregulation on various psychiatric disorders, including anxiety, depression and post-traumatic stress disorder (PTSD), highlighting potential avenues for therapeutic interventions. Overall, the importance of interdisciplinary research is in bridging the gap between neuroscience and psychology, paving the way for a deeper understanding of human emotions and sensations which is reflected in both simple experiences is emphasised. [ABSTRACT FROM AUTHOR]

Moods, orthodoxy holds, exist outside the space of reasons. A depressed subject may change their thoughts and behaviors as a result of their depression. But, according to this view, their mood gives them no genuine reason to do so. Instead, moods are mere causal influences on cognition. The issue is that moods, with their diffuse phenomenology, appear to lack intentionality (Directionlessness). But intentionality appears to be a necessary condition on rationality (The Content Constraint). Together, these principles conflict with the idea that moods are rational states of mind (Mood Rationality). The inconsistency of these three principles is the Puzzle of Mood Rationality. Now to many, this puzzle is hardly vexing: we should reject Mood Rationality. But, I argue, Mood Rationality is true despite its unpopularity. Thus, we need another way of resolving the puzzle. To do so, I distinguish intentionality as a first‐personal, phenomenological notion from representation as a third‐personal, cognitive scientific notion. I then argue that moods satisfy a revised version of the Content Constraint and sketch an account of moods as representational but non‐intentional mental states, drawing on the Valuationist paradigm in affective neuroscience. I end by showing how this account enables us to explain moods’ rationality. [ABSTRACT FROM AUTHOR]

Bats are often considered to be objects of biophobia, i.e., the tendency to respond with a negative emotion, such as fear or disgust, especially during the COVID-19 pandemic. However, existing studies have rarely compared both positive and negative emotions towards bats, leading to a potential negativity bias. This is crucial given the importance of emotions to bat-related human behaviours, such as in bat conservation-related actions. Via two online surveys conducted among German residents, we aimed to (i) assess positive and negative emotions towards bats, (ii) examine emotional shifts during the pandemic and (iii) explore how emotions, along with socio-demographics, predict the intent to perform bat-conservation actions. The first survey was undertaken ten months after the official declaration of the COVID-19 pandemic (December 2020 - January 2021), when bats gained societal attention due to speculation about the origin of the SARS-CoV-2 virus, and the second one ran twelve months later (January 2022). Overall, respondents held higher positive emotions than negative ones towards bats in both surveys, with no significant emotional shift observed. Positive emotions positively correlated with intentions to perform bat-conservation actions, while negative emotions showed no such relationship. Although our findings might be context-specific to populations in Germany or Europe, given European-Union legislation protecting bats and their habitats, they highlight the nuanced and complicated emotions that can be associated with certain species. Understanding these emotions can guide targeted conservation strategies and public outreach. Our results caution against overly generalising discussions of biophobia in conservation. [ABSTRACT FROM AUTHOR]

Emotion recognition plays a crucial role in affective computing, and electroencephalography (EEG) signals are increasingly applied in this field due to their effectiveness in reflecting brain activity. In this paper, we propose a novel EEG emotion recognition model that combines the ReliefF-based Graph Pooling Convolutional Network and BiGRU Attention Mechanisms (RGPCN-BiGRUAM). RGPCN-BiGRUAM effectively integrates the advantages of graph convolutional networks and recurrent neural networks. By incorporating ReliefF weights and an attention mechanism into graph pooling, our model enhances the aggregation of high-quality features while discarding irrelevant ones, thereby improving the efficiency of information transmission. The implementation of a multi-head attention mechanism fusion in BiGRU addresses the limitations of single-output features, achieving optimal selection of global features. Comparative experiments on public datasets SEED and DEAP demonstrate that our proposed RGPCN-BiGRUAM significantly improves classification performance compared to classic algorithms, achieving state-of-the-art results. Ablation studies further validate the design principles of our model. The results of this study indicate that RGPCN-BiGRUAM has strong potential for EEG emotion recognition, offering substantial possibilities for future applications. [ABSTRACT FROM AUTHOR]

This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom (version 5.16.6) to set up a virtual Mars station using custom simulation software (Mars Star City, version 4.0). Participants were divided into 14 experimental teams, which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multi-level regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an "emotional rollercoaster"—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more while speaking less, leads to better results. [ABSTRACT FROM AUTHOR]

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience. [ABSTRACT FROM AUTHOR]

Introduction: Burnout poses a substantial, ongoing threat to healthcare worker (HCW) wellbeing and to the delivery of safe, quality healthcare. While systemic and organization-level changes in healthcare are critically important, HCWs also need individual-level skills to promote resilience. The objective of this trial is to test feasibility, acceptability, and efficacy of PARK, an online self-guided positive affect regulation intervention, in a sample of healthcare workers during the COVID-19 pandemic. Design and methods: In the context of the unprecedented rise in burnout during the COVID-19 pandemic, we conducted a randomized waitlist-controlled trial of the Positive Affect Regulation sKills (PARK) program—a five-week, online, self-guided coping skills intervention nested within an ongoing cohort of HCWs. N = 554 healthcare workers were randomly assigned to receive the intervention immediately or to receive the intervention after approximately 12 weeks. Outcomes included change in burnout, emotional wellbeing (positive affect, meaning and purpose, depression, anxiety) and sleep over approximately 12 weeks. Analyses included mixed-effects linear regression models comparing change over time in outcomes between intervention and control conditions. Results: One third (n = 554) of the participants in the cohort of HCWs consented to participate and enrolled in PARK in April 2022. Compared to those who did not enroll, participants in the trial reported higher burnout, poorer emotional wellbeing, and poorer sleep at baseline (April, 2022; all ps <.05). Intent-to-treat analyses showed that participants randomly assigned to the intervention immediately (PARK-Now) improved significantly on anxiety (within-group change on PROMIS T-score = -0.63; p =.003) whereas those in the waitlist (PARK-Later) did not (within group T-score change 0.04, p = 0.90). The between-group difference in change, however, was not statistically significant (B = -0.67 p = 0.10). None of the other wellbeing outcomes changed significantly in the intervention group compared to the waitlist. Additional as-treated analyses indicated that those participants who completed all 5 of the weekly online lessons (N = 52; 9.4%) improved significantly more on the primary outcome of positive affect compared to those who enrolled in PARK but completed zero lessons (n = 237; 42.8%; B = 2.85; p =.0001). Conclusions: Online self-guided coping skills interventions like PARK can be effective in targeted samples and future work will focus on adaptations to increase engagement and tailor PARK for HCWs who could most benefit. [ABSTRACT FROM AUTHOR]

Background: Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders with higher incidence in males and is characterized by atypical verbal/nonverbal communication, restricted interests that can be accompanied by repetitive behavior, and disturbances in social behavior. This study investigated brain mechanisms that contribute to sociability deficits and sex differences in an ASD animal model. Methods: Sociability was measured in C58/J and C57BL/6J mice using the 3-chamber social choice test. Bulk RNA-Seq and snRNA-Seq identified transcriptional changes in C58/J and C57BL/6J amygdala within which DMRseq was used to measure differentially methylated regions in amygdala. Results: C58/J mice displayed divergent social strata in the 3-chamber test. Transcriptional and pathway signatures revealed immune-related biological processes differ between C58/J and C57BL/6J amygdala. Hypermethylated and hypomethylated genes were identified in C58/J versus C57BL/6J amygdala. snRNA-Seq data in C58/J amygdala identified differential transcriptional signatures within oligodendrocytes and microglia characterized by increased ASD risk gene expression and predicted impaired myelination that was dependent on sex and sociability. RNA velocity, gene regulatory network, and cell communication analysis showed diminished oligodendrocyte/microglia differentiation. Findings were verified using Bulk RNA-Seq and demonstrated oxytocin's beneficial effects on myelin gene expression. Limitations: Our findings are significant. However, limitations can be noted. The cellular mechanisms linking reduced oligodendrocyte differentiation and reduced myelination to an ASD phenotype in C58/J mice need further investigation. Additional snRNA-Seq and spatial studies would determine if effects in oligodendrocytes/microglia are unique to amygdala or if this occurs in other brain regions. Oxytocin's effects need further examination to understand its' potential as an ASD therapeutic. Conclusions: Our work demonstrates the C58/J mouse model's utility in evaluating the influence of sex and sociability on the transcriptome in concomitant brain regions involved in ASD. Our single-nucleus transcriptome analysis elucidates potential pathological roles of oligodendrocytes and microglia in ASD. This investigation provides details regarding regulatory features disrupted in these cell types, including transcriptional gene dysregulation, aberrant cell differentiation, altered gene regulatory networks, and changes to key pathways that promote microglia/oligodendrocyte differentiation. Our studies provide insight into interactions between genetic risk and epigenetic processes associated with divergent affiliative behavior and lack of positive sociability. [ABSTRACT FROM AUTHOR]

AbstractEmotion recognition using EEG is a difficult study because the signals’ unstable behavior, which is brought on by the brain’s complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model’s utility in emotional computing. [ABSTRACT FROM AUTHOR]

Background: The interplay between teaching engagement and performance has garnered attention in both theoretical and empirical research, primarily due to its influence on student academic achievement, teacher well-being, and the realization of institutional goals. This is especially pertinent in the realm of preschool education, where the scope of learning extends beyond academic content to encompass the broader socialization of children. Drawing from Affective Neuroscience research, this study investigates the role of affective tendencies as mediators in the relationship between work engagement and job performance. Objective: The primary aim of this research is to examine a chain mediation model that hypothesizes the predictive role of teacher engagement. This model posits the intermediary influence of four basic emotions--CARING, SEEKING, ANGER, and FEAR--followed by the mediating effect of job satisfaction on teacher job performance. Method: The study utilized a sample of 842 Chinese preschool teachers. Data were collected through an online questionnaire, employing a time-lagged design. The analysis was conducted using Model 80 of the PROCESS Macros. Results: The findings reveal that both positive and negative emotions significantly predict teachers' job satisfaction. However, job satisfaction does not influence job performance. The analysis confirmed the direct and total effects of teacher engagement, as well as the indirect effects, particularly through the positive emotion of Caring. Implications: The results are instrumental in informing and refining interventions designed to enhance teacher engagement and performance, underscoring the importance of emotional factors in the educational environment. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self‐supervised pre‐training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self‐supervised pre‐training without the need for direct access to specific emotion category labels, enabling it to pre‐train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo‐labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self‐supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition. [ABSTRACT FROM AUTHOR]

Inflammatory bowel diseases (IBDs) are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The gut–brain axis is disrupted in IBDs, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBDs affects the brain's inflammatory response system, hormonal axis, and blood–brain barrier integrity, influencing the gut microbiota. This review aims to explore the association between dysregulations in the gut–brain axis, serum biomarkers, and the development of cognitive disorders. Studies suggest a potential association between IBDs and the development of neurodegeneration. The mechanisms include systemic inflammation, nutritional deficiency, GBA dysfunction, and the effect of genetics and comorbidities. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on neurodegeneration. Serum levels of vitamins, inflammatory and neuronal damage biomarkers, and neuronal growth factors have been investigated for their potential to predict the development of neurodegenerative diseases, but current results are inconclusive and require more studies. [ABSTRACT FROM AUTHOR]

This article proposes a pattern theory of compassion on a model similar to a pattern theory of emotion. This pattern theory of compassion is consistent with cognitive and affective neuroscience studies of compassion. The pattern theory of compassion facilitates sharp conceptual distinctions among compassion, empathy, and sympathy. A clear conception of compassion is helpfully relevant to compassion training and the development of compassion-based therapies in mental health and medical contexts. Concepts of empathy, sympathy and compassion are often confused in a variety of literatures. This article proposes a pattern-theoretic approach to distinguishing compassion from empathy and sympathy. Drawing on psychology, Western philosophy, affective neuroscience, and contemplative science, we clarify the nature of compassion as a specific pattern of dynamically related factors that include physiological, cognitive, and affective processes, relational/intersubjective processes, and motivational/action tendencies. We also show that the dynamic nature of the compassion pattern is reflected in neuroscientific findings, as well as in compassion practice. The pattern theory of compassion allows us to make several clear distinctions between compassion, empathy, and sympathy. [ABSTRACT FROM AUTHOR]

Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various domains, such as healthcare and entertainment. EEG signals have been particularly useful in emotion recognition due to their non-invasive nature and high temporal resolution. However, the development of accurate and efficient algorithms for emotion classification using EEG signals remains a challenging task. This paper proposes a novel hybrid algorithm for emotion classification based on EEG signals, which combines multiple adaptive network models and probabilistic neural networks. The research aims to improve the recognition accuracy of three and four emotions, which has been a challenge for existing approaches. The proposed model consists of N adaptively neuro-fuzzy inference system (ANFIS) classifiers designed in parallel, in which N is the number of emotion classes. The selected features with the most appropriate distribution for classification are given as input vectors to the ANFIS structures, and the system is trained. The outputs of these trained ANFIS models are combined to create a feature vector, which provides the inputs for adaptive networks, and the system is trained to acquire the emotional recognition output. The performance of the proposed model has been evaluated for classification on well-known emotion benchmark datasets, including DEAP and Feeling Emotions. The study results indicate that the model achieves an accuracy rate of 73.49% on the DEAP datasets and 95.97% on the Feeling Emotions datasets. These results demonstrate that the proposed model efficiently recognizes emotions and exhibits a promising classification performance. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization. [ABSTRACT FROM AUTHOR]

While previous research has investigated the effects of emotional videos on peripheral physiological measures and conscious experience, this study extends the research to include electrocortical measures, specifically the steady‐state visual‐evoked potential (ssVEP). A carefully curated set of 45 videos, designed to represent a wide range of emotional and neutral content, were presented with a flickering border. The videos featured a continuous single‐shot perspective, natural soundtrack, and excluded elements associated with professional films, to enhance realism. The results demonstrate a consistent reduction in ssVEP amplitude during emotional videos which strongly correlates with the rated emotional intensity of the clips. This suggests that narrative audiovisual stimuli have the potential to track dynamic emotional processing in the cortex, providing new avenues for research in affective neuroscience. The findings highlight the potential of using realistic video stimuli to investigate how the human brain processes emotional events in a paradigm that increases ecological validity. Future studies can further develop this paradigm by expanding the video set, targeting specific cortical networks, and manipulating narrative predictability. Overall, this study establishes a foundation for investigating emotional perception using realistic video stimuli and has the potential to expand our understanding of real‐world emotional processing in the human brain. Affective neuroscience research depends on the ability to evoke states of emotion that are comparable to real‐life situations. Here we demonstrate a novel laboratory paradigm in which electrocortical effects can be estimated during naturalistic emotional multimodal video perception. Realistic video stimuli may enable research questions regarding dynamic emotional processes to be investigated. [ABSTRACT FROM AUTHOR]

This document is a collection of abstracts from various articles on the topic of loneliness. The articles cover a range of subjects, including the impact of the internet on loneliness, the role of primary care in addressing social isolation, the historical increases in loneliness in the United States compared to Europe, the biological effects of loneliness, the importance of human touch in preventing a lonely death, the relationship between loneliness and volunteering, the use of companion robots to alleviate loneliness in older adults, and the impact of cat fostering on loneliness and well-being in older adults. These articles provide valuable insights into the causes and consequences of loneliness and offer potential strategies for addressing this issue. One study specifically examined the impact of cat fostering on older adults' interest in cat adoption when perceived barriers to adoption were removed. The study found that the majority of participants (95.7%) adopted their fostered cat, and there was a significant improvement in loneliness and a near-significant improvement in mental health after 4 months of fostering. This suggests that fostering a shelter cat with the option for adoption may be an effective solution for alleviating loneliness and improving mental health in older adults. [Extracted from the article]