Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research.

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.

Emotional sociology applied: predictive influence of affective neuroscience personality traits on Chinese preschool teachers' performance and wellbeing.

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.

Brain representations of affective valence and intensity in sustained pleasure and pain.

Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study (n = 58) of sustained pleasure and pain. We found that the distinct subpopulations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity. Affective valence and intensity predictive models showed significant decoding performance in an independent test dataset (n = 62). These models were differentially connected to distinct large-scale brain networks-the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting a systems-level understanding of human affective experiences.

EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets.

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.

The pattern theory of compassion.

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.

Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.

Introduction: The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. Methods: The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Results: Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. Discussion: In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.

Understanding Spontaneous Symbolism in Psychotherapy Using Embodied Thought.

Spontaneous, unwilled subjective imagery and symbols (including dreams) often emerge in psychotherapy that can appear baffling and confound interpretation. Early psychoanalytic theories seemed to diverge as often as they agreed on the meaning of such content. Nevertheless, after reviewing key findings in the empirical science of spontaneous thought as well as insights gleaned from neuroscience and especially embodied cognition, it is now possible to construct a more coherent theory of interpretation that is clinically useful. Given that thought is so thoroughly embodied, it is possible to demonstrate that universalities in human physiology yield universalities in thought. Such universalities can then be demonstrated to form a kind of biologically directed universal "code" for understanding spontaneous symbolic expressions that emerge in psychotherapy. An example is given that illustrates how this can be applied to clinical encounters.

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain.

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.

Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought.

Is there an affective neuroscience of spirituality? The development and validation of the OCEANic feelings scale.

Background: Oceanic feelings represent a phenomenological structure of affective sensations that characteristically involve feelings of self-dissolution and feelings of unity and transcendence. This study presents the preliminary version of a self-report instrument to measure individual dispositions toward oceanic feelings in order to enable further research within the concept of primary emotions postulated by Jaak Panksepp. Methods: A first version of the questionnaire was applied to a total sample of 926 German-speaking adults of the general population. After performing item analysis and principal component analysis (PCA) in a first study (N = 300), the questionnaire was shortened. In a second study (N = 626), confirmatory factor analysis (CFA) was conducted and emerged scales were related to the already established instruments for the assessment of primary emotions (BANPS-GL) and Big Five personality traits (BFI-44). Results: The OCEANic scale exhibited reliabilities ranging from Cronbach's α = 0.82 (positive) to α = 0.88 (negative) and plausible correlations with behavioral traits related to the seven affective neurobiological systems (ANGER, FEAR, CARE, SEEK, PLAY, SADNESS, and LUST) as well as with personality factors measured by the Big Five Inventory. For CFA, a bifactorial model with an overall factor demonstrated good fit: RMSEA = 0.00 (90% CI:0.00, 0.03); TLI = 1.00; CFI = 1.00; NFI = 0.99. Discussion: The OCEANic scale enables the operationalization of oceanic feelings comprising two subscales and one total scale. The results indicate good reliability and acceptable factorial validity. Establishment and further validation of the OCEANic scale within future research will be needed to fully understand the role of oceanic feelings within the human affective life, especially the personality trait of spirituality.

Consciousness, the affectome, and human life.

I have been working on interactions between conscious and non-conscious processes since the late 1970s. In this commentary, I offer a perspective on conscious/non-conscious interactions that might a useful adjunct to the Human Affectome Project as it evolves.

Affective super-traits and/or individual patterns: a variable-centered and a person-centered approach of primary emotional aspects of personality.

Theoretical approaches of personality structure are diverse. We examine the primary emotional aspects of personality as the correspondence of two mainstream constructs: the lexically-based Big Five (BIG5) and the biologically-based Affective Neuroscience Theory (ANT) within two approaches. In the variable-centered approach (VCA), our aim is to identify affective super-traits; while in the person-centered approach (PCA) to uncover latent profile patterns. 240 participants (177 women, 63 men) completed the 112-item affective neuroscience personality scales (ANPS), and the 44-item Big Five Inventory (BFI). We identified four super-traits: Negative emotions (FEAR, SADNESS, Emotional instability), Positive emotions and stimulation (SEEK, Extraversion), Affiliation and social bonds (reversed ANGER, CARE, Agreeableness), Self-regulation (PLAY, Conscientiousness. Based on the VCA, we conclude that the four super-traits represent two main affective tendencies (Positive emotions and approaching, Negative emotions and avoidance), interpersonal (Affiliation) and intrapersonal (Self-regulation) dynamics of personality. As a result of Latent Profile Analysis in the PCA, we explored three latent groups with different patterns of primary emotional traits based on their responsiveness (Highly emotional, Balanced, Low emotional). Our findings provide a holistic approach to emotional aspects of personality, and might have further implications for clinical psychology, neuroscience, and cross-cultural studies on emotions.

Affective neuroscience: applications for sexual medicine research and clinical practice.

INTRODUCTION: Affective neuroscience is the study of the brain substrates of emotional, embodied experiences. Affective neuroscience theory (ANT) is based on experimental evidence that all mammals are hardwired with 7 primary subcortical emotional operating systems, or "core emotions," that have overlapping but distinct circuits buried in the deep, ancient parts of the brain. Imbalances in the 7 core emotions can affect multiple aspects of the individual's psychosocial well-being (eg, depression, anxiety, substance abuse). Here, we propose that core emotions can also influence sexual function and, specifically, that imbalances in core emotions are the bridge connecting psychiatric symptoms (eg, anhedonia) to sexual dysfunction (eg, anorgasmia). OBJECTIVES: In this targeted review and commentary, we outline potential connections between ANT and sexual medicine research and clinical practice. We summarize ANT by defining the 3-level BrainMind and core emotions; examining how they relate to personality, behavior, and mental health; and determining the implications for sexual health research and clinical practice. METHODS: A targeted literature review was conducted. Case studies were adapted from client files and clinician interviews and then anonymized. RESULTS: We propose a novel organizational schema for implementing affective balance therapies for sexual dysfunction, which integrate psychoeducational, somatic, and cognitive therapeutic approaches under the ANT framework. We provide 3 patient case studies (anorgasmia, hypersexuality, spinal cord injury) outlining the implementation of this approach and patient outcomes. CONCLUSION: ANT has practical translational applications in sexual health research and clinical practice. By integrating our understanding of the role of core emotions in human sexuality, clinicians can better tailor treatments to address sexual dysfunction.

Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI.

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Realizing consumers' existential dreams via product marketing and mixed reality: a perspective based on affective neuroscience theories.

In an era of swift societal changes and escalating consumerism, this paper presents an exploration of an innovative approach that integrates product marketing strategies, mixed reality (MR) technology, and affective neuroscience theories to actualize consumers' existential dreams. MR, with its unique capacity to blend the virtual and real worlds, can enhance the consumer experience by creating immersive, personalized environments that resonate with consumers' existential aspirations. Insights from affective neuroscience, specifically the brain's processing of emotions, guide the development of emotionally engaging marketing strategies, which strengthen the connection between consumers, products, and brands. These integrated strategies not only present a novel blueprint for companies to deepen consumer engagement but also promise more fulfilling and meaningful consumer experiences. Moreover, this approach contributes to societal well-being and prosperity, marking a significant stride in the field of marketing.

Brief Form of the Affective Neuroscience Personality Scales: Preliminary Psychometric Properties in a European Portuguese Community Sample.

Personality theory based on affective neuroscience research suggests the presence of seven affective neurobiological systems. These dimensions have been identified using psychometric instruments such as the Affective Neuroscience Personality Scales (ANPS) and its brief version known as the Brief Affective Neuroscience Personality Scales (BANPS). Despite being a psychometric valid instrument, the BANPS was not adapted to European Portuguese. This study describes a psychometric analysis of the BANPS in a Portuguese population sample. A European Portuguese-speaking sample was recruited (N = 355, Mage = 27.31, DP = 12.75). Confirmatory factor analysis (CFA) was used to confirm the BANPS factorial structure. Pearson correlations were used to explore convergent validity with self-reports for psychological distress and psychopathology. Six factor model was confirmed with adequate goodness-of-fit indices (χ2(449) = 808,9841, TLI = .90, CFI = .92, RMSEA = .048 (.042-.053). Non-agreeable affective systems (anger, fear, distress), correlated positively with psychological distress and symptomology, while play and care systems correlated negatively. Seek and care subscales showed the weakest psychometric properties. The original factor structure was confirmed, suggesting the BANPS may be a valid measure to assess affective behavioral traits in the Portuguese population. Further studies in clinical populations may improve the psychometric data of the BANPS.

Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modeling and event-related potentials.

It has been hypothesized that lonely individuals demonstrate hypervigilance toward social threats. However, recent studies have raised doubts about the reliability of tasks commonly used to measure attentional biases toward threats. Two alternative approaches have been suggested to overcome the limitations of traditional analysis of attentional bias. First, the neurophysiological indicators of orienting to threats were shown to have superior psychometric characteristics compared to overt measures of behavioral performance. The second approach involves utilizing computational modeling to isolate latent components corresponding to specific cognitive mechanisms from observable data. To test the usefulness of these approaches in loneliness research, we analyzed behavioral and electroencephalographic (EEG) data from 26 lonely and 26 non-lonely participants who performed a dot-probe task using a computational modeling approach. We applied the Drift Diffusion Model (DDM) and extracted N2pc-an event-related potential that serves as an indicator of spatial attention. No evidence for social threat hypervigilance has been found in DDM parameters nor in N2pc characteristics in the current study. However, we did observe decreased drift rate and increased variability in drift rate between trials within the lonely group, indicating reduced efficiency in perceptual decision-making among lonely individuals. These effects were not detected using standard behavioral measures used in the dot-probe paradigm. Given that DDM indicators were sensitive to differences in perceptual discrimination between the two groups, even when no overt differences were found in standard behavioral measures, it may be postulated that computational approaches offer a more comprehensive understanding of cognitive processes.

The German version of the brief affective neuroscience personality scales including a LUST scale (BANPS-GL).

Objectives: This study presents the German version of the Brief Affective Neuroscience Personality Scales (BANPS), which includes an additional subscale for the dimension LUST. The BANPS represents a shortened version of the Affective Neuroscience Personality Scales (ANPS), a self-report instrument to assess individual dispositions toward primary emotional systems as proposed by Jaak Panksepp. Methods: In a large sample (N = 926), the reliability and various facets of validity of the German translation of the BANPS were examined together with the newly included LUST scale. The BANPS-GL was related to the Big Five Inventory (BFI) and Sexual Sensation Seeking Scale (SSSS) and analyzed via confirmatory factor analysis (CFA). Results: Overall, the BANPS-GL exhibited reliabilities ranging from McDonald's ω = 0.70 (CARE) to α = 0.86 (SADNESS) and plausible correlations with external criteria. For CFA a correlated 7-factor model demonstrated good fit [TLI = 0.95; RMSEA = 0.04 (90% CI: 0.04, 0.05); SRMR = 0.06]. A similar fit was demonstrated for a 2-higher-factor model [TLI = 0.93; RMSEA = 0.05 (90% CI: 0.05, 0.06); SRMR = 0.07]. Conclusion: In broad agreement with the results of the original English version, the BANPS-GL showed good reliability and acceptable factorial validity, and overall improved the psychometric properties of the original long form. Finally, the inclusion of the dimension LUST allows for a complete coverage of the primary emotion dispositions as originally conceptualized by Panksepp.

EEG Microstates in Social and Affective Neuroscience.

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.

Play and tickling responses map to the lateral columns of the rat periaqueductal gray.

The persistence of play after decortication points to a subcortical mechanism of play control. We found that global blockade of the rat periaqueductal gray with either muscimol or lidocaine interfered with ticklishness and play. We recorded vocalizations and neural activity from the periaqueductal gray of young, playful rats during interspecific touch, play, and tickling. Rats vocalized weakly to touch and more strongly to play and tickling. Periaqueductal gray units showed diverse but strong modulation to tickling and play. Hierarchical clustering based on neuronal responses to play and tickling revealed functional clusters mapping to different periaqueductal gray columns. Specifically, we observed play-neutral/tickling-inhibited and tickling/play-neutral units in dorsolateral and dorsomedial periaqueductal gray columns. In contrast, strongly play/tickling-excited units mapped to the lateral columns and were suppressed by anxiogenic conditions. Optogenetic inactivation of lateral periaqueductal columns disrupted ticklishness and play. We conclude that the lateral periaqueductal gray columns are decisive for play and laughter.