Neural Circuits for Emotion.

Emotions are fundamental to our experience and behavior, affecting and motivating all aspects of our lives. Scientists of various disciplines have been fascinated by emotions for centuries, yet even today vigorous debates abound about how to define emotions and how to best study their neural underpinnings. Defining emotions from an evolutionary perspective and acknowledging their important functional roles in supporting survival allows the study of emotion states in diverse species. This approach enables taking advantage of modern tools in behavioral, systems, and circuit neurosciences, allowing the precise dissection of neural mechanisms and behavior underlying emotion processes in model organisms. Here we review findings about the neural circuit mechanisms underlying emotion processing across species and try to identify points of convergence as well as important next steps in the pursuit of understanding how emotions emerge from neural activity.

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.

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.

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.

The phobic brain: Morphometric features correctly classify individuals with small animal phobia.

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.

Large-scale lesion symptom mapping of depression identifies brain regions for risk and resilience.

Understanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we performed a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression. Five hundred and twenty-six individuals participated in the study across two sites (356 male, average age 52.4 ± 14.5 years). Each subject had a focal brain lesion identified on structural imaging and an assessment of depression using the Beck Depression Inventory-II, both obtained in the chronic period post-lesion (>3 months). Multivariate lesion-symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as 'risk' versus 'resilience' regions. The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively. Lesion-symptom mapping identified brain regions significantly associated with both higher and lower depression severity (r = 0.11; P = 0.01). Peak 'risk' regions include the bilateral anterior insula, bilateral dorsolateral prefrontal cortex and left dorsomedial prefrontal cortex. Functional lesion network mapping demonstrated that these 'risk' regions localized to nodes of the salience network. Peak 'resilience' regions include the right orbitofrontal cortex, right medial prefrontal cortex and right inferolateral temporal cortex, nodes of the default mode network. Structural lesion network mapping implicated dorsal prefrontal white matter tracts as 'risk' tracts and ventral prefrontal white matter tracts as 'resilience' tracts, although the structural lesion network mapping findings did not survive correction for multiple comparisons. Taken together, these results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.

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.

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.

The relationship of psychological health and primary emotional traits in medical students.

INTRODUCTION: Several studies have called for attention to medical students' well-being. Building on the neuroevolutionary affective neuroscience perspective that views primary emotional systems as central to well-being and the foundation of personality, this study investigated the facets of medical students' psychological well-being that are challenged and the relationships between emotional traits, psychological well-being, and depression. METHODS: In a single-center cross-sectional study, medical students' primary emotional traits (SEEKING, FEAR, ANGER, SADNESS, CARE, PLAY and Spirituality), psychological well-being dimensions (autonomy, environmental mastery, positive relations, self-acceptance, purpose in life, and personal growth), and depressive symptoms were assessed using the Affective Neuroscience Personality Scale; the Psychological Well-being Scale, which provides normative data; and the Beck Depression Inventory. RESULTS: Compared with the normative data, the medical students perceived lower psychological autonomy, positive relations, and self-acceptance but higher purpose in life. The medical students' emotional traits were related to specific psychological well-being facets and depression. SEEKING and, inversely, FEAR were related to well-being across dimensions and depressive symptoms. CONCLUSION: Our findings are the first to show a link between emotional traits and specific facets of psychological health in medical students. Thus, this study encourages medical teachers to set learning environments that target multiple facets of well-being that harness primary emotional traits.

Differential chronic social stress models in male and female mice.

Chronic stress creates an allostatic overload that may lead to mood disorders such as anxiety and depression. Modern causes of chronic stress in humans are mostly social in nature, relating to work and relationship stress. Research into neural and molecular mechanisms of vulnerability and resilience following chronic social stress (CSS) is ongoing and uses animal models to discover efficient prevention strategies and treatments. To date, most CSS studies have neglected the female sex and used male-focused aggression-based animal models such as chronic social defeat stress (CSDS). Accumulating evidence on sex differences suggests differences in the stress response, the prevalence of stress-related illness and in response to treatment, indicating that researchers should expand CSS investigation to include female-focused protocols alongside the popular CSDS protocols. Here, we describe a novel female mouse model of CSS and a parallel modified male mouse model of CSDS in C57BL/6 mice. These new models enable the investigation of vulnerability, coping and downstream effectors mediating short-term and long-term consequences of CSS in both sexes. Our data demonstrate differential effects on male and female mice during, soon after, and many weeks after CSS. Female mice are more prone to body weight loss during CSS and hyperactive anxious behaviour following CSS. Both sexes show reduced social interaction, but only stressed male mice show long-term changes in emotional memory and neuroendocrine function. We further discuss future avenues of research using these models to investigate mechanisms pertaining to sensitivity to CSS and treatment response profiles, in a sex-appropriate manner.

Discrete emotions discovered by contactless measurement of facial blood flows.

ABSTRACTExperiential and behavioural aspects of emotions can be measured readily but developing a contactless measure of emotions' physiological aspects has been a major challenge. We hypothesised that different emotion-evoking films can produce distinctive facial blood flow patterns that can serve as physiological signatures of discrete emotions. To test this hypothesis, we created a new Transdermal Optical Imaging system that uses a conventional video camera to capture facial blood flows in a contactless manner. Using this and deep machine learning, we analysed videos of the faces of people as they viewed film clips that elicited joy, sadness, disgust, fear or a neutral state. We found that each of these elicited a distinct blood flow pattern in the facial epidermis, and that Transdermal Optical Imaging is an effective contactless and inexpensive tool to the reveal physiological correlates of discrete emotions.

Psychometric validation of the polish version of the emotional style questionnaire.

In three studies, we document the validity of the Polish version of the Emotional Style Questionnaire (ESQ)-a 24-item self-report measure that captures how people vary across six dimensions that make up a healthy emotional life. Study 1 provides support for the proposed factorial structure of the Polish version of the scale and gender measurement invariance, demonstrates the similarity of the Polish and English versions of the scale, and reveals the excellent test-retest reliability of the scale across an interval of two months. Study 2 confirms the adequacy of the factorial structure in another sample, shows that the construct validity for each of the six subscales is similar to that of the original scale, and documents the age measurement invariance of the scale. Finally, in Study 3, we investigate measurement invariance across Polish and American samples and demonstrate that Poles are characterized by lower scores on healthy emotionality and its dimensions compared to Americans. We conclude that the Polish version of the ESQ is a psychometrically valid and easily implementable measurement tool that can be applied in research settings to measure healthy emotionality and its components. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-03323-4.

Psychometric validation of the Persian version of the Emotional Style Questionnaire.

Emotions play a central role in scientific models of decision-making, human development, interpersonal processes, psychopathology, and well-being. The Emotional Style Questionnaire (ESQ) is a novel and multifaceted psychometric scale that assesses the dimensions of individual's emotional styles. The present study evaluated the validity and factor structure of the Persian ESQ. The original version of the ESQ was translated and back-translated into Persian, followed by a pilot study. A sample of university students and staff participated in a survey (n = 822) which included the ESQ, Overall Anxiety Severity and Impairment Scale (OASIS), World Health Organization Quality of Life Brief Version (WHOQOL-BREF), Difficulties in Emotion Regulation Scale (DERS), and Positive and Negative Affect Schedule (PANAS). Results showed that the Persian ESQ had very good internal consistency (Cronbach's alpha coefficient: 0.84; Composite reliability = 0.89) and adequate test-retest reliability after four weeks (intraclass coefficient, r = .71 with 95% CI [.63, .77]). The confirmatory factor analysis model fitted the data well (χ2/df = 2.86, CFI = .970, SRMR = .046, PCLOSE = .85 > .05, RMSEA = .048, 90% CI [.043, .053]). Also, measurement invariance indicated the ESQ had acceptable construct validity among different groups. As for criterion-related validity, the ESQ positively correlated with scores on the WHOQOL-BREF (r = .76) and PANAS-positive affect (r = .62), and negatively correlated with the scores on the OASIS (r = -.68), DERS (r = -.39), and PANAS-negative affect (r = -72). The findings provide evidence that the ESQ is a reliable and valid instrument for assessing healthy emotionality among Persian speaking individuals. The Persian ESQ can be used in psychological intervention and clinical research in Iran and other Persian-speaking countries. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-020-01205-1.

Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation.

When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.

Within-person increase in pathological worry predicts future depletion of unique executive functioning domains.

BACKGROUND: Affective neuroscience and scar theories propose that increased excessive worry, the hallmark symptom of generalized anxiety disorder (GAD), predicts future declines in executive functioning (EF). However, the preponderance of cross-sectional designs used to examine between-person chronic worry-EF relationships has blocked progress on understanding their potentially causal within-person associations. Accordingly, this study used bivariate dual latent change score (LCS) models to test whether within-person increased GAD severity might relate to future reduced EF. METHODS: Community-dwelling adults (N = 2581, 46 years on average, s.d. = 11.40, 54.71% female) were assessed for GAD symptom severity (Composite International Diagnostic Interview-Short Form) across three waves, spaced about 9 years apart. Three aspects of EF [inhibition, set-shifting, and mixing costs (MCs; a measure related to common EF)], were assessed with stop-and-go switch tasks. Participants responded to 20 normal and 20 reverse single-task block trials and 32 mixed-task switch block trials. EF tests were administered at time 2 (T2) and time 3 (T3), but not at time 1 (T1). RESULTS: After controlling for T1 depression, LCS models revealed that within-person increased T1 - T2 GAD severity substantially predicted future reduced T2 - T3 inhibition and set-shifting (both indexed by accuracy and latency), and MC (indexed by latency) with moderate-to-large effect sizes (|d| = 0.51-0.96). CONCLUSIONS: Results largely support scar theories by offering preliminary within-person, naturalistic evidence that heightened excessive worry can negatively predict future distinct aspects of cognitive flexibility. Effectively targeting pathological worry might prevent difficulties arising from executive dysfunction.

Emotional Objectivity: Neural Representations of Emotions and Their Interaction with Cognition.

Recent advances in our understanding of information states in the human brain have opened a new window into the brain's representation of emotion. While emotion was once thought to constitute a separate domain from cognition, current evidence suggests that all events are filtered through the lens of whether they are good or bad for us. Focusing on new methods of decoding information states from brain activation, we review growing evidence that emotion is represented at multiple levels of our sensory systems and infuses perception, attention, learning, and memory. We provide evidence that the primary function of emotional representations is to produce unified emotion, perception, and thought (e.g., "That is a good thing") rather than discrete and isolated psychological events (e.g., "That is a thing. I feel good"). The emergent view suggests ways in which emotion operates as a fundamental feature of cognition, by design ensuring that emotional outcomes are the central object of perception, thought, and action.

Neural bases of social feedback processing and self-other distinction in late childhood: The role of attachment and age.

Attachment plays a key role in how children process information about the self and others. Here, we examined the neural bases of interindividual differences in attachment in late childhood and tested whether social cognition-related neural activity varies as function of age. In a small sample of 8-year-old to 12-year-old children (n = 21/19), we used functional magnetic resonance imaging to measure neural responses during social feedback processing and self-other distinction. Attachment was assessed using child self-report. The social feedback processing task presented smiling and angry faces either confirming or disconfirming written information about participant performance on a perceptual game. In addition to observing main effects of facial emotion and performance, an increase in age was related to a shift from negative (i.e., angry faces/bad performance) to positive (i.e., smiling faces/good performance) information processing in the left amygdala/hippocampus, bilateral fusiform face area, bilateral anterior temporal pole (ATP), and left anterior insula. There were no effects of attachment on social feedback processing. The self-other distinction task presented digital morphs between children's own faces and faces of their mother or stranger females. We observed differential activation in face processing and mentalizing regions in response to self and mother faces versus morphed faces. Furthermore, left ATP activity was associated with attachment anxiety such that greater attachment anxiety was related to a shift from heightened processing of self and mother faces to morphed faces. There were no effects of age on self-other distinction. We discuss our preliminary findings in the context of attachment theory and previous work on social evaluation and self-other processing.

Theoretical, and epistemological challenges in scientific investigations of complex emotional states in animals.

This review brings to light critical epistemological and theoretical considerations when studying complex emotional states in animals. We discuss anthropomorphic and Umwelt perspectives of nonhuman animals and the ways in which distinct theories of consciousness and neural processing may restrict the potential for the development of knowledge on the topic. Within the same line of argumentation, we consider influences of the debate between monism and dualism and psychology's behaviorism and cognitive theories. Finally, we contrast the affective consciousness, higher-order emotional consciousness, and constructed emotion theories to further our understanding of complex emotional states in animals.

The Role of the Amygdala and the Ventromedial Prefrontal Cortex in Emotional Regulation: Implications for Post-traumatic Stress Disorder.

The importance of the amygdala as a salience detector and in emotional learning is now well accepted. The mechanisms that regulate and inhibit the amygdala, however, are less well understood. This review provides evidence from imaging and lesion studies to support the role of the ventromedial prefrontal cortex (vmPFC) as a moderator and inhibitor of the amygdala. The dual inhibition model centres on the broadly defined ventromedial prefrontal cortex (vmPFC) and the distinct role of two of its subcomponents, the rostral anterior cingulate cortex and orbitofrontal cortex. The dual inhibition model posits that these two regions, along with their associated inhibitory pathways, must interact for adequate inhibitory control of the amygdala and emotional regulation. Following a description of the model's experimental support, it is then proposed as a neuropsychological mechanism for post-traumatic stress disorder (PTSD). Flashbacks, as a defining feature of PTSD, are described in terms of a subcortical orienting network. Finally, there is a discussion of how a neuropsychological understanding of post-traumatic stress disorder (PTSD) might inform a clinician's approach to treatment and how the dual inhibition model might have a more general application to understanding emotional dysregulation.

On motivational influences, moving beyond valence, and integrating dimensional and discrete views of emotion.

The field of cognition and emotion has grown considerably over the past 30 years, with an increased emphasis on the relationships between emotional and motivational components and how they contribute to basic perceptual, cognitive, and neural processes. For instance, research has revealed that emotion often influences these processes via emotion's relationship with motivational dimensions, as when positive emotions low versus high in approach motivational intensity have different influences on attentional and other cognitive processes. Research has also revealed that motivational direction (approach vs. withdrawal) and affective valence (positive vs. negative) are not as closely related as once was theorised; that is, positive affect is not inevitably associated with approach motivation and negative affect is not inevitably associated with withdrawal motivation. These and other lines of research with anger have suggested that the field needs to move beyond a focus on affective valence and it needs to consider an integration of dimensional and discrete models of emotion. The article also includes some suggestions for improving methods of measuring and inducing emotions and some recommendations for future researchers.