Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis

Frontal EEG correlation based human emotion identification and classification

Humans express their feelings and intentions of their actions or communication through emotions. Recent advancements in technology involve machines in human communication in day-to-day life. Thus, understanding of human emotions by machines will be very helpful in assisting the user in a far better way. Various physiological and non-physiological signals can be used to make the machines to recognize the emotion of a person. The identification of emotional content in the signals is crucial to understand emotion and the machines act with emotional intelligence at appropriate times, thus providing a better human machine interaction with emotion identification system and mental health monitoring for psychiatric patients. This work includes the creation of an emotion EEG dataset, the development of an algorithm for identifying the emotion elicitation segments in the EEG signal, and the classification of emotions from EEG signals. The EEG signals are divided into 3s segments, and the segments with emotional content are selected based on the decrease in correlation between the frontal electrodes. The selected segments are validated with the facial expressions of the subjects in the appropriate time segments of the face video. EEGNet is used to classify the emotion from the EEG signal. The classification accuracy with the selected emotional EEG segments is higher compared to the accuracy using all the EEG segments. In subject-specific classification, an average accuracy of 80.87% is obtained from the network trained with selected EEG segments, and 70.5% is obtained from training with all EEG segments. In subject-independent classification, the accuracy of classification is 67% and 63.8% with and without segment selection, respectively. The proposed method of selection of EEG segments is validated using the DEAP dataset, and classification accuracies and F1-scores of subject dependent and subject-independent methods are presented.

Emotional pictures in the brain and their interaction with the task: A fine-grained fMRI coordinate-based meta-analysis study

The impacting research on emotions of the last decades was carried out with different methods. The most popular was based on the use of a validated sample of slides, the International Affective Pictures System (IAPS), divided mainly into pleasant, neutral and unpleasant categories, and on fMRI as a measure of brain activation induced by these stimuli. With the present coordinate-based meta-analysis (CBMA) based on ALE approach, we aimed to unmask the main brain networks involved in the contrast of pleasant vs. neutral and unpleasant vs. neutral IAPS slide categories. Furthermore, we included studies employing both IAPS and non-IAPS (but analogously validated) pictures, a condition termed as IAPS EXTENDED. After selecting 97 papers published in the 2000-2023 interval, the planned contrasts were analyzed by also considering their interaction with the Load factor of the concomitant task, which comprised the conditions: No Load (passive viewing), Low-Load tasks and High-Load tasks. We analyzed a total of 152 experiments (106 focusing on the negative vs. neutral contrast; 46 reporting positive vs. neutral contrasts). We additionally performed conjunction and contrast analyses. Results confirmed outcomes of past meta-analyses on the involvement of a number of cortical and subcortical paralimbic and limbic regions during unpleasant picture processing, but the increase of the pubblications on this topic in last years, together with a more fine-grained analysis, allowed us to find also the involvement of additional areas, such as the right middle frontal gyrus, left inferior frontal gyrus (BA 9), posterior cingulate, and left inferior parietal lobule. Concerning passive viewing and low-load tasks, a clear frontal asymmetry emerged with greater right prefrontal activation (BA9) to unpleasant vs. pleasant pictures, whereas, during No Load tasks only, left frontal dominance to pleasant vs. unpleasant stimuli was found (BA13). The unpleasant vs. neutral comparison on High-Load tasks (the pleasant condition had an insufficient sample size) revealed a specific lateralization of several areas of the right hemisphere (STG-BA 38, MFG-BA 46, FG-BA 37), whereas, in the other load conditions, the inferior frontal gyrus was right lateralized, but the main activated regions were bilateral or left lateralized. Results are discussed considering the effects of both valence and task/load variables, and the involvement of hippocampus/amygdala, hemispheric asymmetries of emotions, the occipito-temporal areas, several sub-regions of the prefrontal/orbitofrontal cortex, and an extended motor network.

Morality in Our Mind and Across Cultures and Politics

Moral judgments differ across cultures and politics, but they share a common theme in our minds: perceptions of harm. Both cultural ethnographies on moral values and psychological research on moral cognition highlight this shared focus on harm. Perceptions of harm are constructed from universal cognitive elements-including intention, causation, and suffering-but depend on the cultural context, allowing many values to arise from a common moral mind. This review traces the concept of harm across philosophy, cultural anthropology, and psychology, then discusses how different values (e.g., purity) across various taxonomies are grounded in perceived harm. We then explore two theories connecting culture to cognition-modularity and constructionism-before outlining how pluralism across human moral judgment is explained by the constructed nature of perceived harm. We conclude by showing how different perceptions of harm help drive political disagreements and reveal how sharing stories of harm can help bridge moral divides.

Emotion: An evolutionary model of lateralization in the human brain

Since several reviews have recently discussed the lateralization of emotions, this chapter will take into account the possible evolutionary meaning of this lateralization. The organization of the chapter will be based on the following steps. I will first propose that emotions must be considered as a complex adaptive system, complementary to the more phylogenetically advanced cognitive system. Second, I will remind historical aspects and consolidated results on the lateralization of emotions. Then I will discuss the phylogenetic aspects of the problem, trying to evaluate if emotional asymmetries concern only humans and some nonhuman primates or are part of a continuum between humans and many phylogenetically distant animal species. After having reviewed various aspects of emotional lateralization across different animal species and (more specifically) in nonhuman primates, I will propose a general model of hemispheric asymmetries in the human brain, based on theoretical models and empiric data. Theoretical models stem from the influence that the presence or the absence of language can have on concomitant hemispheric functions, whereas supporting neuropsychologic data have been gathered in patients with unilateral brain damage.

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.

Does Trauma Change the Way Individuals with Post-Traumatic Stress Disorder (PTSD) Deal with Positive Stimuli?

INTRODUCTION

Post-Traumatic Stress Disorder (PTSD) is a highly prevalent disorder and a highly debilitating condition. Although current theories focused on depressed mood and intrusion as critical dimensions, the mechanism through which depression increases the risk of PTSD remains unclear. Research usually concentrates on the hyperactive negative valence system (NVS) (e.g., increased fear and threat responses), but some evidence suggests a significant role for the hypoactive positive valence system (PVS) (e.g., less neural activation towards rewards).

METHOD

The main aim of the present research was to investigate whether probable PTSD leads to a different evaluation of the implicit processing in a refugee's sample. Ratings of arousal, dominance, and valence from 60 International Affective Picture System (IAPS) pictures (positive, neutral, and negative) were collected from 42 individuals with probable PTSD, and a group of 26 trauma-exposed individuals (Mage = 28.49 years, SD = ±7.78).

RESULTS

ANOVA results revealed a main group effect (η2p = 0.379) on arousal, dominance, valence dimensions, and pictures' categories (η2p = 0.620), confirming evidence according to which PTSD origins a state of maladaptive hyperarousal and troubles the regulation of emotions, and not supporting the view that such difficulties arise only with negative stimuli. Participants with probable PTSD deemed negative stimuli as more threatening than they really are, reacting to unpleasant images with greater negative emotionality (i.e., enhanced arousal and lower valence ratings) compared with individuals without PTSD. Moreover, they rated positive stimuli as less pleasant. Furthermore, arousal ratings were negatively correlated with valence (r = -0.709, p < 0.01) indicating that pictures with high arousal (negative) were associated with lower valence.

DISCUSSION

Our findings supported evidence according to which PTSD caused a constant state of hyperarousal and difficulties in regulating emotions facing environmental stimuli. Positive stimuli are considered less pleasant, and this inhibits from completely benefiting from them.

CONCLUSION

Our study provides evidence for a differential and potentially complementary involvement of NVS and PVS in PTSD development. Intervention for PTSD may, thus, target both negative and positive valence processing.

Alterations in large-scale resting-state network nodes following transcranial focused ultrasound of deep brain structures

Background

Low-intensity transcranial focused ultrasound (tFUS) is a brain stimulation approach that holds promise for the treatment of brain-based disorders. Studies in humans have shown that tFUS can successfully modulate perfusion in focal sonication targets, including the amygdala; however, limited research has explored how tFUS impacts large-scale neural networks.

Objective

The aim of the current study was to address this gap and examine changes in resting-state connectivity between large-scale network nodes using a randomized, double-blind, within-subjects crossover study design.

Methods

Healthy adults (n = 18) completed two tFUS sessions, 14 days apart. Each session included tFUS of either the right amygdala or the left entorhinal cortex (ErC). The inclusion of two active targets allowed for within-subjects comparisons as a function of the locus of sonication. Resting-state functional magnetic resonance imaging was collected before and after each tFUS session.

Results

tFUS altered resting-state functional connectivity (rsFC) within and between rs-network nodes. Pre-to-post sonication of the right amygdala modulated connectivity within nodes of the salience network (SAN) and between nodes of the SAN and the default mode network (DMN) and frontoparietal network (FRP). A decrease in SAN to FPN connectivity was specific to the amygdala target. Pre-to-post sonication of the left ErC modulated connectivity between the dorsal attention network (DAN) and FPN and DMN. An increase in DAN to DMN connectivity was specific to the ErC target.

Conclusion

These preliminary findings may suggest that tFUS induces neuroplastic changes beyond the immediate sonication target. Additional studies are needed to determine the long-term stability of these effects.

Functional brain connectivity changes associated with day-to-day fluctuations in affective states

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models' generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

Neural Predictors of Fear Depend on the Situation

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

Volition and control in law and in brain science: neurolegal translation of a foundational concept

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.

Understanding human amygdala function with artificial neural networks

The amygdala is a cluster of subcortical nuclei that receives diverse sensory inputs and projects to the cortex, midbrain and other subcortical structures. Numerous accounts of amygdalar contributions to social and emotional behavior have been offered, yet an overarching description of amygdala function remains elusive. Here we adopt a computationally explicit framework that aims to develop a model of amygdala function based on the types of sensory inputs it receives, rather than individual constructs such as threat, arousal, or valence. Characterizing human fMRI signal acquired as participants viewed a full-length film, we developed encoding models that predict both patterns of amygdala activity and self-reported valence evoked by naturalistic images. We use deep image synthesis to generate artificial stimuli that distinctly engage encoding models of amygdala subregions that systematically differ from one another in terms of their low-level visual properties. These findings characterize how the amygdala compresses high-dimensional sensory inputs into low-dimensional representations relevant for behavior.

More than labels: neural representations of emotion words are widely distributed across the brain

Although emotion words such as "anger," "disgust," "happiness," or "pride" are often thought of as mere labels, increasing evidence points to language as being important for emotion perception and experience. Emotion words may be particularly important for facilitating access to the emotion concepts. Indeed, deficits in semantic processing or impaired access to emotion words interfere with emotion perception. Yet, it is unclear what these behavioral findings mean for affective neuroscience. Thus, we examined the brain areas that support processing of emotion words using representational similarity analysis of functional magnetic resonance imaging data (N = 25). In the task, participants saw 10 emotion words (e.g. "anger," "happiness") while in the scanner. Participants rated each word based on its valence on a continuous scale ranging from 0 (Pleasant/Good) to 1 (Unpleasant/Bad) scale to ensure they were processing the words. Our results revealed that a diverse range of brain areas including prefrontal, midline cortical, and sensorimotor regions contained information about emotion words. Notably, our results overlapped with many regions implicated in decoding emotion experience by prior studies. Our results raise questions about what processes are being supported by these regions during emotion experience.

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study

Background

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC).

Methods

In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17).

Results

During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked.

Conclusion

Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression.

Limitations

The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts

While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.

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.

Brain waves and landscape settings: emotional responses to attractiveness

Neuro-architecture is a specific branch of architecture that studies how the physical environment can change our mental processes and influence our behaviors. One of the main purposes of this field is to use changes in brain activities as a measure to quantify attractiveness of the landscapes. In this study, we investigated how changes in elements of attractiveness influence ones' emotional perception and present the related pattern of changes in brain activities. Therefore, we implied five elements of attractiveness including mystery, visual openness, landscape or greenness, walkability, and social interaction using the Delphi method. Then, we made changes in each element separately to make the landscape more attractive and assessed their effects on a group of young adults. We used the self-assessment manikin questionnaire to measure the participants' emotional perception while the participants' brain activities were recorded using a 32-channel EEG while exposed to the landscape images. The results showed that changes in attractive elements of the landscape could significantly improve ones' emotional perception of the landscape. In addition, these changes are perceived by changing the oscillatory pattern of brain activities. We hope these findings could shed a light to use of neural markers in measurement of place attractiveness.

fMRI BOLD responses to film stimuli and their association with exhaled nitric oxide in asthma and health

Little is known about central nervous system (CNS) responses to emotional stimuli in asthma. Nitric oxide in exhaled breath (FENO) is elevated in asthma due to allergic immune processes, but endogenous nitric oxide is also known to modulate CNS activity. We measured fMRI blood oxygen-dependent (BOLD) brain activation to negative (blood-injection-injury themes) and neutral films in 31 participants (15 with asthma). Regions-of-interest analysis was performed on key areas relevant to central adaptive control, threat processing, or salience networks, with dorsolateral prefrontal cortex (PFC), anterior insula, dorsal anterior cingulate cortex (dACC), amygdala, ventral striatum, ventral tegmentum, and periaqueductal gray, as well as top-down modulation of emotion, with ventrolateral and ventromedial PFC. Both groups showed less BOLD deactivation from fixation cross-baseline in the left anterior insula and bilateral ventromedial PFC for negative than neutral films, and for an additional number of areas, including the fusiform gyrus, for film versus recovery phases. Less deactivation during films followed by less recovery from deactivation was found in asthma compared to healthy controls. Changes in PCO2 did not explain these findings. FENO was positively related to BOLD activation in general, but more pronounced in healthy controls and more likely in neutral film processing. Thus, asthma is associated with altered processing of film stimuli across brain regions not limited to central adaptive control, threat processing, or salience networks. Higher levels of NO appear to facilitate CNS activity, but only in healthy controls, possibly due to allergy's masking effects on FENO.

Review of EEG Affective Recognition with a Neuroscience Perspective

Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life-influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain's emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond.

Differential modulation of resting-state functional connectivity between amygdala and precuneus after acute physical exertion of varying intensity: indications for a role in affective regulation

Introduction

Physical activity influences psychological well-being. This study aimed to determine the impact of exercise intensity on psychological well-being and alterations in emotion-related brain functional connectivity (FC).

Methods

Twenty young, healthy, trained athletes performed a low- and high-intensity interval exercise (LIIE and HIIE) as well as a control condition in a within-subject crossover design. Before and after each condition, Positive And Negative Affect Scale (PANAS) was assessed as well as resting-state functional MRI (rs-fMRI). Voxel-wise FC was examined for bilateral amygdala seed region to whole-brain and emotion-related anatomical regions (e.g., insula, temporal pole, precuneus). Data analyses were performed using linear mixed-effect models with fixed factors condition and time.

Results

The PANAS Positive Affect scale showed a significant increase after LIIE and HIIE and a significant reduction in Negative Affect after the control condition. In rs-fMRI, no significant condition-by-time interactions were observed between the amygdala and whole brain. Amygdala-precuneus FC analysis showed an interaction effect, suggesting reduced post-exercise anticorrelation after the control condition, but stable, or even slightly enhanced anticorrelation for the exercise conditions, especially HIIE.

Discussion

In conclusion, both LIIE and HIIE had positive effects on mood and concomitant effects on amygdala-precuneus FC, particularly after HIIE. Although no significant correlations were found between amygdala-precuneus FC and PANAS, results should be discussed in the context of affective disorders in whom abnormal amygdala-precuneus FC has been observed.

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.

Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger

The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.

"Amity Seizures": A previously unreported semiology localizing to a circuit between the right hippocampus and orbitofrontal area

We describe a case of focal epilepsy with a semiology consisting of behaviors indicating an enthusiastic desire for those around him to get along and engage in friendly relations, which we refer to as "amity seizures". The patient was a 41-year-old right-handed male with seizures since age 26. Semiology consisted of stereotyped enthusiastic behaviors such as expressing "Peace! Peace!… Come on, we all on the same team, right?!", and giving hugs, kisses, and high-fives to those around him. On SEEG evaluation, 2 independent areas of seizure onset were identified, the right hippocampus and right posterior orbitofrontal area. Locally confined seizures had bland manifestation. However, spread from right hippocampus to right orbitofrontal area, or vice versa, elicited his typical amity seizure semiology. To our knowledge this is the first report of the seizure semiology we have coined "Amity seizures". While emotions were once thought to localize to discrete brain regions, they are now accepted to arise from networks across multiple brain regions. The fact that this behavior only occurred when seizures spread from either of 2 onset zones to the other suggests that this semiology results from network engagement between, and likely beyond, either onset zone.

Brain imaging studies of emotional well-being: a scoping review

This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors.

Systematic review registration

https://osf.io/t9cf6/.

Basic Emotions or Constructed Emotions: Insights From Taking an Evolutionary Perspective

The ongoing debate between basic emotion theories (BETs) and the theory of constructed emotion (TCE) hampers progress in the field of emotion research. Providing a new perspective, here we aim to bring the theories closer together by dissecting them according to Tinbergen's four questions to clarify a focus on their evolutionary basis. On the basis of our review of the literature, we conclude that whereas BETs focus on the evolution question of Tinbergen, the TCE is more concerned with the causation of emotion. On the survival value of emotions both theories largely agree: to provide the best reaction in specific situations. Evidence is converging on the evolutionary history of emotions but is still limited for both theories-research within both frameworks focuses heavily on the causation. We conclude that BETs and the TCE explain two different phenomena: emotion and feeling. Therefore, they seem irreconcilable but possibly supplementary for explaining and investigating the evolution of emotion-especially considering their similar answer to the question of survival value. Last, this article further highlights the importance of carefully describing what aspect of emotion is being discussed or studied. Only then can evidence be interpreted to converge toward explaining emotion.

Automatic Brain Categorization of Discrete Auditory Emotion Expressions

Seamlessly extracting emotional information from voices is crucial for efficient interpersonal communication. However, it remains unclear how the brain categorizes vocal expressions of emotion beyond the processing of their acoustic features. In our study, we developed a new approach combining electroencephalographic recordings (EEG) in humans with a frequency-tagging paradigm to 'tag' automatic neural responses to specific categories of emotion expressions. Participants were presented with a periodic stream of heterogeneous non-verbal emotional vocalizations belonging to five emotion categories: anger, disgust, fear, happiness and sadness at 2.5 Hz (stimuli length of 350 ms with a 50 ms silent gap between stimuli). Importantly, unknown to the participant, a specific emotion category appeared at a target presentation rate of 0.83 Hz that would elicit an additional response in the EEG spectrum only if the brain discriminates the target emotion category from other emotion categories and generalizes across heterogeneous exemplars of the target emotion category. Stimuli were matched across emotion categories for harmonicity-to-noise ratio, spectral center of gravity and pitch. Additionally, participants were presented with a scrambled version of the stimuli with identical spectral content and periodicity but disrupted intelligibility. Both types of sequences had comparable envelopes and early auditory peripheral processing computed via the simulation of the cochlear response. We observed that in addition to the responses at the general presentation frequency (2.5 Hz) in both intact and scrambled sequences, a greater peak in the EEG spectrum at the target emotion presentation rate (0.83 Hz) and its harmonics emerged in the intact sequence in comparison to the scrambled sequence. The greater response at the target frequency in the intact sequence, together with our stimuli matching procedure, suggest that the categorical brain response elicited by a specific emotion is at least partially independent from the low-level acoustic features of the sounds. Moreover, responses at the fearful and happy vocalizations presentation rates elicited different topographies and different temporal dynamics, suggesting that different discrete emotions are represented differently in the brain. Our paradigm revealed the brain's ability to automatically categorize non-verbal vocal emotion expressions objectively (at a predefined frequency of interest), behavior-free, rapidly (in few minutes of recording time) and robustly (with a high signal-to-noise ratio), making it a useful tool to study vocal emotion processing and auditory categorization in general and in populations where behavioral assessments are more challenging.

Topographic representation of visually evoked emotional experiences in the human cerebral cortex

Affective neuroscience seeks to uncover the neural underpinnings of emotions that humans experience. However, it remains unclear whether an affective space underlies the discrete emotion categories in the human brain, and how it relates to the hypothesized affective dimensions. To address this question, we developed a voxel-wise encoding model to investigate the cortical organization of human emotions. Results revealed that the distributed emotion representations are constructed through a fundamental affective space. We further compared each dimension of this space to 14 hypothesized affective dimensions, and found that many affective dimensions are captured by the fundamental affective space. Our results suggest that emotional experiences are represented by broadly spatial overlapping cortical patterns and form smooth gradients across large areas of the cortex. This finding reveals the specific structure of the affective space and its relationship to hypothesized affective dimensions, while highlighting the distributed nature of emotional representations in the cortex.

Cortical haemodynamic responses predict individual ability to recognise vocal emotions with uninformative pitch cues but do not distinguish different emotions

We investigated the cortical representation of emotional prosody in normal-hearing listeners using functional near-infrared spectroscopy (fNIRS) and behavioural assessments. Consistent with previous reports, listeners relied most heavily on F0 cues when recognizing emotion cues; performance was relatively poor-and highly variable between listeners-when only intensity and speech-rate cues were available. Using fNIRS to image cortical activity to speech utterances containing natural and reduced prosodic cues, we found right superior temporal gyrus (STG) to be most sensitive to emotional prosody, but no emotion-specific cortical activations, suggesting that while fNIRS might be suited to investigating cortical mechanisms supporting speech processing it is less suited to investigating cortical haemodynamic responses to individual vocal emotions. Manipulating emotional speech to render F0 cues less informative, we found the amplitude of the haemodynamic response in right STG to be significantly correlated with listeners' abilities to recognise vocal emotions with uninformative F0 cues. Specifically, listeners more able to assign emotions to speech with degraded F0 cues showed lower haemodynamic responses to these degraded signals. This suggests a potential objective measure of behavioural sensitivity to vocal emotions that might benefit neurodiverse populations less sensitive to emotional prosody or hearing-impaired listeners, many of whom rely on listening technologies such as hearing aids and cochlear implants-neither of which restore, and often further degrade, the F0 cues essential to parsing emotional prosody conveyed in speech.

On the beauty of sadness: it's okay to say, I am sad, thank you

We live in times when our culture is obsessed with happiness. The value of almost every aspect of our lives is increasingly judged in terms of their contribution to our happiness. Happiness has become the ultimate goal by which values and priorities are constructed and the only thing for which any action in pursuit of does not require justification. In contrast, sadness is increasingly abnormalized and pathologized. In this paper, an effort is made to counteract the narrative that sadness, a critical aspect of human life is abnormal or a pathological condition. The evolutionary benefits of sadness and its place in human flourishing are discussed. A rebranding of sadness is proposed that emphasizes the free expression of sadness in everyday greetings to remove it from its current negative state and promote many of its benefits including post-traumatic growth and resilience.

Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm

Our emotions and sentiments are influenced by naturalistic stimuli such as the movies we watch and the songs we listen to, accompanied by changes in our brain activation. Comprehension of these brain-activation dynamics can assist in identification of any associated neurological condition such as stress and depression, leading towards making informed decision about suitable stimuli. A large number of open-access functional magnetic resonance imaging (fMRI) datasets collected under naturalistic conditions can be used for classification/prediction studies. However, these datasets do not provide emotion/sentiment labels, which limits their use in supervised learning studies. Manual labeling by subjects can generate these labels, however, this method is subjective and biased. In this study, we are proposing another approach of generating automatic labels from the naturalistic stimulus itself. We are using sentiment analyzers (VADER, TextBlob, and Flair) from natural language processing to generate labels using movie subtitles. Subtitles generated labels are used as the class labels for positive, negative, and neutral sentiments for classification of brain fMRI images. Support vector machine, random forest, decision tree, and deep neural network classifiers are used. We are getting reasonably good classification accuracy (42-84%) for imbalanced data, which is increased (55-99%) for balanced data.

"The many faces of sorrow": An empirical exploration of the psychological plurality of sadness

Sadness has typically been associated with failure, defeat and loss, but it has also been suggested that sadness facilitates positive and restructuring emotional changes. This suggests that sadness is a multi-faceted emotion. This supports the idea that there might in fact be different facets of sadness that can be distinguished psychologically and physiologically. In the current set of studies, we explored this hypothesis. In a first stage, participants were asked to select sad emotional faces and scene stimuli either characterized or not by a key suggested sadness-related characteristic: loneliness or melancholy or misery or bereavement or despair. In a second stage, another set of participants was presented with the selected emotional faces and scene stimuli. They were assessed for differences in emotional, physiological and facial-expressive responses. The results showed that sad faces involving melancholy, misery, bereavement and despair were experienced as conferring dissociable physiological characteristics. Critical findings, in a final exploratory design, in a third stage, showed that a new set of participants could match emotional scenes to emotional faces with the same sadness-related characteristic with close to perfect precision performance. These findings suggest that melancholy, misery, bereavement and despair can be distinguishable emotional states associated with sadness.

The EEG microstate representation of discrete emotions

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.

Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

Mixed emotions to social situations: An fMRI investigation

BACKGROUND

Neuroscience research has generally studied emotions each taken in isolation. However, mixed emotional states (e.g., the co-occurrence of amusement and disgust, or sadness and pleasure) are common in everyday life. Psychophysiological and behavioral evidence suggests that mixed emotions may have response profiles that are distinguishable from their constituent emotions. Yet, the brain bases of mixed emotions remain unresolved.

METHODS

We recruited 38 healthy adults who viewed short, validated film clips, eliciting either positive (amusing), negative (disgusting), neutral, or mixed (a mix of amusement and disgust) emotional states, while brain activity was assessed by functional magnetic resonance imaging (fMRI). We assessed mixed emotions in two ways: first by comparing neural reactivity to ambiguous (mixed) with that to unambiguous (positive and negative) film clips and second by conducting parametric analyses to measure neural reactivity with respect to individual emotional states. We thus obtained self-reports of amusement and disgust after each clip and computed a minimum feeling score (shared minimum of amusement and disgust) to quantify mixed emotional feelings.

RESULTS

Both analyses revealed a network of the posterior cingulate (PCC), medial superior parietal lobe (SPL)/precuneus, and parieto-occipital sulcus to be involved in ambiguous contexts eliciting mixed emotions.

CONCLUSION

Our results are the first to shed light on the dedicated neural processes involved in dynamic social ambiguity processing. They suggest both higher-order (SPL) and lower-order (PCC) processes may be needed to process emotionally complex social scenes.

Automatic and controlled attentional orienting toward emotional faces in patients with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative motor disorder that can associate with deficits in cognitive and emotional processing. In particular, PD has been reported to be mainly associated with defects in executive control and orienting attentional systems. The deficit in emotional processing mainly emerged in facial expression recognition. It is possible that the defects in emotional processing in PD may be secondary to other cognitive impairments, such as attentional deficits. This study was designed to systematically investigate the different weight of automatic and controlled attentional orienting mechanisms implied in emotional selective attention in PD. To address our purpose, we assessed drug-naïve PD patients and age-matched healthy controls with two dot-probe tasks that differed for stimuli duration. Automatic and controlled attentions were evaluated with stimuli lasting 100 ms and 500 ms, respectively. Furthermore, we introduced an emotion recognition task to investigate the performance in explicit emotion classification. The stimuli used in both the tasks dot-probe and emotion recognition were expressive faces displaying neutral, disgusted, fearful, and happy expressions.Our results showed that in PD patients, compared with healthy controls, there was 1) an alteration of automatic and controlled attentional orienting toward emotional faces in both the dot-probe tasks (with short and long durations), and 2) no difference in the emotion recognition task. These findings suggest that, from the early stages of the disease, PD can yield specific deficits in implicit emotion processing task (i.e., dot-probe task) despite a normal performance in explicit tasks that demand overt emotion recognition.

The Sound of Emotion: Pinpointing Emotional Voice Processing Via Frequency Tagging EEG

Successfully engaging in social communication requires efficient processing of subtle socio-communicative cues. Voices convey a wealth of social information, such as gender, identity, and the emotional state of the speaker. We tested whether our brain can systematically and automatically differentiate and track a periodic stream of emotional utterances among a series of neutral vocal utterances. We recorded frequency-tagged EEG responses of 20 neurotypical male adults while presenting streams of neutral utterances at a 4 Hz base rate, interleaved with emotional utterances every third stimulus, hence at a 1.333 Hz oddball frequency. Four emotions (happy, sad, angry, and fear) were presented as different conditions in different streams. To control the impact of low-level acoustic cues, we maximized variability among the stimuli and included a control condition with scrambled utterances. This scrambling preserves low-level acoustic characteristics but ensures that the emotional character is no longer recognizable. Results revealed significant oddball EEG responses for all conditions, indicating that every emotion category can be discriminated from the neutral stimuli, and every emotional oddball response was significantly higher than the response for the scrambled utterances. These findings demonstrate that emotion discrimination is fast, automatic, and is not merely driven by low-level perceptual features. Eventually, here, we present a new database for vocal emotion research with short emotional utterances (EVID) together with an innovative frequency-tagging EEG paradigm for implicit vocal emotion discrimination.

Emotional processing in patients with single brain damage in the right hemisphere

BACKGROUND

The interest in the relationship between brain damage and social cognition has increased in recent years. The objectives of the present study were the following: (1) to evaluate and compare emotional facial recognition and subjective emotional experience in patients who have suffered a single ischemic stroke in the right hemisphere (RH) and in healthy people, (2) to analyze the relationship between both variables in both groups of subjects, and (3) to analyze the association between the cerebral location of the stroke and these two variables.

METHODS

Emotional facial recognition and the subjective emotional experience of 41 patients who had suffered a single ischemic stroke in the RH and 45 volunteers without previous cerebrovascular pathology were evaluated.

RESULTS

Brain damaged patients performed lower in facial emotional recognition and had a less intense subjective emotional response to social content stimuli compared to healthy subjects. Likewise, among patients with RH ischemic stroke, we observed negative associations between facial recognition of surprise and reactivity to unpleasant images, and positive associations between recognition of disgust and reactivity to pleasant images. Finally, patients with damage in the caudate nucleus of the RH presented a deficit in the recognition of happiness and sadness, and those with damage in the frontal lobe exhibited a deficit in the recognition of surprise, compared to those injured in other brain areas.

CONCLUSIONS

Emotional facial recognition and subjective emotional experience are affected in patients who have suffered a single ischemic stroke in the RH. Professionals caring for stroke patients should improve their understanding of the general condition of affected persons and their environment, assess for risk of depression, and facilitate their adaptation to work, family, and social environments.

Dysfunctional cerebello-cerebral network associated with vocal emotion recognition impairments

Vocal emotion recognition, a key determinant to analyzing a speaker's emotional state, is known to be impaired following cerebellar dysfunctions. Nevertheless, its possible functional integration in the large-scale brain network subtending emotional prosody recognition has yet to be explored. We administered an emotional prosody recognition task to patients with right versus left-hemispheric cerebellar lesions and a group of matched controls. We explored the lesional correlates of vocal emotion recognition in patients through a network-based analysis by combining a neuropsychological approach for lesion mapping with normative brain connectome data. Results revealed impaired recognition among patients for neutral or negative prosody, with poorer sadness recognition performances by patients with right cerebellar lesion. Network-based lesion-symptom mapping revealed that sadness recognition performances were linked to a network connecting the cerebellum with left frontal, temporal, and parietal cortices. Moreover, when focusing solely on a subgroup of patients with right cerebellar damage, sadness recognition performances were associated with a more restricted network connecting the cerebellum to the left parietal lobe. As the left hemisphere is known to be crucial for the processing of short segmental information, these results suggest that a corticocerebellar network operates on a fine temporal scale during vocal emotion decoding.

Emotions as computations

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others' (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

The neurobiological basis of affect is consistent with psychological construction theory and shares a common neural basis across emotional categories

Affective experience colours everyday perception and cognition, yet its fundamental and neurobiological basis is poorly understood. The current debate essentially centers around the communalities and specificities across individuals, events, and emotional categories like anger, sadness, and happiness. Using fMRI during the experience of these emotions, we critically compare the two dominant conflicting theories on human affect. Basic emotion theory posits emotions as discrete universal entities generated by dedicated emotion category-specific neural circuits, while psychological construction theory claims emotional events as unique, idiosyncratic, and constructed by psychological primitives like core affect and conceptualization, which underlie each emotional event and operate in a predictive framework. Based on the findings of 8 a priori-defined model-specific prediction tests on the neural response amplitudes and patterns, we conclude that the neurobiological basis of affect is primarily characterized by idiosyncratic mechanisms and a common neural basis shared across emotion categories, consistent with psychological construction theory. The findings provide further insight into the organizational principles of the neural basis of affect and brain function in general. Future studies in clinical populations with affective symptoms may reveal the corresponding underlying neural changes from a psychological construction perspective.

Frequency dependent emotion differentiation and directional coupling in amygdala, orbitofrontal and medial prefrontal cortex network with intracranial recordings

The amygdala, orbitofrontal cortex (OFC) and medial prefrontal cortex (mPFC) form a crucial part of the emotion circuit, yet their emotion induced responses and interactions have been poorly investigated with direct intracranial recordings. Such high-fidelity signals can uncover precise spectral dynamics and frequency differences in valence processing allowing novel insights on neuromodulation. Here, leveraging the unique spatio-temporal advantages of intracranial electroencephalography (iEEG) from a cohort of 35 patients with intractable epilepsy (with 71 contacts in amygdala, 31 in OFC and 43 in mPFC), we assessed the spectral dynamics and interactions between the amygdala, OFC and mPFC during an emotional picture viewing task. Task induced activity showed greater broadband gamma activity in the negative condition compared to positive condition in all the three regions. Similarly, beta activity was increased in the negative condition in the amygdala and OFC while decreased in mPFC. Furthermore, beta activity of amygdala showed significant negative association with valence ratings. Critically, model-based computational analyses revealed unidirectional connectivity from mPFC to the amygdala and bidirectional communication between OFC-amygdala and OFC-mPFC. Our findings provide direct neurophysiological evidence for a much-posited model of top-down influence of mPFC over amygdala and a bidirectional influence between OFC and the amygdala. Altogether, in a relatively large sample size with human intracranial neuronal recordings, we highlight valence-dependent spectral dynamics and dyadic coupling within the amygdala-mPFC-OFC network with implications for potential targeted neuromodulation in emotion processing.

Bridging the Gap: Human Emotions and Animal Emotions

Our experiences of the conscious mental states that we call emotions drive our interest in whether such states also exist in other animals. Because linguistic report can be used as a gold standard (albeit indirect) indicator of subjective emotional feelings in humans but not other species, how can we investigate animal emotions and what exactly do we mean when we use this term? Linguistic reports of human emotion give rise to emotion concepts (discrete emotions; dimensional models), associated objectively measurable behavioral and bodily emotion indicators, and understanding of the emotion contexts that generate specific states. We argue that many animal studies implicitly translate human emotion concepts, indicators and contexts, but that explicit consideration of the underlying pathways of inference, their theoretical basis, assumptions, and pitfalls, and how they relate to conscious emotional feelings, is needed to provide greater clarity and less confusion in the conceptualization and scientific study of animal emotion.

Decoding six basic emotions from brain functional connectivity patterns

Although distinctive neural and physiological states are suggested to underlie the six basic emotions, basic emotions are often indistinguishable from functional magnetic resonance imaging (fMRI) voxelwise activation (VA) patterns. Here, we hypothesize that functional connectivity (FC) patterns across brain regions may contain emotion-representation information beyond VA patterns. We collected whole-brain fMRI data while human participants viewed pictures of faces expressing one of the six basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) or showing neutral expressions. We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space. Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions. An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified. Finally, we demonstrated that within the same brain regions, FC-based decoding consistently performed better than VA-based decoding. Taken together, our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.

Activation network mapping for integration of heterogeneous fMRI findings

Functional neuroimaging techniques have been widely used to probe the neural substrates of facial emotion processing in healthy people. However, findings are largely inconsistent across studies. Here, we introduce a new technique termed activation network mapping to examine whether heterogeneous functional magnetic resonance imaging findings localize to a common network for emotion processing. First, using the existing method of activation likelihood estimation meta-analysis, we showed that individual-brain-based reproducibility was low across studies. Second, using activation network mapping, we found that network-based reproducibility across these same studies was higher. Validation analysis indicated that the activation network mapping-localized network aligned with stimulation sites, structural abnormalities and brain lesions that disrupt facial emotion processing. Finally, we verified the generality of the activation network mapping technique by applying it to another cognitive process, that is, rumination. Activation network mapping may potentially be broadly applicable to localize brain networks of cognitive functions.

Personal insult disrupts regulatory brain networks in violent offenders

The failure to adequately regulate negative emotions represents a prominent characteristic of violent offenders. In this functional magnetic resonance imaging study, we used technical, nonsocial frustration to elicit anger in violent offenders (n = 19) and then increased the provocation by adding personal insults (social provocation). The aim was to investigate neural connectivity patterns involved in anger processing, to detect the effect of increasing provocation by personal insult, and to compare anger-related connectivity patterns between offenders and noncriminal controls (n = 12). During technical frustration, the offenders showed increased neural connectivity between the amygdala and prefrontal cortex compared to the controls. Conversely, personal insults, and thus increased levels of provocation, resulted in a significant reduction of neural connectivity between regions involved in cognitive control in the offenders but not controls. We conclude that, when (nonsocially) frustrated, offenders were able to employ regulatory brain networks by displaying stronger connectivity between regulatory prefrontal and limbic regions than noncriminal controls. In addition, offenders seemed particularly sensitive to personal insults, which led to increased implicit aggression (by means of motoric responses) and reduced connectivity in networks involved in cognitive control (including dorsomedial prefrontal cortex, precuneus, middle/superior temporal regions).

Are the concepts of emotion special? A comparison between basic-emotion, secondary-emotion, abstract, and concrete words

The study of emotional concepts stands at a very interesting intersection between the theoretical debate about the nature of emotions and the debate about the nature of processing concrete concepts and abstract concepts. On the one hand, it is debated whether it is possible to differentiate basic emotions from secondary emotions and, on the other hand, whether emotional concepts differ from abstract concepts. In this regard, the prototypical perceptual aspects are considered an important factor both for the differentiation between concrete and abstract concepts and for the differentiation between basic and secondary emotions (facial expressions). Thus, the objective has been to determine if (a) the presence or absence of a prototypical perceptual referent, and (b) the type of concept (referring to emotion and not referring to emotion), produce differences between concepts of basic emotions, secondary emotions and concepts not related to emotions, concrete and abstract, in the tasks of qualification of concreteness, imageability and availability of context and the task of the list of properties, that have been used in previous studies. A total of 86 university students from the suburbs of La Serena - Coquimbo (Chile), all native Spanish speakers, participated in the study. The results show that in the perception of concreteness and in the total of enumerated properties, emotional concepts presented similar results to abstract concepts not related to emotion and there was no difference between basic and secondary emotion concepts. In imageability and context availability, emotional concepts were perceived as different from and more concrete than abstract concepts. In addition, the cause-effect type attributes allowed to clearly differentiate emotional concepts from those not related to emotion and to differentiate between basic and secondary emotion concepts. These types of attributes appear almost exclusively in emotional concepts and are more frequent in basic emotions. These results are partially consistent with the predictions of Neurocultural and Conceptual Act theories about emotions.

Using the theory of constructed emotion to inform the study of cognition-emotion interactions

In this article I suggest how theories of emotion construction may inform the study of cognition-emotion interactions. To do so, I adopt the two main concepts core affect and emotions as categories: Core affect, one's current affective state, which is defined by the two dimensions pleasure and arousal, is an inherent part of any conscious experience. Specific emotions are understood as categories including highly diverse exemplars. I argue that (1) affective states can and should not be differentiated from cognitive states, and that (2) specific emotions may follow the same principles as other biological or more general categories. I review some empirical evidence in support of these ideas and show avenues for future research.