Neurobiological correlates of emotional intelligence in voice and face perception networks

Neuroticism and openness exhibit an anti-correlation pattern to dissociable default mode network: using resting connectivity and structural equation modeling analysis

The default mode network (DMN) can be subdivided into ventral and dorsal subsystems, which serve affective cognition and mental sense construction, respectively. An internally dissociated pattern of anti-correlations was observed between these two subsystems. Although numerous studies on neuroticism and openness have demonstrated the neurological functions of the DMN, little is known about whether different subsystems and hubs regions within the network are engaged in different functions in response to the two traits. We recruited 223 healthy volunteers in this study and collected their resting-state functional magnetic resonance imaging (fMRI) and NEO Five-Factor Inventory scores. We used independent component analysis (ICA) to obtain the DMN, before further decomposing it into the ventral and dorsal subsystems. Then, the network coherence of hubs regions within subsystems was extracted to construct two structural equation models (SEM) to explore the relationship between neuroticism and openness traits and DMN. We observed that the ventral DMN could significantly predict positive openness and negative neuroticism. The dorsal DMN was diametrically opposed. Additionally, the medial prefrontal cortex (mPFC) and middle temporal gyrus (MTG), both of which are core hubs of the subnetworks within the DMN, are significantly positively correlated with neuroticism and openness. These findings may point to a biological basis that neuroticism and openness are engaged in opposite mechanisms and support the hypothesis about the functional dissociation of the DMN.

Modality-specific brain representations during automatic processing of face, voice and body expressions

A central question in affective science and one that is relevant for its clinical applications is how emotions provided by different stimuli are experienced and represented in the brain. Following the traditional view emotional signals are recognized with the help of emotion concepts that are typically used in descriptions of mental states and emotional experiences, irrespective of the sensory modality. This perspective motivated the search for abstract representations of emotions in the brain, shared across variations in stimulus type (face, body, voice) and sensory origin (visual, auditory). On the other hand, emotion signals like for example an aggressive gesture, trigger rapid automatic behavioral responses and this may take place before or independently of full abstract representation of the emotion. This pleads in favor specific emotion signals that may trigger rapid adaptative behavior only by mobilizing modality and stimulus specific brain representations without relying on higher order abstract emotion categories. To test this hypothesis, we presented participants with naturalistic dynamic emotion expressions of the face, the whole body, or the voice in a functional magnetic resonance (fMRI) study. To focus on automatic emotion processing and sidestep explicit concept-based emotion recognition, participants performed an unrelated target detection task presented in a different sensory modality than the stimulus. By using multivariate analyses to assess neural activity patterns in response to the different stimulus types, we reveal a stimulus category and modality specific brain organization of affective signals. Our findings are consistent with the notion that under ecological conditions emotion expressions of the face, body and voice may have different functional roles in triggering rapid adaptive behavior, even if when viewed from an abstract conceptual vantage point, they may all exemplify the same emotion. This has implications for a neuroethologically grounded emotion research program that should start from detailed behavioral observations of how face, body, and voice expressions function in naturalistic contexts.

Research on social attention in autism and the challenges of the research domain criteria (RDoC) framework

The fuzzy nature of categories of psychopathology, such as autism, leads to significant research challenges. Alternatively, focusing research on the study of a common set of important and well-defined psychological constructs across psychiatric conditions may make the fundamental etiological processes of psychopathology easier to discern and treat (Cuthbert, 2022). The development of the research domain criteria (RDoC) framework is designed to guide this new research approach (Insel et al., 2010). However, progress in research may be expected to continually refine and reorganize the understanding of the specifics of these mental processes (Cuthbert & Insel, 2013). Moreover, knowledge gleaned from the study of both normative and atypical development can be mutually informative in the evolution of our understanding of these fundamental processes. A case in point is the study of social attention. This Autism 101 commentary provides an educational summary of research over the last few decades indicates that social attention is major construct in the study of human social-cognitive development, autism and other forms of psychopathology. The commentary also describes how this research can inform the Social Process dimension of the RDoC framework.

Psychopathic and autistic traits differentially influence the neural mechanisms of social cognition from communication signals

Psychopathy is associated with severe deviations in social behavior and cognition. While previous research described such cognitive and neural alterations in the processing of rather specific social information from human expressions, some open questions remain concerning central and differential neurocognitive deficits underlying psychopathic behavior. Here we investigated three rather unexplored factors to explain these deficits, first, by assessing psychopathy subtypes in social cognition, second, by investigating the discrimination of social communication sounds (speech, non-speech) from other non-social sounds, and third, by determining the neural overlap in social cognition impairments with autistic traits, given potential common deficits in the processing of communicative voice signals. The study was exploratory with a focus on how psychopathic and autistic traits differentially influence the function of social cognitive and affective brain networks in response to social voice stimuli. We used a parametric data analysis approach from a sample of 113 participants (47 male, 66 female) with ages ranging between 18 and 40 years (mean 25.59, SD 4.79). Our data revealed four important findings. First, we found a phenotypical overlap between secondary but not primary psychopathy with autistic traits. Second, primary psychopathy showed various neural deficits in neural voice processing nodes (speech, non-speech voices) and in brain systems for social cognition (mirroring, mentalizing, empathy, emotional contagion). Primary psychopathy also showed deficits in the basal ganglia (BG) system that seems specific to the social decoding of communicative voice signals. Third, neural deviations in secondary psychopathy were restricted to social mirroring and mentalizing impairments, but with additional and so far undescribed deficits at the level of auditory sensory processing, potentially concerning deficits in ventral auditory stream mechanisms (auditory object identification). Fourth, high autistic traits also revealed neural deviations in sensory cortices, but rather in the dorsal auditory processing streams (communicative context encoding). Taken together, social cognition of voice signals shows considerable deviations in psychopathy, with differential and newly described deficits in the BG system in primary psychopathy and at the neural level of sensory processing in secondary psychopathy. These deficits seem especially triggered during the social cognition from vocal communication signals.

Neural correlates of perceived emotions in human insula and amygdala for auditory emotion recognition

The emotional status of a speaker is an important non-linguistic cue carried by human voice and can be perceived by a listener in vocal communication. Understanding the neural circuits involved in processing emotions carried by human voice is crucial for understanding the neural basis of social interaction. Previous studies have shown that human insula and amygdala responded more selectively to emotional sounds than non-emotional sounds. However, it is not clear whether the neural selectivity to emotional sounds in these brain structures is determined by the emotion presented by a speaker which is associated with the acoustic properties of the sounds or by the emotion perceived by a listener. In this study, we recorded intracranial electroencephalography (iEEG) responses to emotional human voices while subjects performed emotion recognition tasks. We found that the iEEG responses of Heschl's gyrus (HG) and posterior insula were determined by the presented emotion, whereas the iEEG responses of anterior insula and amygdala were driven by the perceived emotion. These results suggest that the anterior insula and amygdala play a crucial role in conscious perception of emotions carried by human voice.

Correlates of individual voice and face preferential responses during resting state

Human nonverbal social signals are transmitted to a large extent by vocal and facial cues. The prominent importance of these cues is reflected in specialized cerebral regions which preferentially respond to these stimuli, e.g. the temporal voice area (TVA) for human voices and the fusiform face area (FFA) for human faces. But it remained up to date unknown whether there are respective specializations during resting state, i.e. in the absence of any cues, and if so, whether these representations share neural substrates across sensory modalities. In the present study, resting state functional connectivity (RSFC) as well as voice- and face-preferential activations were analysed from functional magnetic resonance imaging (fMRI) data sets of 60 healthy individuals. Data analysis comprised seed-based analyses using the TVA and FFA as regions of interest (ROIs) as well as multi voxel pattern analyses (MVPA). Using the face- and voice-preferential responses of the FFA and TVA as regressors, we identified several correlating clusters during resting state spread across frontal, temporal, parietal and occipital regions. Using these regions as seeds, characteristic and distinct network patterns were apparent with a predominantly convergent pattern for the bilateral TVAs whereas a largely divergent pattern was observed for the bilateral FFAs. One region in the anterior medial frontal cortex displayed a maximum of supramodal convergence of informative connectivity patterns reflecting voice- and face-preferential responses of both TVAs and the right FFA, pointing to shared neural resources in supramodal voice and face processing. The association of individual voice- and face-preferential neural activity with resting state connectivity patterns may support the perspective of a network function of the brain beyond an activation of specialized regions.

Reading Emotions in Faces With and Without Masks Is Relatively Independent of Extended Exposure and Individual Difference Variables

The ability to read emotions in faces helps humans efficiently assess social situations. We tested how this ability is affected by aspects of familiarization with face masks and personality, with a focus on emotional intelligence (measured with an ability test, the MSCEIT, and a self-report scale, the SREIS). To address aspects of the current pandemic situation, we used photos of not only faces per se but also of faces that were partially covered with face masks. The sample (N = 49), the size of which was determined by an a priori power test, was recruited in Germany and consisted of healthy individuals of different ages [M = 24.8 (18-64) years]. Participants assessed the emotional expressions displayed by six different faces determined by a 2 (sex) × 3 (age group: young, medium, and old) design. Each person was presented with six different emotional displays (angry, disgusted, fearful, happy, neutral, and sad) with or without a face mask. Accuracy and confidence were lower with masks-in particular for the emotion disgust (very often misinterpreted as anger) but also for happiness, anger, and sadness. When comparing the present data collected in July 2021 with data from a different sample collected in May 2020, when people first started to familiarize themselves with face masks in Western countries during the first wave of the COVID-19 pandemic, we did not detect an improvement in performance. There were no effects of participants' emotional intelligence, sex, or age regarding their accuracy in assessing emotional states in faces for unmasked or masked faces.

Emotional intelligence mediates the association between middle temporal gyrus gray matter volume and social anxiety in late adolescence

As a common mental health problem, social anxiety refers to the fear and avoidance of interacting in social or performance situations, which plays a crucial role in many health and social problems. Although a growing body of studies has explored the neuroanatomical alterations related to social anxiety in clinical patients, far fewer have examined the association between social anxiety and brain morphology in the general population, which may help us understand the neural underpinnings of social anxiety more comprehensively. Here, utilizing a voxel-based morphometry approach via structural magnetic resonance imaging, we investigated brain gray matter correlates of social anxiety in 231 recent graduates of the same high school grade. We found that social anxiety was positively associated with gray matter volume in the right middle temporal gyrus (MTG), which is a core brain area for cognitive processing of emotions and feelings. Critically, emotional intelligence mediated the impact of right MTG volume on social anxiety. Notably, our results persisted even when controlling for the effects of general anxiety and depression. Altogether, our research reveals right MTG gray matter volume as a neurostructural correlate of social anxiety in a general sample of adolescents and suggests a potential indirect effect of emotional intelligence on the association between gray matter volume and social anxiety.

Dynamic human and avatar facial expressions elicit differential brain responses

Computer-generated characters, so-called avatars, are widely used in advertising, entertainment, human–computer interaction or as research tools to investigate human emotion perception. However, brain responses to avatar and human faces have scarcely been studied to date. As such, it remains unclear whether dynamic facial expressions of avatars evoke different brain responses than dynamic facial expressions of humans. In this study, we designed anthropomorphic avatars animated with motion tracking and tested whether the human brain processes fearful and neutral expressions in human and avatar faces differently. Our fMRI results showed that fearful human expressions evoked stronger responses than fearful avatar expressions in the ventral anterior and posterior cingulate gyrus, the anterior insula, the anterior and posterior superior temporal sulcus, and the inferior frontal gyrus. Fearful expressions in human and avatar faces evoked similar responses in the amygdala. We did not find different responses to neutral human and avatar expressions. Our results highlight differences, but also similarities in the processing of fearful human expressions and fearful avatar expressions even if they are designed to be highly anthropomorphic and animated with motion tracking. This has important consequences for research using dynamic avatars, especially when processes are investigated that involve cortical and subcortical regions.

Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

Background: Improved conversational fluency is sometimes identified postoperatively in patients with epilepsy, but improvements can be difficult to assess using tests such as the intelligence quotient (IQ) test. Evaluation of pre- and postoperative differences might be considered subjective at present because of the lack of objective criteria. Artificial intelligence (AI) could possibly be used to make the evaluations more objective. The aim of this case report is thus to analyze the speech of a young female patient with epilepsy before and after surgery. Method: The speech of a nine-year-old girl with epilepsy secondary to tuberous sclerosis complex is recorded during interviews one month before and two months after surgery. The recorded speech is then manually transcribed and annotated, and subsequently automatically analyzed using AI software. IQ testing is also conducted on both occasions. The patient remains seizure-free for at least 13 months postoperatively. Results: There are decreases in total interview time and subjective case markers per second, whereas there are increases in morphemes and objective case markers per second. Postoperatively, IQ scores improve, except for the Perceptual Reasoning Index. Conclusions: AI analysis is able to identify differences in speech before and after epilepsy surgery upon an epilepsy patient with tuberous sclerosis complex.

A Neuroimaging Study of Personality Traits and Self-Reflection

This study examines the blood-oxygen level dependent (BOLD) activation of the brain associated with the four distinctive thinking styles associated with the four personality orientations of the Gountas Personality Orientations (GPO) survey: Emotion/Feeling-Action, Material/Pragmatic, Intuitive/Imaginative, and Thinking/Logical. The theoretical postulation is that each of the four personality orientations has a dominant (primary) thinking style and a shadow (secondary) thinking style/trait. The participants (N = 40) were initially surveyed to determine their dominant (primary) and secondary thinking styles. Based on participant responses, equal numbers of each dominant thinking style were selected for neuroimaging using a unique fMRI cognitive activation paradigm. The neuroimaging data support the general theoretical hypothesis of the existence of four different BOLD activation patterns, associated with each of the four thinking styles. The fMRI data analysis suggests that each thinking style may have its own cognitive activation system, involving the frontal ventromedial, posterior medial, parietal, motor, and orbitofrontal cortex. The data also suggest that there is a left hemisphere relationship for the Material/Pragmatic and Thinking/Logical styles and a right activation relationship for Emotional/Feeling and Intuitive/Imaginative styles. Additionally, the unique self-reflection paradigm demonstrated that perception of self or self-image, may be influenced by personality type; a finding of potentially far-reaching implications.