Emotional expressions evoke a differential response in the fusiform face area

Differential dopamine function in fibromyalgia

Approximately 30 % of Americans suffer from chronic pain disorders, such as fibromyalgia (FM), which can cause debilitating pain. Many pain-killing drugs prescribed for chronic pain disorders are highly addictive, have limited clinical efficacy, and do not treat the cognitive symptoms reported by many patients. The neurobiological substrates of chronic pain are largely unknown, but evidence points to altered dopaminergic transmission in aberrant pain perception. We sought to characterize the dopamine (DA) system in individuals with FM. Positron emission tomography (PET) with [(18)F]fallypride (FAL) was used to assess changes in DA during a working memory challenge relative to a baseline task, and to test for associations between baseline D2/D3 availability and experimental pain measures. Twelve female subjects with FM and 11 female controls completed study procedures. Subjects received one FAL PET scan while performing a "2-back" task, and one while performing a "0-back" (attentional control, "baseline") task. FM subjects had lower baseline FAL binding potential (BP) in several cortical regions relative to controls, including anterior cingulate cortex. In FM subjects, self-reported spontaneous pain negatively correlated with FAL BP in the left orbitofrontal cortex and parahippocampal gyrus. Baseline BP was significantly negatively correlated with experimental pain sensitivity and tolerance in both FM and CON subjects, although spatial patterns of these associations differed between groups. The data suggest that abnormal DA function may be associated with differential processing of pain perception in FM. Further studies are needed to explore the functional significance of DA in nociception and cognitive processing in chronic pain.

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain.

Decoding facial expressions based on face-selective and motion-sensitive areas

Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc.

Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli

Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.

Somatosensory Representations Link the Perception of Emotional Expressions and Sensory Experience

Studies of human emotion perception have linked a distributed set of brain regions to the recognition of emotion in facial, vocal, and body expressions. In particular, lesions to somatosensory cortex in the right hemisphere have been shown to impair recognition of facial and vocal expressions of emotion. Although these findings suggest that somatosensory cortex represents body states associated with distinct emotions, such as a furrowed brow or gaping jaw, functional evidence directly linking somatosensory activity and subjective experience during emotion perception is critically lacking. Using functional magnetic resonance imaging and multivariate decoding techniques, we show that perceiving vocal and facial expressions of emotion yields hemodynamic activity in right somatosensory cortex that discriminates among emotion categories, exhibits somatotopic organization, and tracks self-reported sensory experience. The findings both support embodied accounts of emotion and provide mechanistic insight into how emotional expressions are capable of biasing subjective experience in those who perceive them.

Face-selective regions differ in their ability to classify facial expressions

Recognition of facial expressions is crucial for effective social interactions. Yet, the extent to which the various face-selective regions in the human brain classify different facial expressions remains unclear. We used functional magnetic resonance imaging (fMRI) and support vector machine pattern classification analysis to determine how well face-selective brain regions are able to decode different categories of facial expression. Subjects participated in a slow event-related fMRI experiment in which they were shown 32 face pictures, portraying four different expressions: neutral, fearful, angry, and happy and belonging to eight different identities. Our results showed that only the amygdala and the posterior superior temporal sulcus (STS) were able to accurately discriminate between these expressions, albeit in different ways: the amygdala discriminated fearful faces from non-fearful faces, whereas STS discriminated neutral from emotional (fearful, angry and happy) faces. In contrast to these findings on the classification of emotional expression, only the fusiform face area (FFA) and anterior inferior temporal cortex (aIT) could discriminate among the various facial identities. Further, the amygdala and STS were better than FFA and aIT at classifying expression, while FFA and aIT were better than the amygdala and STS at classifying identity. Taken together, our findings indicate that the decoding of facial emotion and facial identity occurs in different neural substrates: the amygdala and STS for the former and FFA and aIT for the latter.

Task influences pattern discriminability for faces and bodies in ventral occipitotemporal cortex

Our prior research showed that faces and bodies activate overlapping regions of the ventral occipitotemporal cortex (VOTC). However, faces and bodies were nonetheless discriminable in these same overlapping regions when their spatial patterns of activity were classified using multivoxel pattern analysis (MVPA). Here we investigated whether these spatial patterns and their time courses were influenced by different categorization tasks. Participants viewed pictures of faces or headless bodies depicting a happy or fearful emotion. In one task, they categorized the picture as a face or a body regardless of emotion. In the other task, they categorized the emotion regardless of whether it was depicted by a face or body. Using a classifier trained on independent data, we found higher face-body classification accuracy for the emotion categorization task. The classifier was applied to each post-stimulus time-point to characterize the temporal course of classification. Accuracy initially rose equivalently above chance for both tasks, but then increased over a longer duration when participants categorized emotions. Thus, the temporal course of pattern differences between faces and bodies in VOTC was modulated by the behavioral goal of the observer, suggesting the top-down modulatory effect of task context on the category-selectivity activity in the VOTC.

Decoding negative affect personality trait from patterns of brain activation to threat stimuli

INTRODUCTION

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample.

METHODS

fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli.

RESULTS

The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions).

CONCLUSION

These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts.

Lesion Mapping the Four-Factor Structure of Emotional Intelligence

Emotional intelligence (EI) refers to an individual’s ability to process and respond to emotions, including recognizing the expression of emotions in others, using emotions to enhance thought and decision making, and regulating emotions to drive effective behaviors. Despite their importance for goal-directed social behavior, little is known about the neural mechanisms underlying specific facets of EI. Here, we report findings from a study investigating the neural bases of these specific components for EI in a sample of 130 combat veterans with penetrating traumatic brain injury. We examined the neural mechanisms underlying experiential (perceiving and using emotional information) and strategic (understanding and managing emotions) facets of EI. Factor scores were submitted to voxel-based lesion symptom mapping to elucidate their neural substrates. The results indicate that two facets of EI (perceiving and managing emotions) engage common and distinctive neural systems, with shared dependence on the social knowledge network, and selective engagement of the orbitofrontal and parietal cortex for strategic aspects of emotional information processing. The observed pattern of findings suggests that sub-facets of experiential and strategic EI can be characterized as separable but related processes that depend upon a core network of brain structures within frontal, temporal and parietal cortex.

Neural representations of emotion are organized around abstract event features

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal.

Investigating the brain basis of facial expression perception using multi-voxel pattern analysis

Humans can readily decode emotion expressions from faces and perceive them in a categorical manner. The model by Haxby and colleagues proposes a number of different brain regions with each taking over specific roles in face processing. One key question is how these regions directly compare to one another in successfully discriminating between various emotional facial expressions. To address this issue, we compared the predictive accuracy of all key regions from the Haxby model using multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. Regions of interest were extracted using independent meta-analytical data. Participants viewed four classes of facial expressions (happy, angry, fearful and neutral) in an event-related fMRI design, while performing an orthogonal gender recognition task. Activity in all regions allowed for robust above-chance predictions. When directly comparing the regions to one another, fusiform gyrus and superior temporal sulcus (STS) showed highest accuracies. These results underscore the role of the fusiform gyrus as a key region in perception of facial expressions, alongside STS. The study suggests the need for further specification of the relative role of the various brain areas involved in the perception of facial expression. Face processing appears to rely on more interactive and functionally overlapping neural mechanisms than previously conceptualised.

Face recognition in schizophrenia disorder: A comprehensive review of behavioral, neuroimaging and neurophysiological studies

Facial emotion processing has been extensively studied in schizophrenia patients while general face processing has received less attention. The already published reviews do not address the current scientific literature in a complete manner. Therefore, here we tried to answer some questions that remain to be clarified, particularly: are the non-emotional aspects of facial processing in fact impaired in schizophrenia patients? At the behavioral level, our key conclusions are that visual perception deficit in schizophrenia patients: are not specific to faces; are most often present when the cognitive (e.g. attention) and perceptual demands of the tasks are important; and seems to worsen with the illness chronification. Although, currently evidence suggests impaired second order configural processing, more studies are necessary to determine whether or not holistic processing is impaired in schizophrenia patients. Neural and neurophysiological evidence suggests impaired earlier levels of visual processing, which might involve the deficits in interaction of the magnocellular and parvocellular pathways impacting on further processing. These deficits seem to be present even before the disorder out-set. Although evidence suggests that this deficit may be not specific to faces, further evidence on this question is necessary, in particularly more ecological studies including context and body processing.

A common neural code for perceived and inferred emotion

Although the emotions of other people can often be perceived from overt reactions (e.g., facial or vocal expressions), they can also be inferred from situational information in the absence of observable expressions. How does the human brain make use of these diverse forms of evidence to generate a common representation of a target's emotional state? In the present research, we identify neural patterns that correspond to emotions inferred from contextual information and find that these patterns generalize across different cues from which an emotion can be attributed. Specifically, we use functional neuroimaging to measure neural responses to dynamic facial expressions with positive and negative valence and to short animations in which the valence of a character's emotion could be identified only from the situation. Using multivoxel pattern analysis, we test for regions that contain information about the target's emotional state, identifying representations specific to a single stimulus type and representations that generalize across stimulus types. In regions of medial prefrontal cortex (MPFC), a classifier trained to discriminate emotional valence for one stimulus (e.g., animated situations) could successfully discriminate valence for the remaining stimulus (e.g., facial expressions), indicating a representation of valence that abstracts away from perceptual features and generalizes across different forms of evidence. Moreover, in a subregion of MPFC, this neural representation generalized to trials involving subjectively experienced emotional events, suggesting partial overlap in neural responses to attributed and experienced emotions. These data provide a step toward understanding how the brain transforms stimulus-bound inputs into abstract representations of emotion.