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

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.

Exploring Affective Representations in Emotional Narratives: An Exploratory Study Comparing ChatGPT and Human Responses

While artificial Intelligence (AI) has made significant advancements, the seeming absence of its emotional ability has hindered effective communication with humans. This study explores how ChatGPT (ChatGPT-3.5 Mar 23, 2023 Version) represents affective responses to emotional narratives and compare these responses to human responses. Thirty-four participants read affect-eliciting short stories and rated their emotional responses and 10 recorded ChatGPT sessions generated responses to the stories. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification accuracies predicting affective categories of stories, valence, and arousal of humans based on the affective ratings of ChatGPT and vice versa were not significant, indicating differences in the way the affective states were represented., indicating differences in the way the affective states were represented. These findings suggested that ChatGPT can distinguish emotional states and generate affective responses consistently, but there are differences in how the affective states are represented between ChatGPT and humans. Understanding these mechanisms is crucial for improving emotional interactions with AI.

Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling

In this study, we explored whether the Korean version of Singer Loomis Type Deployment Inventory II (K-SLTDI) captures the opposing tendencies of Jung's theory of psychological type. The types are Extroverted Sensing, Extroverted Intuition, Extroverted Feeling, Extroverted Thinking, Introverted Sensing, Introverted Intuition, Introverted Feeling, and Introverted Thinking. A nationwide online survey was conducted in South Korea. We performed multidimensional scaling and classification analyses based on 521 Korean adult profiles with eight psychological types to test the bipolarity assumption. The results showed that the Procrustes-rotated four-dimensional space successfully represented four types of opposing tendencies. Moreover, the bipolarity assumption in the four dimensions of Jungian typology was tested and compared between lower and higher psychological distress populations via cluster analysis. Lastly, we explored patterns of responses in lower and higher psychological distress populations using intersubject correlation. Both similarity analyses and classification results consistently support the theoretical considerations on the conceptualization of Jung's type in independent order that the types could be derived without bipolar assumption as Singer and Loomis expected in their Type Development Inventory. Limitations in our study include the sample being randomly selected internet users during the COVID-19 pandemic, despite excellence in the use of the internet in the general Korean population.

A normative model of brain responses to social scenarios reflects the maturity of children and adolescents' social-emotional abilities

The rapid brain maturation in childhood and adolescence accompanies the development of socio-emotional functioning. However, it is unclear how the maturation of the neural activity drives the development of socio-emotional functioning and individual differences. This study aimed to reflect the age dependence of inter-individual differences in brain responses to socio-emotional scenarios and to develop naturalistic imaging indicators to assess the maturity of socio-emotional ability at the individual level. Using three independent naturalistic imaging datasets containing healthy participants (n = 111, 21 and 122), we found and validated that age-modulated inter-individual concordance of brain responses to socio-emotional movies in specific brain regions. The similarity of an individual's brain response to the average response of older participants was defined as response typicality, which predicted an individual's emotion regulation strategies in adolescence and theory of mind (ToM) in childhood. Its predictive power was not superseded by age, sex, cognitive performance or executive function. We further showed that the movie's valence and arousal ratings grounded the response typicality. The findings highlight that forming typical brain response patterns may be a neural phenotype underlying the maturation of socio-emotional ability. The proposed response typicality represents a neuroimaging approach to measure individuals' maturity of cognitive reappraisal and ToM.

Test-retest reliability of dynamic functional connectivity in naturalistic paradigm functional magnetic resonance imaging

Dynamic functional connectivity (dFC) has been increasingly used to characterize the brain transient temporal functional patterns and their alterations in diseased brains. Meanwhile, naturalistic neuroimaging paradigms have been an emerging approach for cognitive neuroscience with high ecological validity. However, the test–retest reliability of dFC in naturalistic paradigm neuroimaging is largely unknown. To address this issue, we examined the test–retest reliability of dFC in functional magnetic resonance imaging (fMRI) under natural viewing condition. The intraclass correlation coefficients (ICC) of four dFC statistics including standard deviation (Std), coefficient of variation (COV), amplitude of low frequency fluctuation (ALFF), and excursion (Excursion) were used to measure the test–retest reliability. The test–retest reliability of dFC in naturalistic viewing condition was then compared with that under resting state. Our experimental results showed that: (a) Global test–retest reliability of dFC was much lower than that of static functional connectivity (sFC) in both resting‐state and naturalistic viewing conditions; (b) Both global and local (including visual, limbic and default mode networks) test–retest reliability of dFC could be significantly improved in naturalistic viewing condition compared to that in resting state; (c) There existed strong negative correlation between sFC and dFC, weak negative correlation between dFC and dFC‐ICC (i.e., ICC of dFC), as well as weak positive correlation between dFC‐ICC and sFC‐ICC (i.e., ICC of sFC). The present study provides novel evidence for the promotion of naturalistic paradigm fMRI in functional brain network studies.

Predictive processing models and affective neuroscience

The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.

Negative content enhances stimulus-specific cerebral activity during free viewing of pictures, faces, and words

Negative visual stimuli have been found to elicit stronger brain activation than do neutral stimuli. Such emotion effects have been shown for pictures, faces, and words alike, but the literature suggests stimulus-specific differences regarding locus and lateralization of the activity. In the current functional magnetic resonance imaging study, we directly compared brain responses to passively viewed negative and neutral pictures of complex scenes, faces, and words (nouns) in 43 healthy participants (21 males) varying in age and demographic background. Both negative pictures and faces activated the extrastriate visual cortices of both hemispheres more strongly than neutral ones, but effects were larger and extended more dorsally for pictures, whereas negative faces additionally activated the superior temporal sulci. Negative words differentially activated typical higher-level language processing areas such as the left inferior frontal and angular gyrus. There were small emotion effects in the amygdala for faces and words, which were both lateralized to the left hemisphere. Although pictures elicited overall the strongest amygdala activity, amygdala response to negative pictures was not significantly stronger than to neutral ones. Across stimulus types, emotion effects converged in the left anterior insula. No gender effects were apparent, but age had a small, stimulus-specific impact on emotion processing. Our study specifies similarities and differences in effects of negative emotional content on the processing of different types of stimuli, indicating that brain response to negative stimuli is specifically enhanced in areas involved in processing of the respective stimulus type in general and converges across stimuli in the left anterior insula.

An fMRI Study of Affective Congruence across Visual and Auditory Modalities

Evaluating multisensory emotional content is a part of normal day-to-day interactions. We used fMRI to examine brain areas sensitive to congruence of audiovisual valence and their overlap with areas sensitive to valence. Twenty-one participants watched audiovisual clips with either congruent or incongruent valence across visual and auditory modalities. We showed that affective congruence versus incongruence across visual and auditory modalities is identifiable on a trial-by-trial basis across participants. Representations of affective congruence were widely distributed with some overlap with the areas sensitive to valence. Regions of overlap included bilateral superior temporal cortex and right pregenual anterior cingulate. The overlap between the regions identified here and in the emotion congruence literature lends support to the idea that valence may be a key determinant of affective congruence processing across a variety of discrete emotions.

Distinct neural mechanisms underlying conceptual knowledge of manner and instrument verbs

Studies on the organization of conceptual knowledge have examined categories of concrete nouns extensively. Less is known about the neural basis of verb categories suggested by linguistic theories. We used functional MRI to examine the differences between manner verbs, which encode information about the manner of an action, versus instrument verbs, which encode information about an object as part of their meaning. Using both visual and verbal stimuli and a combination of univariate and multivariate pattern analyses, our results show that accessing conceptual representations of instrument class involves brain regions typically associated with complex action and object perception, including the anterior inferior parietal cortex and occipito-temporal cortex. On the other hand, accessing conceptual representations of the manner class involves regions that are commonly associated with the processing of visual and biological motion, in the posterior superior temporal sulcus. These findings support the idea that the semantics of manner and instrument verbs are supported by distinct neural mechanisms.

fMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Abstract Representations of Emotions Perceived From the Face, Body, and Whole-Person Expressions in the Left Postcentral Gyrus

Emotions can be perceived through the face, body, and whole-person, while previous studies on the abstract representations of emotions only focused on the emotions of the face and body. It remains unclear whether emotions can be represented at an abstract level regardless of all three sensory cues in specific brain regions. In this study, we used the representational similarity analysis (RSA) to explore the hypothesis that the emotion category is independent of all three stimulus types and can be decoded based on the activity patterns elicited by different emotions. Functional magnetic resonance imaging (fMRI) data were collected when participants classified emotions (angry, fearful, and happy) expressed by videos of faces, bodies, and whole-persons. An abstract emotion model was defined to estimate the neural representational structure in the whole-brain RSA, which assumed that the neural patterns were significantly correlated in within-emotion conditions ignoring the stimulus types but uncorrelated in between-emotion conditions. A neural representational dissimilarity matrix (RDM) for each voxel was then compared to the abstract emotion model to examine whether specific clusters could identify the abstract representation of emotions that generalized across stimulus types. The significantly positive correlations between neural RDMs and models suggested that the abstract representation of emotions could be successfully captured by the representational space of specific clusters. The whole-brain RSA revealed an emotion-specific but stimulus category-independent neural representation in the left postcentral gyrus, left inferior parietal lobe (IPL) and right superior temporal sulcus (STS). Further cluster-based MVPA revealed that only the left postcentral gyrus could successfully distinguish three types of emotions for the two stimulus type pairs (face-body and body-whole person) and happy versus angry/fearful, which could be considered as positive versus negative for three stimulus type pairs, when the cross-modal classification analysis was performed. Our study suggested that abstract representations of three emotions (angry, fearful, and happy) could extend from the face and body stimuli to whole-person stimuli and the findings of this study provide support for abstract representations of emotions in the left postcentral gyrus.