The neural basis of emotions varies over time: different regions go with onset- and offset-bound processes underlying emotion intensity

Measuring Continuous Affect in Daily Life With Intensity Profile Drawings

We examined continuous affect drawings as innovative measure of affective experiences over time. Intensive longitudinal data often rely on discrete assessments, containing "blind spots" between measurements. With continuous affect drawings participants visually depict their affect fluctuations between assessments. In an experience sampling study, participants (N = 115) rated their momentary positive and negative affect 6 times daily. From the second daily rating on, they additionally drew their positive and negative affect changes and reported affective events between assessments. They received one measurement burst between assessments daily. The strength of the approach is a substantial amount of informational gain (average 7%) over linearly interpolated points between assessments. The additional information was subsequently categorized into positive and negative affect peaks and valleys, each occurring once a day per person on average. The probability of detecting peaks and valleys increased with reported events. The drawings correlated positively with momentary affect scores from the burst. Yet, the drawing predicted the bursts less well suggesting that the momentary ratings may yield different information than the drawings. Although the timing of retrospective drawings is less precise than individual momentary assessments, this method provides a comprehensive understanding of affective experiences between assessments, offering a unique perspective on affect dynamics.

Feelings as a currency of care: A role for agreeableness in emotional reactivity

OBJECTIVE AND BACKGROUND

The personality trait of agreeableness is linked to a number of core tendencies (e.g., empathy, warmth) that operate in a feeling-based manner. Following considerations of this type, it is proposed that the motivations and characteristics of agreeable individuals, relative to disagreeable individuals, should render them more receptive to emotional events and more responsive to them for this reason.

METHOD

Potential links between agreeableness and emotional reactivity were assessed in two studies involving four samples (total N = 517) in which participants continuously rated their feeling states in response to a variety of affective images.

RESULTS

Agreeableness did not predict the speed with which emotional reactions began, but agreeable individuals exhibited higher-magnitude peak intensities, regardless of whether stimuli were appetitive (pleasant) or aversive (unpleasant) in nature.

CONCLUSIONS

The findings provide novel insights into the personality trait of agreeableness, emotional reactivity phenomena, and the dynamic processes that link agreeableness to emotion.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

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.

[Multidimensional and computational theory of mood]

What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.

Default and control network connectivity dynamics track the stream of affect at multiple timescales

In everyday life, the stream of affect results from the interaction between past experiences, expectations and the unfolding of events. How the brain represents the relationship between time and affect has been hardly explored, as it requires modeling the complexity of everyday life in the laboratory setting. Movies condense into hours a multitude of emotional responses, synchronized across subjects and characterized by temporal dynamics alike real-world experiences. Here, we use time-varying intersubject brain synchronization and real-time behavioral reports to test whether connectivity dynamics track changes in affect during movie watching. The results show that polarity and intensity of experiences relate to the connectivity of the default mode and control networks and converge in the right temporoparietal cortex. We validate these results in two experiments including four independent samples, two movies and alternative analysis workflows. Finally, we reveal chronotopic connectivity maps within the temporoparietal and prefrontal cortex, where adjacent areas preferentially encode affect at specific timescales.

[Psychiatry without mind?]

Philosophy of Mind is currently one of the most prolific fields of research in philosophy and has witnessed a progressive hybridization with cognitive science. It focuses on fundamental questions to neuroscience and psychiatry, such as the nature of mental states and cognitive processes, or the relationships between mental states and the world. Anticipating the accumulation of experimental data from neuroscience, it provides a framework for the generation of theories in cognitive science. Philosophy of mind has thus laid the foundations of the conceptual space within which cognitive sciences have spread: a large part of contemporary theories in cognitive science result from a hybridization of conceptions forged by philosophers of mind and data produced by neuroscientists. Yet contemporary psychiatry is still reluctant to feed on the philosophy of mind, other than through the fragments that emerge from neuroscience. In this paper, we describe the evolution of contemporary philosophy of mind, and we detail its contributions around three central themes for psychiatry: naturalization of mind, mental causality, and subjectivity of mental states. We show how philosophy of mind provide the conceptual framework to link different levels of explanation in psychiatry: from biological to functional, from neurophysiology to cognition, from matter to mind.

Neural Responses During Emotion Transitions and Emotion Regulation

Why are some people more susceptible to interference from previous emotional stimuli? Neural mechanisms underlying emotion regulation are typically studied with one-off positive or negative stimuli. Less is known about how they operate during dynamic emotional experiences, which more closely resemble how emotions occur in real life. Therefore, we investigated the interaction among temporal context, stimulus content, and regulatory strategy. Image sequences included either neutral to negative emotion or negative to neutral emotion. Participants were instructed to either passively watch the emotional stimuli or apply cognitive reappraisal during the image sequences presentation. Participants also reported their habitual use of cognitive reappraisal in their daily lives on a standard scale. We measured functional connectivity (FC) with electroencephalography (EEG) source localization. A three-way interaction suggested that, in addition to momentary emotional content and regulatory effort, the temporal context of stimuli impacts the FC between the ventromedial prefrontal cortex (vmPFC) and the ventral anterior cingulate cortex (ACC) in both alpha and beta frequency bands. In the reappraisal condition-but not the passive watch conditions-, individual differences in habitual reappraisal were manifested in the FC of vmPFC-ACC in alpha band. Emotion transitions may be more demanding because prefrontal-posterior FC in the beta band decreased during emotion transitions regardless of emotional content or regulation efforts. Flexible emotion regulation enables the recruiting of neural activities in response to the content of dynamic, ever-changing experiences encountered in daily life. Studying brain responses to dynamic emotional stimuli may shed light on individual differences in adaptation and psychological health. It also provides a more ecologically valid assessment of emotion regulation.

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak–end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.

State and trait characteristics of anterior insula time-varying functional connectivity

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.

Non-Coding RNAs in Psychiatric Disorders and Suicidal Behavior

It is well known that only a small proportion of the human genome code for proteins; the rest belong to the family of RNAs that do not code for protein and are known as non-coding RNAs (ncRNAs). ncRNAs are further divided into two subclasses based on size: 1) long non-coding RNAs (lncRNAs; >200 nucleotides) and 2) small RNAs (<200 nucleotides). Small RNAs contain various family members that include microRNAs (miRNAs), small interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), and small nuclear RNAs (snRNAs). The roles of ncRNAs, especially lncRNAs and miRNAs, are well documented in brain development, homeostasis, stress responses, and neural plasticity. It has also been reported that ncRNAs can influence the development of psychiatric disorders including schizophrenia, major depressive disorder, and bipolar disorder. More recently, their roles are being investigated in suicidal behavior. In this article, we have comprehensively reviewed the findings of lncRNA and miRNA expression changes and their functions in various psychiatric disorders including suicidal behavior. We primarily focused on studies that have been done in postmortem human brain. In addition, we have briefly reviewed the role of other small RNAs (e.g. piwiRNA, siRNA, snRNA, and snoRNAs) and their expression changes in psychiatric illnesses.

Personality traits and negative affect mediate the relationship between cortical thickness of superior frontal cortex and aggressive behavior

Aggression reflects the psychological and physical behavior that perpetrator intends to harm victim. Initiation of aggression is influenced by the distal factors (e.g. personality) and proximate causes (e.g. affect) of perpetrator. However, few studies explored the brain structural basis of relationship between these traits and aggressive behavior. In this study, we first explored the association between cortical thickness and aggression in a large young adult sample from the Human Connectome Project. Results found aggressive behavior assessed by the Adult Self-Report was positively correlated with cortical thickness in left superior frontal gyrus (SFG), which was implicated in emotion regulation and executive function. Then, mediation analyses with distal and proximate factors separately showcased that the association between the left SFG thickness and aggressive behavior was partially mediated by negative affect (anger and sadness), and fully mediated by personality traits (agreeableness and neuroticism). Taken together, these experimental findings established dorsal prefrontal cortex as the key region in generating aggressive behavior, and gave a neutral explanation for why individuals with high negative affect and neuroticism exhibit more aggression. This study implicated the possible targeted brain region and behavioral intervention for such at-risk individuals initiating violence.

The impact of self-distancing on emotion explosiveness and accumulation: An fMRI study

Emotions unfold over time with episodes differing in explosiveness (i.e., profiles having a steep vs. a gentle start) and accumulation (i.e., profiles increasing over time vs. going back to baseline). In the present fMRI study, we wanted to replicate and extend previous findings on the psychological and neural mechanisms underlying emotion explosiveness and accumulation. Specifically, we aimed to: (a) replicate the finding that different neural mechanisms are associated with emotion explosiveness and accumulation, (b) replicate the finding that adopting a self-distanced (vs. self-immersed) perspective decreases emotion explosiveness and accumulation at the level of self-report, and (c) examine whether adopting a self-distanced (vs. self-immersed) perspective similarly modulates activity in the brain regions associated with emotion explosiveness and accumulation. Participants in an fMRI scanner were asked to adopt a self-immersed or self-distanced perspective while reading and thinking about negative social feedback, and to report on felt changes in negative affect during that period using an emotion intensity profile tracking approach. We replicated previous findings showing that emotion explosiveness and accumulation were related to activity in regions involved in self-referential processing (such as the medial prefrontal cortex) and sustained visceral arousal (such as the posterior insula), respectively. The finding that adopting a self-distanced (vs. self-immersed) perspective lowers emotion explosiveness and accumulation was also replicated at a self-report level. However, perspective taking did not impact activity in the neural correlates of emotion explosiveness and accumulation.

An Appraisal-Driven Componential Approach to the Emotional Brain

This article suggests that methodological and conceptual advancements in affective sciences militate in favor of adopting an appraisal-driven componential approach to further investigate the emotional brain. Here we propose to operationalize this approach by distinguishing five functional networks of the emotional brain: (a) the elicitation network, (b) the expression network, (c) the autonomic reaction network, (d) the action tendency network, and (e) the feeling network, and discuss these networks in the context of the affective neuroscience literature. We also propose that further investigating the “appraising brain” is the royal road to better understand the elicitation network, and may be key to revealing the neural causal mechanisms underlying the emotion process as a whole.