Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

A network approach to emotion regulation and symptom activation in depression and anxiety

Background

Emotions can be regulated through several regulatory strategies that are involved in the development of psychopathological symptoms. Despite the well-established association between psychopathology and emotion dysregulation, little is known about the relationship between individual symptoms of depression and anxiety and emotion regulation strategies (ERS), as well as between ERS themselves.

Method

We conducted a cross-sectional study and examined the interactions between six ERS (reappraisal, engagement, rumination, suppression, arousal control, and distraction) and assessed their distinctive association with the activation of specific symptoms of depression and anxiety in a community sample of 376 adults (80.4% female; Mage  = 32.70; SDage  = 11.80). The Regulation Emotion Systems Survey (RESS) was used to measure ERS. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) were used to assess psychological symptoms. An exploratory graph analysis was performed to examine the structural properties of the network of interactions between these behaviors. Additionally, to test the association of ERS with the activation of the depression symptoms network, an expected symptoms activity (ESA) was conducted.

Results

Six communities were found that correspond to the six ERS. Rumination and suppression have a significant association with symptom activation (particularly low self-esteem), whereas reappraisal reduces symptomatic activation. The effect of arousal control, engagement, and distraction appears to depend on the remaining ERS rather than having much influence on their own.

Conclusion

This study provides insight into how ERS interact with each other and with individual symptoms of depression and anxiety. Understanding the effects of these interactions on symptom activation and comorbidity can improve our understanding of psychopathology.

Sex differences in entrapment in a multinational sample: a network analysis perspective

Background

The concept of entrapment has been highlighted as a transdiagnostic element that manifests itself in disorders such as depression, anxiety, and suicidal ideation. Although research has been conducted in different contexts independently, a comprehensive multi-country study to assess gender differences in entrapment through network analysis has not yet been carried out. The objective of this study was to evaluate the entrapment network in men and women at the multinational level.

Methods

A sample of 2,949 participants, ranging in age from 18 to 73 years from six countries (Germany, Iran, Spain, Slovakia, El Salvador, and Peru), was considered. They completed the entrapment scale. A network analysis was performed for both men and women to identify the connectivity between indicators and the formation of clusters and domains, in addition to the centrality assessment in both sex groups.

Results

The study findings revealed the presence of a third domain focused on external interpersonal entrapment in the network of men and women. However, in relation to the interconnectivity between domains, variations were evidenced in both networks, as well as in centrality, it was reported that men present a greater generalized entrapment in various aspects of life, while women tend to experience a more focused entrapment in expressions of intense emotional charge.

Conclusion

The multinational study identified variations in the structure of entrapment between genders, with three domains (internal, external, and external-interpersonal) and differences in the interaction of indicators and groupings, as well as discrepancies in centrality.

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

The health crisis caused by COVID-19 in the United Kingdom and the confinement measures that were subsequently implemented had unprecedented effects on the mental health of older adults, leading to the emergence and exacerbation of different comorbid symptoms including depression and anxiety. This study examined and compared depression and anxiety symptom networks in two specific quarantine periods (June-July and November-December) in the older adult population in the United Kingdom. We used the database of the English Longitudinal Study of Aging COVID-19 Substudy, consisting of 5797 participants in the first stage (54% women) and 6512 participants in the second stage (56% women), all over 50 years of age. The symptoms with the highest centrality in both times were: "Nervousness (A1)" and "Inability to relax (A4)" in expected influence and predictability, and "depressed mood (D1"; bridging expected influence). The latter measure along with "Irritability (A6)" overlapped in both depression and anxiety clusters in both networks. In addition, a the cross-lagged panel network model was examined in which a more significant influence on the direction of the symptom "Nervousness (A1)" by the depressive symptoms of "Anhedonia (D6)", "Hopelessness (D7)", and "Sleep problems (D3)" was observed; the latter measure has the highest predictive capability of the network. The results report which symptoms had a higher degree of centrality and transdiagnostic overlap in the cross-sectional networks (invariants) and the cross-lagged panel network model of anxious and depressive symptomatology.

Improving the Walktrap Algorithm Using K-Means Clustering

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

The inner workings of anger: A network analysis of anger and emotion regulation

OBJECTIVE

This study aimed to investigate the interrelations between emotion regulation strategies and different types of anger using network analysis.

METHOD

Data were drawn from a cross-sectional sample of 538 adults (55% females; mean age = 39.8 years, SD = 12.3) seeking treatment for anger. Data were collected between March and November 2019 in Sweden. Participants completed measures of anger problems (anger expression, anger suppression, angry reactions, anger rumination, trait anger, hostility, physical aggression, and verbal aggression) and emotion regulation (cognitive reappraisal, expressive suppression, anger relaxation, and five mindfulness strategies). To determine whether distinct clusters of anger nodes would emerge, exploratory graph analysis was employed. Based on clustering of nodes, we estimated separate networks including all measures of emotion regulation.

RESULTS

Two clusters emerged: one consisting primarily of cognitive components of anger, and another of behavioral. Across networks, anger nodes were strongly interconnected, and anger rumination and anger suppression were especially influential. Several direct links were found between specific emotion regulation strategies and cognitive components of anger, whereas most strategies were only indirectly related to angry behavior. Cognitive reappraisal showed no direct link with any of the anger nodes.

CONCLUSIONS

Our findings reveal potential pathways by which different emotion regulation strategies may influence different types of anger, which could serve as therapeutic targets.

Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Embedding Research on Emotion Duration in a Network Model

Contrary to early theorizing, emotions often last for longer periods of time. Variability in people's emotion duration contributes to psychopathologies. Therefore, emotion theories need to account for this variability. So far, reviews only list predictors of emotion duration without integrating them in a theoretical framework. Mechanisms explaining why these predictors relate to emotion duration remain unknown. I propose to embed research on emotion duration in a network model of emotions and illustrate the central ideas with simulations using a formal network model. In the network model, the components of an emotion have direct causal effects on each other. According to the model, emotions last longer (a) when the components are more strongly connected or (b) when the components have higher thresholds (i.e., they are more easily activated). High connectivity prolongs emotions because components are constantly reactivated. Higher thresholds prolong emotions because components are more easily reactivated even when connectivity is lower. Indirect evidence from research on emotion coherence and research on the relationship of predictors of emotion duration with components outside of emotional episodes supports the usefulness of the network model. I further argue and show in simulations that a common cause model, in which a latent emotion causes changes in emotion components, cannot account for research on emotion duration. Finally, I describe future directions for research on emotion duration and emotion dynamics from a network perspective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00203-3.

Psychological impact of COVID-19: A cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database

Background

The COVID-19 pandemic and its subsequent health restrictions had an unprecedented impact on mental health, contributing to the emergence and reinforcement of various psychopathological symptoms. This complex interaction needs to be examined especially in a vulnerable population such as older adults.

Objective

In the present study we analyzed network structures of depressive symptoms, anxiety, and loneliness from the English Longitudinal Study of Aging COVID-19 Substudy over two waves (Months of June-July and November-December 2020).

Methods

For this purpose, we use measures of centrality (expected and bridge-expected influence) in addition to the Clique Percolation method to identify overlapping symptoms between communities. We also use directed networks to identify direct effects between variables at the longitudinal level.

Results

UK adults aged >50 participated, Wave 1: 5,797 (54% female) and Wave 2: 6,512 (56% female). Cross-sectional findings indicated that difficulty relaxing, anxious mood, and excessive worry symptoms were the strongest and similar measures of centrality (Expected Influence) in both waves, while depressive mood was the one that allowed interconnection between all networks (bridge expected influence). On the other hand, sadness and difficulty sleeping were symptoms that reflected the highest comorbidity among all variables during the first and second waves, respectively. Finally, at the longitudinal level, we found a clear predictive effect in the direction of the nervousness symptom, which was reinforced by depressive symptoms (difficulties in enjoying life) and loneliness (feeling of being excluded or cut off from others).

Conclusion

Our findings suggest that depressive, anxious, and loneliness symptoms were dynamically reinforced as a function of pandemic context in older adults in the UK.

Lay conceptions of "being moved" ("bewegt sein") include a joyful and a sad type: Implications for theory and research

Being moved has received increased attention in emotion psychology as a social emotion that fosters bonds between individuals and within communities. This increased attention, however, has also sparked debates about whether the term "being moved" refers to a single distinct profile of emotion components or rather to a range of different emotion profiles. We addressed this question by investigating lay conceptions of the emotion components (i.e., elicitors, cognitive appraisals, subjective feelings, bodily symptoms, and consequences for thought/action) of "bewegt sein" (the German term for "being moved"). Participants (N = 106) provided written descriptions of both a moving personal experience and their conceptual prototype of "being moved," which were subjected to content analysis to obtain quantitative data for statistical analyses. Based on latent class analyses, we identified two classes for both the personal experiences (joyfully-moved and sadly-moved classes) and the being-moved prototype (basic-description and extended-description classes). Being joyfully moved occurred when social values and positive relationship experiences were salient. Being sadly moved was elicited by predominantly negative relationship experiences and negatively salient social values. For both classes, the most frequently reported consequences for thought/action were continued cognitive engagement, finding meaning, and increased valuation of and striving for connectedness/prosociality. Basic descriptions of the prototype included "being moved" by positive or negative events as instances of the same emotion, with participants in the extended-description class also reporting joy and sadness as associated emotions. Based on our findings and additional theoretical considerations, we propose that the term "being moved" designates an emotion with an overall positive valence that typically includes blends of positively and negatively valenced emotion components, in which especially the weight of the negative components varies. The emotion's unifying core is that it involves feeling the importance of individuals, social entities, and abstract social values as sources of meaning in one's life.

Measuring emotions in mathematics: the Achievement Emotions Questionnaire-Mathematics (AEQ-M)

Understanding the structure, antecedents, and outcomes of students' emotions has become a topic of major interest in research on mathematics education. Much of this work is based on the Achievement Emotions Questionnaire-Mathematics (AEQ-M), a self-report instrument assessing students' mathematics-related emotions. The AEQ-M measures seven emotions (enjoyment, pride, anger, anxiety, shame, hopelessness, boredom) across class, learning, and test contexts (internal structure). Based on control-value theory, it is assumed that these emotions are evoked by control and value appraisals, and that they influence students' motivation, learning strategies, and performance (external relations). Despite the popularity and frequent use of the AEQ-M, the research leading to its development has never been published, creating uncertainty about the validity of the proposed internal structure and external relations. We close this gap in Study 1 (N = 781 students, Grades 5-10, mean age 14.1 years, 53.5% female) by demonstrating that emotions are organized across contexts and linked to their proposed antecedents and outcomes. Study 2 (N = 699 students, Grade 7 and 9, mean age 14.0 years, 56.9% female) addresses another deficit in research on the AEQ-M, the lack of evidence regarding the assumption that emotions represent sets of interrelated affective, cognitive, motivational, and physiological/expressive components. We close this gap by evaluating extended AEQ-M scales, systematically assessing these components for five core mathematics emotions (enjoyment, anger, anxiety, hopelessness, boredom). Our work provides solid grounds for future research using the AEQ-M to assess emotions and their components in the domain of mathematics.