Neural patterns during anticipation predict emotion regulation success for reappraisal

Neuroimaging evidence of disturbed self-appraisal in posttraumatic stress disorder: A systematic review

BACKGROUND

The experience of self-hood in posttraumatic stress disorder (PTSD) is altered cognitively and somatically. Dysfunctional negative cognitions about the self are a central mechanism of PTSD symptomatology and treatment. However, while higher-order brain models of disturbances in self-appraisal (i.e., cognitive processes relating to evaluating the self) have been examined in other psychiatric disorders, it is unclear how normative brain function during self-appraisal is impaired in PTSD.

METHODS

This paper presents a PRISMA systematic review of functional neuroimaging studies (n = 5), to establish a neurobiological account of how self-appraisal processes are disturbed in PTSD. The review was prospectively registered with PROSPERO (CRD42023450509).

RESULTS

Self-appraisal in PTSD is linked to disrupted activity in core self-processing regions of the Default Mode Network (DMN); and regions involved in cognitive control and emotion regulation, salience and valuation.

LIMITATIONS

Because self-appraisal in PTSD is relatively under-studied, only a small number of studies could be included for review. Cross-study heterogeneity in analytic approaches and trauma-exposure history prohibited a quantitative meta-analysis.

CONCLUSIONS

This paper proposes a mechanistic account of how neural dysfunctions may manifest clinically in PTSD and inform targeted selection of appropriate treatment options. We present a research agenda for future work to advance the field.

Disentangling periodic and aperiodic resting EEG correlates of personality

Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity-Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.

Positive emotions and their upregulation increase willingness to consume healthy foods

While highly relevant for everyday life, it is unclear whether experiencing incidental positive or negative emotional states, and active emotion regulation, influence the weighting of perceived taste and health in food choices. In Experiment 1, we examined two emotion regulation strategies, reappraisal and distraction, used to decrease negative emotions. Participants were cued to experience or decrease their emotional response for either neutral or negative incidental emotion-inducing images. They subsequently rated their willingness to consume foods, which varied in their taste and health attributes. Mixed-effects model analysis showed that compared to neutral, negative emotions decreased willingness to consume, regardless of perceived taste and health, but neither emotion regulation strategy had a significant effect. Experiment 2 used images inducing incidental positive emotions in combination with three emotion regulation strategies: reappraisal, distraction, and increasing positive emotions. Experiencing positive emotions generally increased willingness to consume, with stronger effects for tasty and healthy foods. Decreasing positive emotions via reappraisal decreased willingness to consume, particularly for healthy foods. Increasing positive emotion intensity further increased willingness to consume, with stronger effects for healthy foods. The results suggest that experiencing positive emotions increases desire particularly strongly for healthy foods, which can additionally be modulated via emotion regulation. This has important implications for designing health-related interventions targeting mood improvement.

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX)

Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

More than just fear: Development and psychometric evaluation of the Spider Distress Scale to assess spider fear and spider-related disgust

Spider fear is an excellent model to experimentally study processes in the maintenance and treatment of long-lasting fears. A valid, reliable, and practical tool to assess spider-related distress dimensionally, and to differentiate between spider-related fear and disgust in a time-sensitive manner, may help to better understand individual differences in these two emotions and to tailor treatments accordingly. We developed a concise self-report questionnaire, the Spider Distress Scale (SDS), that combines the strengths of established spider fear questionnaires and addresses their shortcomings. We explored (study 1 and 2) and confirmed (study 3) a two-factor structure of the SDS in samples from the general population (n = 370; n = 360; n = 423), recruited online via Prolific Academic from the United Kingdom, the Netherlands, and the United States. The fear and disgust factors of the SDS are highly internally consistent and the SDS has excellent test-retest reliability. We found good convergent and discriminant validity, based on self-report measures and spider behavioural approach tasks, and the SDS successfully differentiated between individuals with and without spider fear (study 4, n = 75). Our series of studies suggests that fear and disgust are functionally related, but that disgust towards spiders can be differentially assessed when focussing on unique elements of disgust-related information.

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) to estimate a regression model predicting ratings of tastiness and healthiness for each participant, based on their neural activity occurring in the first second of food cue processing. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness. In Experiment 2, 89 healthy participants completed a similar rating task, followed by an additional experimental phase, in which they indicated their desire to consume snack foods with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Charitable donations are an altruistic behavior whereby individuals donate money or other resources to benefit others while the recipient is normally absent from the context. Several psychological factors have been shown to influence charitable donations, including a cost-benefit analysis, the motivation to engage in altruistic behavior, and the perceived psychological benefits of donation. Recent work has identified the ventral medial prefrontal cortex (MPFC) for assigning value to options in social decision making tasks, with other regions involved in empathy and emotion contributing input to the value computation (e.g. Hare et al., 2010; Hutcherson et al., 2015; Tusche et al., 2016). Most impressively, multivariate pattern analysis (MVPA) has been applied to fMRI data to predict donation behavior on a trial-by-trial basis from ventral MPFC activity (Hare et al., 2010) while identifying the contribution of emotional processing in other regions to the value computation (e.g. Tusche et al., 2016). MVPA of EEG data may be able to provide further insight into the timing and scalp topography of neural activity related to both value computation and emotional effects on donation behavior. We examined the effect of incidental emotional states and the perceived urgency of the charitable cause on donation behavior using support vector regression on EEG data to predict donation amount on a trial by trial basis. We used positive, negative, and neutral pictures to induce incidental emotional states in participants before they made donation decisions concerning two types of charities. One category of charity was oriented toward saving people from current suffering, and the other was to prevent future suffering. Behaviorally, subjects donated more money in a negative emotional state relative to other emotional states, and more money to alleviate current over future suffering. The data-driven multivariate pattern analysis revealed that the electrophysiological activity elicited by both emotion-priming pictures and charity cues could predict the variation in donation magnitude on a trial-by-trial basis.