Less is more: Morphometric and psychological differences between low and high reappraisers

Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach

Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies.

Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach

Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms.

Emotion regulation and delusion-proneness relate to empathetic tendencies in a transdiagnostic sample

Empathetic tendencies (i.e., perspective taking and empathic concern) are a key factor in interpersonal relationships, which may be impacted by emotion regulation (i.e., reappraisal and suppression) and mental health symptoms, such as psychotic-like experiences. However, it is unclear if certain psychotic-like experiences, such as delusion-proneness, are still associated with reduced empathetic tendencies after accounting for emotion regulation style and dimensions of psychopathology that are often comorbid. In the current study, linear models tested these associations in a transdiagnostic community sample (N = 128), using the Interpersonal Reactivity Index (IRI), Emotion Regulation Questionnaire, and the Peter's Delusion Inventory. Results indicated that perspective taking was positively associated with reappraisal and negatively associated with delusion-proneness, after controlling for age, sex, race, intelligence, and symptoms of anxiety and depression. A significant change in R 2 supported the addition of delusion-proneness in this model. Specificity analyses demonstrated perspective taking was also negatively associated with suppression, but this relationship did not remain after accounting for the effects of reappraisal and delusion-proneness. Additional specificity analyses found no association between empathic concern and reappraisal or delusion-proneness but replicated previous findings that empathic concern was negatively associated with suppression. Taken together, delusion-proneness accounts for unique variance in perspective taking, which can inform future experimental research and may have important implications for psychosocial interventions.

Interpersonal emotion regulation questionnaire: psychometric properties of the Italian version and associations with psychopathology

The emerging field of interpersonal emotion regulation (IER) is drawing attention to forms of emotion regulation which involve communication and social interaction as part of the regulation process. The availability of instruments to measure IER in different languages represents significant promise for future work in this field. The goal of the present study was to validate an Italian adaptation of a self-report instrument for the assessment of IER, the Interpersonal Emotion Regulation Questionnaire (IERQ; Hofmann et al., 2016). In an Italian sample (N=448), exploratory and confirmatory factor analyses confirmed the original structure comprising four factors (Enhancing Positive Affect, Perspective Taking, Soothing and Social Modelling). Correlations with other measures of emotion regulation showed good convergent validity of the questionnaire.

Positive Cognitive Reappraisal in Stress Resilience, Mental Health, and Well-Being: A Comprehensive Systematic Review

Stress-related psychopathology is on the rise, and there is a pressing need for improved prevention strategies. Positive appraisal style, the tendency to appraise potentially threatening situations in a positive way, has been proposed to act as a key resilience mechanism and therefore offers a potential target for preventive approaches. In this article, we review n  =  99 studies investigating associations of positive cognitive reappraisal, an important sub-facet of positive appraisal style, with outcome-based resilience and relevant other outcomes, which are considered resilience-related. According to the studies reviewed, positive cognitive reappraisal moderates the relation between stressors and negative outcomes and is positively related to several resilience-related outcomes. It also mediates between other resilience factors and resilience, suggesting it is a proximal resilience factor.

Zhongyong Thinking Style and Resilience Capacity in Chinese Undergraduates: The Chain Mediating Role of Cognitive Reappraisal and Positive Affect

Previous studies have suggested that the Zhongyong thinking style (influenced by Chinese culture) is associated with psychological features. However, little is known about the direct association between Zhongyong thinking and resilience and the underlying mechanisms of this relationship in Chinese culture. The present study aimed to investigate the association between Zhongyong thinking and undergraduates' resilience and to assess whether cognitive reappraisal and positive effects mediated this association. A sample of undergraduates (n = 1,356, 70.4% female, mean age = 19 years) was recruited for this study and the participants completed the Zhongyong Thinking Style Scale (ZYTS), the Emotion Regulation Questionnaire (ERQ), the Positive Affect and Negative Affect Scale (PANAS), and the Resilience-11. Results indicated that the Zhongyong thinking style was positively and significantly associated with resilience. Undergraduates' resilience was affected by Zhongyong thinking partly through 3 different pathways: the mediating role of cognitive reappraisal, the mediating role of positive effect, and the mediating chain role of both cognitive reappraisal and positive effect. These findings might provide a deeper understanding of the protective factors for resilience among Chinese undergraduates.

Built to last: Theta and delta changes in resting-state EEG activity after regulating emotions

BACKGROUND

Over the past years, electroencephalography (EEG) studies focused on task-related activity to characterize cortical responses associated with emotion regulation (ER), without exploring the possibility that regulating emotions can leave a trace in the brain by affecting its oscillatory activity. Demonstrating whether the effect of regulation alters the brain activity after the session and whether this reflects an increased cognitive regulatory ability has great relevance.

METHODS

To address this issue, 5 min of electrical brain activity at rest were recorded before and after (1) one session in which participants perceived and regulated (through distancing) their emotions (regulation session, ReS), and (2) another session in which they only perceived emotions (attend session, AtS). One hundred and sixty visual stimuli were presented, and subjective ratings of valence and arousal of stimuli were recorded.

RESULTS

Behavioral results showed the efficacy of the regulation strategy in modulating both arousal and valence. A cluster-based permutation test on EEG data at rest revealed a significant increase in theta and delta activity after the ReS compared to the AtS, suggesting that regulating emotions can alter brain activity after the session.

CONCLUSIONS

These results allowed us to outline a comprehensive view of the neurophysiological mechanisms associated with ER, as well as some possible implications in psychotherapy.

Dissociating the role of dACC and dlPFC for emotion appraisal and mood regulation using cathodal tDCS

Several neuroimaging studies have shown that a distributed network of brain regions is involved in our ability to appraise the emotions we experience in daily life. In particular, scholars suggested that the dorsal anterior cingulate cortex (dACC) may play a role in the appraisal of emotional stimuli together with subcortical regions, especially when stimuli are negatively valenced, and the dorsolateral prefrontal cortex (dlPFC) may play a role in regulating emotions. However, proofs of the causal role of these regions are lacking. In the present study, we aim at testing this model by stimulating both the dACC and the left dlPFC via cathodal tDCS. Twenty-four participants were asked to attend and rate the arousal and valence of negative and neutral emotional stimuli (pictures and words) in three different experimental sessions: cathodal stimulation of dACC, left dlPFC, or sham. In addition to the experimental task, the baseline affective state was measured before and after the stimulation to further assess the effect of stimulation over the baseline affective state after the experimental session. Results showed that cathodal stimulation of dACC, but not the left dlPFC, was associated with reduced arousal ratings of emotional stimuli, both compared with the sham condition. Moreover, cathodal stimulation of left dlPFC decreased participant's positive affective state after the session. These findings suggest for the first time, a dissociation between the dACC and dlPFC, with the former more involved in emotion appraisal, and the latter more involved in mood modulation.

Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach

Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.

Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis

Child trauma plays an important role in the etiology of Bordeline Personality Disorder (BPD). Of all traumas, sexual trauma is the most common, severe and most associated with receiving a BPD diagnosis when adult. Etiologic models posit sexual abuse as a prognostic factor in BPD. Here we apply machine learning using Multiple Kernel Regression to the Magnetic Resonance Structural Images of 20 BPD and 13 healthy control (HC) to see whether their brain predicts five sources of traumas: sex abuse, emotion neglect, emotional abuse, physical neglect, physical abuse (Child Trauma Questionnaire; CTQ). We also applied the same analysis to predict symptom severity in five domains: affective, cognitive, impulsivity, interpersonal (Zanarini Rating Scale for Borderline Personality Disorder; Zan-BPD) for BPD patients only. Results indicate that CTQ sexual trauma is predicted by a set of areas including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum in BPD patients only. Importantly, interpersonal problems only in BPD patients were predicted by a set of areas including temporal lobe and cerebellar regions. Notably, sexual trauma and interpersonal problems were not predicted by structural features in matched healthy controls. This finding may help elucidate the brain circuit affected by traumatic experiences and connected with interpersonal problems BPD suffer from.

Structural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study

The ability to experience, use and eventually control anger is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly understood. To elucidate these points, we employed an unsupervised machine learning approach based on independent component analysis to test the hypothesis that specific functional and structural networks are associated with individual differences in trait anger and anger control. Structural and functional resting state images of 71 subjects as well as their scores from the State-Trait Anger Expression Inventory entered the analyses. At a structural level, the concentration of grey matter in a network including ventromedial temporal areas, posterior cingulate, fusiform gyrus and cerebellum was associated with trait anger. The higher the concentration, the higher the proneness to experience anger in daily life due to the greater tendency to orient attention towards aversive events and interpret them with higher hostility. At a functional level, the activity of the default mode network (DMN) was associated with anger control. The higher the DMN temporal frequency, the stronger the exerted control over anger, thus extending previous evidence on the role of the DMN in regulating cognitive and emotional functions in the domain of anger. Taken together, these results show, for the first time, two specialized brain networks for encoding individual differences in trait anger and anger control.

Anxiety Regulation: From Affective Neuroscience to Clinical Practice

According to psychoanalysis, anxiety signals a threat whenever a forbidden feeling emerges. Anxiety triggers defenses and maladaptive behaviors, thus leading to clinical problems. For these reasons, anxiety regulation is a core aspect of psychodynamic-oriented treatments to help clients. In the present theoretical paper, we review and discuss anxiety generation and dysregulation, first from a neural point of view, presenting findings from neuroimaging and psychophysiological studies. The aim is to trace parallels with psychodynamic theories of anxiety. Then, we discuss the psychological mechanisms and neural bases of emotion regulation in the laboratory, and possible neurobiological mechanisms of anxiety regulation in psychotherapy. We describe two different approaches to emotion/anxiety regulation, one based on the standard cognitive model of emotion regulation, the other based on psychodynamic principles and affective neuroscience. We then illustrate in detail a dynamic experiential approach to regulation. This model claims that emotions arise before cognition and are not inherently dysregulated. Dysregulation emerges from co-occurrences of emotions and associated anxiety. Technical consequences of this model are discussed and include strategies to regulate anxiety.

Tune in to the right frequency: Theta changes when distancing from emotions elicited by unpleasant images and words

Emotion regulation allows humans to successfully modulate their reactions to life events. Whether regulation strategies may alter brain oscillatory activity and how they are influenced by format and emotional dimensions is still under debate. We investigated oscillatory brain dynamics during the implementation of the strategy of Distancing and during the regulation of the emotions elicited by neutral and unpleasant pictures and, for the first time, words. When implementing the strategy, an early increase in theta band in posterior regions was observed (Effect of Strategy). We interpret this effect as a marker of emotion regulation, and we suggest an integrative framework of the role of theta on regulatory processes. When regulating the emotional impact elicited by stimuli, a decrease in the theta and beta bands in posterior regions for pictures, but not for words, was observed (Effect of Regulation). Behaviorally, the Effect of Regulation was evident for both pictures and words and more pronounced for Valence than for Arousal. These results contribute to better understand the neural and behavioral features of Distancing (both Effect of Strategy and of Regulation), and open up the possibility to clarify which strategy works better to modulate specific stimulus types and emotional dimensions.

A Dual Route Model for Regulating Emotions: Comparing Models, Techniques and Biological Mechanisms

The aim of this article is to present recent applications of emotion regulation theory and methods to the field of psychotherapy. The term Emotion Regulation refers to the neurocognitive mechanisms by which we regulate the onset, strength, and the eventual expression of our emotions. Deficits in the regulation of emotions have been linked to most, if not all, psychiatric disorders, with patients presenting either dysregulated emotions, or dysfunctional regulatory strategies. We discuss the implications of regulating emotions from two different theoretical perspectives: the Cognitive Emotion Regulation (CER), and the Experiential-Dynamic Emotion Regulation (EDER) model. Each proposes different views on how emotions are generated, dysregulated and regulated. These perspectives directly influence the way clinicians treat such problems. The CER model views emotional dysregulation as due to a deficit in regulation mechanisms that prioritizes modifying or developing cognitive skills, whilst the EDER model posits emotional dysregulation as due to the presence of dysregulatory mechanisms that prioritizes restoring natural regulatory processes. Examples of relevant techniques for each model are presented including a range of cognitive-behavioral, and experiential (including both dynamic and cognitive) techniques. The aim of the paper is to provide a toolbox from which clinician may gain different techniques to enhance and maintain their patient’s capacity for emotional regulation. Finally, the biological mechanisms behind the two models of emotion regulation are discussed as well as a proposal of a dual route model of emotion regulation.