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Background: The abandonment of psychotherapeutic treatments is influenced by various factors, including patient characteristics, therapist traits, and the therapeutic relationship. Despite the well-documented importance of these factors, limited empirical research has focused on the role of the therapeutic relationship and the characteristics of therapist-patient dyads in predicting treatment dropout. This study protocol outlines a longitudinal research project aimed at predicting dropout and non-response in psychotherapy for individuals with personality disorders. The research seeks to identify predictive factors related to psychotherapy outcomes, focusing on patient, therapist, and dyadic elements. Specifically, the study will examine the influence of therapist characteristics (e.g., personality traits, countertransference, responsiveness) on treatment outcomes, explore the impact of relational factors (e.g., treatment expectations, epistemic trust, therapeutic alliance) on therapy effectiveness, and assess how the therapeutic alliance within therapist-patient dyads affects the likelihood of dropout and non-response., Methods: The longitudinal study will include 100 therapist-patient dyads (200 participants) recruited from various Mental Health Services in Milan, Italy. Validated instruments will be administered to both patients and therapists at four-time points: T0 (baseline), T1 (3 months), T2 (6 months), and T3 (1 year). Data will be collected at baseline and at the one-year mark to evaluate the relationships between therapist, patient, and dyadic factors and treatment outcomes., Discussion: Identifying predictive variables associated with high dropout rates can help preempt treatment discontinuation, reducing the financial and operational burdens on mental health services. Understanding these factors will enable the development of targeted interventions to improve treatment engagement and reduce attrition. This approach could enhance outcomes for individuals with personality disorders and lead to more efficient resource allocation and sustainable delivery of mental health care., (© 2024. The Author(s).)

The neurobiological mechanism of borderline personality disorder (BPD) in adolescents remains unclear. The study aimed to assess the alterations in neural activity within prefrontal cortex in adolescents with BPD and investigate the relationship of prefrontal activity with emotional regulation and cognitive function. This study enrolled 50 adolescents aged 12-17 years with BPD and 21 gender and age-matched healthy control (HC) participants. Study assessment for each participant included a brain resting-state functional MRI (rs-fMRI), clinical assessment questionnaires such as Borderline Personality Features Scale (BPFS), Difficulties in Emotion Regulation Scale (DERS), Ottawa Self-Injury Inventory and Childhood Trauma Questionnaire (CTQ) and cognitive testing with Stroop Color-Word Test (SCWT). Fractional amplitude of low-frequency fluctuations (fALFF) and seed-based functional connectivity (FC) were obtained from rs-fMRI analysis. Correlation analysis was also performed to evaluate the associations of the neuroimaging metrics such as fALFF and FC with clinical assessment questionnaire and cognitive testing scores. Adolescents with BPD showed increased fALFF values in the right inferior frontal gyrus and decreased activity in the left middle frontal gyrus as compared to the HC group (p < 0.05, cluster size ≥ 100, FWE correction). In adolescents with BPD, increased fALFF in the right inferior frontal gyrus was related to the BPFS (emotional dysregulation), DERS-F (lacking of emotional regulation strategies) and Ottawa Self-Injury Inventory-4 C scores (internal emotional regulation function of self-injurious behavior). The reduced fALFF in the left middle frontal gyrus was associated with the SCWT-A (reading characters) and the SCWT-B (reading color) scores. Additionally, the fALFF values in the left middle frontal gyrus and the right inferior frontal gyrus were related to the CTQ-D (emotional neglect) (p < 0.05). The left middle frontal gyrus exhibited increased FC with the right hippocampus, left inferior temporal gyrus and right inferior frontal gyrus (voxel p < 0.001, cluster p < 0.05, FWE correction). The increased FC between the left middle frontal gyrus and the right hippocampus was related to the SCWT-C (cognitive flexibility) score. We observed diverging changes in intrinsic brain activity in prefrontal cortex, and neural compensatory changes to maintain function in adolescents with BPD. In addition, decreased neural function was closely associated with emotional dysregulation, while increased neural function as indicated by brain activity and FC was associated with cognitive dysfunction. These results indicated that alterations of intrinsic brain activity may be one of the underlying neurobiological markers for clinical symptoms in adolescents with BPD., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro-networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies., (© 2024 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks' contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation., (© 2024. The Author(s).)

Female Sexual Objectification refers to perceiving and treating women based on their body appearance. This phenomenon may serve as a precursor for dysfunctional behaviors, particularly among females prone to self-objectification and experiencing shame emotions. Understanding this challenging trajectory by disclosing its neural consequences may be crucial for comprehending extreme psychopathological outcomes. However, investigations in this sense are still scarce. The present study explores the neural correlates of female participants' experiences of being objectified and their relationship with self-objectification, emotional responses and individual dispositions in self-esteem, emotion regulation abilities and self-conscious emotion proneness. To this aim, 25 female participants underwent an fMRI experimental session while they were exposed to interpersonal encounters with objectifying or non-objectifying men. Participants' experienced emotions and levels of attention shifted toward their bodies (self-objectification) was reported after each interaction. The results revealed increased brain activity in objectifying contexts, impacting cortical (frontal, occipital and temporal cortex) and subcortical regions (thalamus, and hippocampus) involved in visual, emotion, and social processing. Remarkably, the inferior temporal gyrus emerged as a crucial neural hub associated in opposite ways with self-esteem and the self-conscious emotion of shame, highlighting its role in self-referential processing during social dynamics. This study points out the importance of adopting a neuroscientific perspective for a deeper understanding of sexual objectification, and to shed light on its possible neural consequences., Competing Interests: Declaration of competing interest The authors have no known conflict of interest to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

The concept of emotional intelligence (EI) refers to the ability to recognize and regulate emotions to appropriately guide cognition and behaviour. Unfortunately, studies on the neural bases of EI are scant, and no study so far has exhaustively investigated grey matter (GM) and white matter (WM) contributions to it. To fill this gap, we analysed trait measure of EI and structural MRI data from 128 healthy participants to shed new light on where and how EI is encoded in the brain. In addition, we explored the relationship between the neural substrates of trait EI and trait anxiety. A data fusion unsupervised machine learning approach (mCCA + jICA) was used to decompose the brain into covarying GM-WM networks and to assess their association with trait-EI. Results showed that high levels trait-EI are associated with decrease in GM-WM concentration in a network spanning from frontal to parietal and temporal regions, among which insula, cingulate, parahippocampal gyrus, cuneus and precuneus. Interestingly, we also found that the higher the GM-WM concentration in the same network, the higher the trait anxiety. These findings encouragingly highlight the neural substrates of trait EI and their relationship with anxiety. The network is discussed considering its overlaps with the Default Mode Network., (© The Author(s) 2024. Published by Oxford University Press.)

A crucial point for the understanding of the link between attachment and emotion regulation concerns the individual tendency in turning to others to alleviate distress. Most previous studies in this field have considered almost exclusively intra-personal forms of emotion regulation, neglecting the role of social interaction in emotion regulation processes. In the present study, instead, we focused on interpersonal emotion regulation. 630 adults were assessed for their attachment orientations, general difficulties in emotion regulation, and habitual intra-personal and interpersonal emotion regulation strategies. Results showed that the imbalance between the hyper-activation and deactivation of the attachment system, which characterize unsecure attachment, reflects a correspondent imbalance in the use of emotion regulation strategies, with an exaggerated dependence on other associated with attachment anxiety and pseudo-autonomy associated to attachment avoidance.

Sexually objectified women are perceived as dehumanized. This may affect the behavioral and neural responses underlying the observer's empathic reactions for their physical pain, although this hypothesis still lacks empirical support. In the present study, we measured the electrophysiological activity of 30 participants (14 females and 16 males), in an empathy for physical pain paradigm in which pictures of sexualized and non-sexualized women were presented in painful and non-painful situations. The behavioral results revealed that sexualized women were evaluated as experiencing less pain than non-sexualized women. Neural evidence corroborated this finding showing that the perception of vicarious physical pain is lacking for sexualized women in both event-related potentials (ERPs) and brain oscillation domains. Specifically, the P2 component and the event-related synchronization/desynchronization (ERS/ERD) on the mu frequency band differed between painful and non-painful stimulation exclusively when women were not sexualized. Our results provide the first evidence that the neurophysiological responses to the vicarious experience of physical pain are dampened or even absent for sexualized women. These findings expand our understanding of the neurophysiological signatures of empathic processes and highlight the detrimental effect of a sexual-objectification bias in everyday contexts., (© 2023 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features. Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschl's gyrus successfully predicted narcissistic personality traits ( p  < 0.003). Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits. This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.

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., (© 2023. The Author(s).)

Insecure attachment and difficulties in regulating anger have both been put forward as possible explanations for emotional dysfunction in borderline personality (BP). This study aimed to test a model according to which the influence of attachment on BP features in a subclinical population is mediated by anger regulation. In a sample of 302 participants, BP features were assessed with the Borderline features scale of the Personality Assessment Inventory (PAI-BOR), attachment was measured with the Experiences in Close Relationships-12 (ECR-12), and trait anger and anger regulation were assessed with the State and Trait Anger Expression Inventory-2 (STAXI-2). The results indicated that anger suppression emerged as a significant mediator of the associations between both anxious and avoidant attachment and BP traits, while anger control resulted as a marginal mediator in the association between attachment avoidance and BP. Suppressing anger may reflect different forms of cognitive or behavioural avoidance of anger, which may differ on the basis of attachment orientations. We argue that these results may have important clinical implications: the promotion of anger regulation in BP should be considered a critical treatment goal.

Individuals who deviate from social norms by committing crimes may have reduced facial emotion recognition abilities. Nevertheless, a specific category of offenders - i.e. organised crime (OC) members - is characterised by hierarchically organised social networks and a tendency to manipulate others to reach their illicit goals. Since recognising emotions is crucial to building social networks, OC members may be more skilled in recognising the facial emotion expressions of others to use this information for their criminal purposes. Evidence of a difference between OC and non-organised crime (NOC) offenders in terms of facial emotion recognition is still lacking. To fill this gap in the literature, we tested 50 OC, 50 NOC offenders, and 50 non-offender controls for their ability to identify six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). All participants underwent a cognitive and psychological evaluation to avoid alternative explanations. Results show that OC members were more able to detect the expression of fear in others as compared to NOC. We interpreted this finding in light of the social context and the behavioural criminal attitude of OC members.

Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms., (© 2022 Society for Psychophysiological Research.)

This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visual arts, and whether these differences can be used to accurately classify individuals as being an artist or not. Previous research using functional MRI has suggested that general creativity involves a balance between the default mode network and the executive control network. However, it is not known whether there are structural differences between the brains of artists and non-artists. In this study, a machine learning method called Multi-Kernel Learning (MKL) was applied to gray matter images of 12 artists and 12 non-artists matched for age and gender. The results showed that the predictive model was able to correctly classify artists from non-artists with an accuracy of 79.17% (AUC 88%), and had the ability to predict new cases with an accuracy of 81.82%. The brain regions most important for this classification were the Heschl area, amygdala, cingulate, thalamus, and parts of the parietal and occipital lobes as well as the temporal pole. These regions may be related to the enhanced emotional and visuospatial abilities that professional artists possess compared to non-artists. Additionally, the reliability of this circuit was assessed using two different classifiers, which confirmed the findings. There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process.

Sexual objectification and the interiorized objectifying gaze (self-objectification) are dangerous phenomena for women's psychological wellness. However, their specific effects on women's socio-affective reactions are still poorly understood, and their neural activity has never been explored before. In the present study, we investigated women's emotional and electrophysiological responses during simulated computer-based objectifying social interactions, and we examined consequent punishing behaviours towards the perpetrator using the ultimatum game. Behavioural results (N = 36) showed that during objectifying encounters women generally felt angrier/disgusted and tended to punish the perpetrator in later interactions. However, the more the women self-objectified, the more they felt ashamed (p = 0.011) and tended to punish the perpetrators less (p = 0.008). At a neural level (N = 32), objectifying interactions modulated female participants' neural signal elicited during the processing of the perpetrator, increasing early (N170) and later (EPN, LPP) ERP components. In addition, only the amplitude of the LPP positively correlated with shame (p = 0.006) and the level of self-objectification (p = 0.018). This finding provides first evidence for the specific time-course of sexual objectification, self-objectification and its associated shame response, and proves that emotional and social consequences of sexual objectification in women may depend on their tendency to self-objectify., (© 2023. The Author(s).)

Self-conscious emotions, such as shame and guilt, play a fundamental role in regulating moral behaviour and in promoting the welfare of society. Despite their relevance, the neural bases of these emotions are uncertain. In the present meta-analysis, we performed a systematic literature review in order to single out functional neuroimaging studies on healthy individuals specifically investigating the neural substrates of shame, embarrassment, and guilt. Seventeen studies investigating the neural correlates of shame/embarrassment and seventeen studies investigating guilt brain representation met our inclusion criteria. The analyses revealed that both guilt and shame/embarrassment were associated with the activation of the left anterior insula, involved in emotional awareness processing and arousal. Guilt-specific areas were located within the left temporo-parietal junction, which is thought to be involved in social cognitive processes. Moreover, specific activations for shame/embarrassment involved areas related to social pain (dorsal anterior cingulate and thalamus) and behavioural inhibition (premotor cortex) networks. This pattern of results might reflect the distinct action tendencies associated with the two emotions.

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.

Anger can be broken down into different elements: a transitory state (state anger), a stable personality feature (trait anger), a tendency to express it (anger-out), or to suppress it (anger-in), and the ability to regulate it (anger control). These elements are characterized by individual differences that vary across a continuum. Among them, the abilities to express and suppress anger are of particular relevance as they determine outcomes and enable successful anger management in daily situations. The aim of this study was to demonstrate that anger suppression and expression can be decoded by patterns of grey matter of specific well-known brain networks. To this aim, a supervised machine learning technique, known as Kernel Ridge Regression, was used to predict anger expression and suppression scores of 212 healthy subjects from the grey matter concentration. Results show that individual differences in anger suppression were predicted by two grey matter patterns associated with the Default-Mode Network and the Salience Network. Additionally, individual differences in anger expression were predicted by a circuit mainly involving subcortical and fronto-temporal regions when considering whole brain grey matter features. These results expand previous findings regarding the neural bases of anger by showing that individual differences in specific anger-related components can be predicted by the grey matter features of specific networks., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.

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., Competing Interests: Declaration of Competing Interest Author SB was funded by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Therapists' responses to patients play a crucial role in psychotherapy and are considered a key component of the patient-clinician relationship, which promotes successful treatment outcomes. To date, no empirical research has ever investigated therapist response patterns to patients with different personality disorders from a neuroscience perspective., Methods: In the present study, psychodynamic therapists ( N = 14) were asked to complete a battery of instruments (including the Therapist Response Questionnaire) after watching three videos showing clinical interactions between a therapist and three patients with narcissistic, histrionic/borderline, and depressive personality disorders, respectively. Subsequently, participants' high-density electroencephalography (hdEEG) was recorded as they passively viewed pictures of the patients' faces, which were selected from the still images of the previously shown videos. Supervised machine learning (ML) was used to evaluate whether: (1) therapists' responses predicted which patient they observed during the EEG task and whether specific clinician reactions were involved in distinguishing between patients with different personality disorders (using pairwise comparisons); and (2) therapists' event-related potentials (ERPs) predicted which patient they observed during the laboratory experiment and whether distinct ERP components allowed this forecast., Results: The results indicated that therapists showed distinct patterns of criticized/devalued and sexualized reactions to visual depictions of patients with different personality disorders, at statistically systematic and clinically meaningful levels. Moreover, therapists' late positive potentials (LPPs) in the hippocampus were able to determine which patient they observed during the EEG task, with high accuracy., Discussion: These results, albeit preliminary, shed light on the role played by therapists' memory processes in psychotherapy. Clinical and neuroscience implications of the empirical investigation of therapist responses are discussed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tanzilli, Trentini, Grecucci, Carone, Ciacchella, Lai, Sabogal-Rueda and Lingiardi.)

A converging body of behavioural findings supports the hypothesis that the dispositional use of emotion regulation (ER) strategies depends on trait emotional intelligence (trait EI) levels. Unfortunately, neuroscientific investigations of such relationship are missing. To fill this gap, we analysed trait measures and resting state data from 79 healthy participants to investigate whether trait EI and ER processes are associated to similar neural circuits. An unsupervised machine learning approach (independent component analysis) was used to decompose resting-sate functional networks and to assess whether they predict trait EI and specific ER strategies. Individual differences results showed that high trait EI significantly predicts and negatively correlates with the frequency of use of typical dysfunctional ER strategies. Crucially, we observed that an increased BOLD temporal variability within sensorimotor and salience networks was associated with both high trait EI and the frequency of use of cognitive reappraisal. By contrast, a decreased variability in salience network was associated with the use of suppression. These findings support the tight connection between trait EI and individual tendency to use functional ER strategies, and provide the first evidence that modulations of BOLD temporal variability in specific brain networks may be pivotal in explaining this relationship., (© 2022. The Author(s).)

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.

Previous electroencephalographic and brain stimulation studies have shown that anger responses may be differently lateralized in the prefrontal cortex, with outward-oriented responses (externalized anger) linked to left prefrontal activity, and inward-oriented responses (internalized anger) linked to right prefrontal activity. However, the specific neural structures involved in this asymmetry, and how they interact to produce individual differences, remain unexplored. Furthermore, it is unclear whether such asymmetry may be explained by general behavioral tendencies, known as Behavioral Activation and Behavioral Inhibition Systems (BIS/BAS). Therefore, we analyzed the tendency of externalizing and internalizing anger, respectively measured by the Anger-Out and Anger-In subscales of the State-Trait Anger Expression Inventory, with the patterns of functional connectivity at rest of 71 participants. A left, prefrontal, resting-state, functional connectivity pattern was found for externalizing anger (Anger-Out), including the left inferior frontal gyrus and the left frontal eye fields. By contrast, a right, prefrontal, resting-state, functional connectivity pattern was found for internalizing anger (Anger-In), including the rostral and lateral prefrontal cortex, the orbitofrontal cortex, the frontal pole, the superior, middle and inferior frontal gyri, and the anterior cingulate. Notably, these patterns were not associated with the BIS/BAS scores. In this study, for the first time, we provide evidence using fMRI functional connectivity for two specific lateralized circuits contributing to individual differences in externalizing and internalizing anger. These results confirm and extend the asymmetry hypothesis for anger and have notable implications in the treatment of anger-related problems., (© 2022. The Author(s).)

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., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Grecucci, Lapomarda, Messina, Monachesi, Sorella and Siugzdaite.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dadomo, Salvato, Lapomarda, Ciftci, Messina and Grecucci.)

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., (© 2021 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

The goal of this research was to validate an Italian adaptation of the questionnaire Difficulties in Interpersonal Regulation of Emotions (DIRE) and to investigate its associations with psychopathology. An Italian sample ( N  = 630) completed the DIRE and the Symptom Checklist-90 (SCL-90). We tested the factorial structure of the DIRE using explorative and confirmatory factorial analyses; we analysed the convergent validity in terms of zero-order correlations with SCL-90 dimensions; and, we conducted multiple regressions to test the predictivity of DIRE factors on specific SCL-90 dimensions. The Italian DIRE replicated the four-factor structure of the original measure, with two interpersonal (Vent and Reassurance-seek) and two intrapersonal (Accept and Avoid) factors. Interpersonal factors resulted correlated with SCL-90 global indexes of psychopathology. Moreover, specific association between DIRE factors and SCL-90 dimensions were found. The Italian DIRE is a reliable and valid measure to evaluate clinically-relevant forms of emotion dysregulation., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Objective Preliminary findings suggest that different kinds of meditation could work on diverse cognitive and psychological processes. The present study aimed at disentangling the effects of three mindfulness techniques on mental rumination and subjective time perspective. Design: 75 young healthy participants were randomly assigned to one of four conditions: mindful breathing, body scan, observing-thoughts meditation and an active control condition. The meditation groups practiced mindfulness daily and attended weekly group meetings for 8 weeks, while the control group was involved in reading and discussing a book about mindfulness. Main outcome measures: Self-report measures of mindfulness skills, ruminative thinking, attitude towards time, anxiety, depression and personality traits. Results: In all meditation conditions, analysis of covariance showed a significant improvement in mindfulness skills compared to the control group. We found a specific effect of mindful breathing in reducing participants' tendency to brooding and in increasing their positive vision of the future, compared to all other conditions. Conclusions: As expected, we found some differential effects: breathing meditation helped practitioners to train more effectively their ability to disengage from maladaptive ruminative thoughts, which could be reflected in a more optimistic attitude toward the future. These results provide useful information to structure better mindfulness-based interventions.

Several studies have suggested a correlation between heart rate variability (HRV), emotion regulation (ER), psychopathological conditions, and cognitive functions in the past two decades. Specifically, recent data seem to support the hypothesis that low-frequency heart rate variability (LF-HRV), an index of sympathetic cardiac control, correlates with worse executive performances, worse ER, and specific psychopathological dimensions. The present work aims to review the previous findings on these topics and integrate them from two main cornerstones of this perspective: Porges' Polyvagal Theory and Thayer and Lane's Neurovisceral Integration Model, which are necessary to understand these associations better. For this reason, based on these two approaches, we point out that low HRV is associated with emotional dysregulation, worse cognitive performance, and transversal psychopathological conditions. We report studies that underline the importance of considering the heart-brain relation in order to shed light on the necessity to implement psychophysiology into a broader perspective on emotions, mental health, and good cognitive functioning. This integration is beneficial not only as a theoretical ground from which to start for further research studies but as a starting point for new theoretical perspectives useful in clinical practice.

According to the nosological classification, Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) are different syndromes. However, these pathological conditions share a number of affective symptoms that make the diagnosis difficult. Affective symptoms range from abnormal mood swings, characterizing both BD and BPD, to regulation dysfunctions, more specific to BPD. To shed light on the neural bases of these aspects, and to better understand differences and similarities between the two disorders, we analysed for the first time gray and white matter features of both BD and BPD. Structural T1 images from 30 patients with BD, 20 with BPD, and 45 controls were analysed by capitalizing on an innovative whole-brain multivariate method known as Source-based Morphometry. Compared to controls, BD patients showed increased gray matter concentration (p = .003) in a network involving mostly subcortical structures and cerebellar areas, possibly related to abnormal mood experiences. Notably, BPD patients showed milder alterations in the same circuit, standing in the middle of a continuum between BD and controls. In addition to this, we found an altered white matter network specific to BPD (p = .018), including frontal-parietal and temporal regions possibly associated with dysfunctional top-down emotion regulation. These findings may shed light on a better understanding of affective disturbances behind the two disorders, with BD patients more characterized by abnormalities in neural structures involved in mood oscillations, and BPD by deficits in the cognitive regulation of emotions. These results may help developing better treatments tailored to the specific affective disturbances displayed by these patients., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This article presents and defends an integrated view of the placebo effect, termed "affective-meaning-making" model, which draws from theoretical reflection, clinical outcomes, and neurophysiological findings. We consider the theoretical limitations of those proposals associated with the "meaning view" on the placebo effect which (a) leave the general aspects of meaning unspecified, (b) fail to analyze fully the role of emotions and affect, and (c) establish no clear connection between the theoretical, physiological, and psychological aspects of the effect. We point out that a promising way to overcome these limitations is given by grounding the placebo effect on Peirce's theory of meaning, in which the role of the meaning constitution and change is placed in logical and objective structures. We also show the connection between our theoretical proposal and the appraisal theory and integrate it with emotion regulation., (© The Author(s) 2021. Published by Oxford University Press, on behalf of the Journal of Medicine and Philosophy Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Bipolar disorder is an affective disorder characterized by rapid fluctuations in mood ranging from episodes of depression to mania, as well as by increased impulsivity. Previous studies investigated the neural substrates of bipolar disorder mainly using univariate methods, with a particular focus on the neural circuitry underlying emotion regulation difficulties. In the present study, capitalizing on an innovative whole-brain multivariate method to structural analysis known as Source-based Morphometry, we investigated the neural substrates of bipolar disorder and their relation with impulsivity, assessed with both self-report measures and performance-based tasks. Structural images from 46 patients with diagnosis of bipolar disorder and 60 healthy controls were analysed. Compared to healthy controls, patients showed decreased gray matter concentration in a parietal-occipital-cerebellar network. Notably, the lower the gray matter concentration in this circuit, the higher the self-reported impulsivity. In conclusion, we provided new evidence of an altered brain network in bipolar disorder patients related to their abnormal impulsivity. Taken together, these findings extend our understanding of the neural and symptomatic characterization of bipolar disorder., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Psychological studies have demonstrated that expectations can have substantial effects on choice behavior, although the role of expectations on social decision making in particular has been relatively unexplored. To broaden our knowledge, we examined the role of expectations on decision making when interacting with new game partners and then also in a subsequent interaction with the same partners. To perform this, 38 participants played an Ultimatum Game (UG) in the role of responders and were primed to expect to play with two different groups of proposers, either those that were relatively fair (a tendency to propose an equal split-the high expectation condition) or unfair (with a history of offering unequal splits-the low expectation condition). After playing these 40 UG rounds, they then played 40 Dictator Games (DG) as allocator with the same set of partners. The results showed that expectations affect UG decisions, with a greater proportion of unfair offers rejected from the high as compared to the low expectation group, suggesting that players utilize specific expectations of social interaction as a behavioral reference point. Importantly, this was evident within subjects. Interestingly, we also demonstrated that these expectation effects carried over to the subsequent DG. Participants allocated more money to the recipients of the high expectation group as well to those who made equal offers and, in particular, when the latter were expected to behave unfairly, suggesting that people tend to forgive negative violations and appreciate and reward positive violations. Therefore, both the expectations of others' behavior and their violations play an important role in subsequent allocation decisions. Together, these two studies extend our knowledge of the role of expectations in social decision making.

The neural bases of anger are still a matter of debate. In particular we do not know whether anger perception and anger experience rely on similar or different neural mechanisms. To study this topic, we performed activation-likelihood-estimation meta-analyses of human neuroimaging studies on 61 previous studies on anger perception and experience. Anger perception analysis resulted in significant activation in the amygdala, the right superior temporal gyrus, the right fusiform gyrus and the right IFG, thus revealing the role of perceptual temporal areas for perceiving angry stimuli. Anger experience analysis resulted in the bilateral activations of the insula and the ventrolateral prefrontal cortex, thus revealing a role for these areas in the subjective experience of anger and, possibly, in a subsequent evaluation of the situation. Conjunction analyses revealed a common area localized in the right inferior frontal gyrus, probably involved in the conceptualization of anger for both perception and experience. Altogether these results provide new insights on the functional architecture underlying the neural processing of anger that involves separate and joint mechanisms. According to our tentative model, angry stimuli are processed by temporal areas, such as the superior temporal gyrus, the fusiform gyrus and the amygdala; on the other hand, the subjective experience of anger mainly relies on the anterior insula; finally, this pattern of activations converges in the right IFG. This region seems to play a key role in the elaboration of a general meaning of this emotion, when anger is perceived or experienced., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021. Published by Elsevier Inc.)

Emotional acceptance is an important emotion regulation strategy promoted by most psychotherapy approaches. We adopted the Activation Likelihood Estimation technique to obtain a quantitative summary of previous fMRI (functional Magnetic Resonance Imaging) studies of acceptance and test different hypotheses on its mechanisms of action. The main meta-analysis included 13 experiments contrasting acceptance to control conditions, yielding a total of 422 subjects and 170 foci of brain activity. Additionally, subgroups of studies with different control conditions (react naturally or focus on emotions) were identified and analysed separately. Our results showed executive areas to be affected by acceptance only in the subgroup of studies in which acceptance was compared to natural reactions. In contrast, a cluster of decreased brain activity located in the posterior cingulate cortex (PCC)/precuneus was associated with acceptance regardless of the control condition. These findings suggest that high-level executive cortical processes are not a distinctive feature of acceptance, whereas functional deactivations in the PCC/precuneus constitute its specific neural substrate. The neuroimaging of emotional acceptance calls into question a key tenet of current neurobiological models of emotion regulation consisting in the necessary involvement of high-level executive processes to actively modify emotional states, suggesting a complementary role for limbic portions of the default system., (© The Author(s) 2021. Published by Oxford University Press.)

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., (© 2020 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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.

Shame plays a fundamental role in the regulation of our social behavior. One intriguing question is whether amygdala might play a role in processing this emotion. In the present single-case study, we tested a patient with acquired damage of bilateral amygdalae and surrounding areas as well as healthy controls on shame processing and other social cognitive tasks. Results revealed that the patient's subjective experience of shame, but not of guilt, was more reduced than in controls, only when social standards were violated, while it was not different than controls in case of moral violations. The impairment in discriminating between normal social situations and violations also emerged. Taken together, these findings suggest that the role of the amygdala in processing shame might reflect its relevance in resolving ambiguity and uncertainty, in order to correctly detect social violations and to generate shame feelings., (Copyright © 2020 Piretti, Pappaianni, Lunardelli, Zorzenon, Ukmar, Pesavento, Rumiati, Job and Grecucci.)

Anxiety is a mental state characterized by an intense sense of tension, worry or apprehension, relative to something adverse that might happen in the future. Researchers differentiate aspects of anxiety into state and trait, respectively defined as a more transient reaction to an adverse situation, and as a more stable personality attribute in experiencing events. It is yet unclear whether brain structural and functional features may distinguish these aspects of anxiety. To study this, we assessed 42 healthy participants with the State-Trait Anxiety Inventory and then investigated with MRI to characterize structural grey matter covariance and resting-state functional connectivity (rs-FC). We found several differences in the structural-functional patterns across anxiety types: (1) trait anxiety was associated to both structural covariance of Default Mode Network (DMN), with an increase in dorsal nodes and a decrease in its ventral part, and to rs-FC of DMN within frontal regions; (2) state anxiety, instead, was widely related to rs-FC of Salience Network and of DMN, specifically in its ventral nodes, but not associated with any structural pattern. In conclusion, our study provides evidence of a neuroanatomical and functional distinction between state and trait anxiety. These neural features may be additional markers in future studies evaluating early diagnosis or treatment effects.

(1) Background: Recently, a series of clinical neuroimaging studies on fibromyalgia (FM) have shown a reduction in cortical volume and abnormally high glutamate (Glu) and glutamate + glutamine (Glx) levels in regions associated with pain modulation. However, it remains unclear whether the volumetric decreases and increased Glu levels in FM are related each other. We hypothesized that higher Glu levels are related to decreases in cortical thickness (CT) and volume in FM patients. (2) Methods: Twelve females with FM and 12 matched healthy controls participated in a session of combined 3.0 Tesla structural magnetic resonance imaging (MRI) and single-voxel MR spectroscopy focused on the thalami and ventrolateral prefrontal cortices (VLPFC). The thickness of the cortical and subcortical gray matter structures and the Glu/Cr and Glx/Cr ratios were estimated. Statistics included an independent t -test and Spearman's test. (3) Results: The Glu/Cr ratio of the left VLPFC was negatively related to the CT of the left inferior frontal gyrus (pars opercularis ( p = 0.01; r = -0.75) and triangularis ( p = 0.01; r = -0.70)). Moreover, the Glx/Cr ratio of the left VLPFC was negatively related to the CT of the left middle anterior cingulate gyrus ( p = 0.003; r = -0.81). Significantly lower CTs in FM were detected in subparts of the cingulate gyrus on both sides and in the right inferior occipital gyrus ( p < 0.001). (4) Conclusions: Our findings are in line with previous observations that high glutamate levels can be related, in a concentration-dependent manner, to the morphological atrophy described in FM patients.

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., (Copyright © 2020 Grecucci, Messina, Amodeo, Lapomarda, Crescentini, Dadomo, Panzeri, Theuninck and Frederickson.)

Emotion regulation plays a crucial role in an individual's well-being, as it is known that deficits in regulating emotions can lead to psychological and psychiatric disorders. Cognitive reappraisal is widely considered to be an adaptive and effective emotion-regulation strategy. People are more or less able to apply it, but it is still not clear how reappraisal affects brain structures and the psychological profile of individuals. In our study we thus aimed to explore the impact of applying reappraisal at both the neural and the psychological level. Source-based morphometry (SBM), a whole-brain multivariate method based on the Independent Component Analysis that extracts patterns of covariation of gray matter ("independent networks"), was applied to the MRI images of 37 participants. In order to enrich their psychological profiles, we measured their experienced affectivity (PANAS) and their empathic abilities (IRI). Based on the frequency of applying reappraisal (ERQ), participants were divided into low and high reappraisers (18 vs. 19). An independent source of gray matter emerged as being different between the groups: specifically, low reappraisers showed more gray matter volume concentration in a network including the frontal, temporal, and parietal regions as compared to high reappraisers. At the psychological level, low reappraisers reported a more strongly experienced negative affect, while no difference among reappraisers emerged with regard to empathic abilities. Capitalizing on a multivariate method for structural analysis that is innovative in this field, this study extends previous observations on individual differences in the ability to regulate emotions, and it describes a plausible impact of reappraisal on brain structures and affectivity.

Background: Whilst there has been extensive study of the mechanisms underlying the regulation for pictures, the ability and the mechanisms beyond the regulation of words remains to be clarified. Similarly, the effect of strategy when applying a regulatory process is still poorly explored. The present study seeks to elucidate these issues comparing the effect of regulation and of strategy to both neutral and emotional words and pictures., Methodology/principal Findings: Thirty young adults applied the strategy of distancing to the emotions elicited by unpleasant and neutral pictures and words while their subjective ratings and ERPs were recorded. At a behavioral level, participants successfully regulated the arousal and the valence of both pictures and words. At a neural level, unpleasant pictures produced an increase in the late positive potential modulated during the regulate condition. Unpleasant linguistic stimuli elicited a posterior negativity as compared to neutral stimuli, but no effect of regulation on ERP was detectable. More importantly, the effect of strategy independently of stimulus type, produced a significant larger Stimulus Preceding Negativity. Dipole reconstruction localized this effect in the middle frontal areas of the brain., Conclusions: As such, these new psychophysiological findings might help to understand how pictures and words can be regulated by distancing in daily life and clinical contexts, and the neural bases of the effect of strategy for which we suggest an integrative model., Competing Interests: The authors have declared that no competing interests exist.

This study evaluated whether the impairment in cooperation that characterizes individuals with borderline personality disorder (BPD) can be explained by the difficulty to use emotion regulation strategies and to accurately perceive the fairness of others' behavior. Forty-one patients with BPD and 41 sex and age matched healthy controls (HC) played the responder's role in a Modified Ultimatum Game during which they were asked to apply 3 different emotion regulation strategies: look, distancing, and reappraisal. Offer rejection rates were used as an index of punishment behavior. After the experiment, participants also rated the degree of perceived equity of the offers after receiving fair and unfair offers. Reappraisal was effective in decreasing punishment behaviors for unfair offers in both the BPD and HC groups. By contrast, BPD patients displayed a different behavior than HC when making decisions upon fair offers, independently from the regulation strategies adopted. In fact, they rejected higher rates of fair offers than HC. Further, BPD patients judged fair offers as less fair than HC. This indicates an altered judgment and decision making on fair interpersonal exchanges. In conclusion, BPD patients exhibit increased punishment behavior during fair, "favorable" social exchanges, which they tend to perceive as less fair than controls. Thus, BPD patients may be biased toward underestimating positive feedback from others. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Despite the traditional view of Schizophrenia (SZ) and Bipolar disorder (BD) as separate diagnostic categories, the validity of such a categorical approach is challenging. In recent years, the hypothesis of a continuum between Schizophrenia (SZ) and Bipolar disorder (BD), postulating a common pathophysiologic mechanism, has been proposed. Although appealing, this unifying hypothesis may be too simplistic when looking at cognitive and affective differences these patients display. In this paper, we aim to test an expanded version of the continuum hypothesis according to which the continuum extends over three clusters: the psychotic, the cognitive, and the affective. We applied an innovative approach known as Source-based Morphometry (SBM) to the structural images of 46 individuals diagnosed with SZ, 46 with BD and 66 healthy controls (HC). We also analyzed the psychological profiles of the three groups using cognitive, affective, and clinical tests. At a neural level, we found evidence for a shared psychotic core in a distributed network involving portions of the medial parietal and temporo-occipital areas, as well as parts of the cerebellum and the middle frontal gyrus. We also found evidence of a cognitive core more compromised in SZ, including alterations in a fronto-parietal circuit, and mild evidence of an affective core more compromised in BD, including portions of the temporal and occipital lobes, cerebellum, and frontal gyrus. Such differences were confirmed by the psychological profiles, with SZ patients more impaired in cognitive tests, while BD in affective ones. On the bases of these results we put forward an expanded view of the continuum hypothesis, according to which a common psychotic core exists between SZ and BD patients complemented by two separate cognitive and affective cores that are both impaired in the two patients' groups, although to different degrees., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)