Shaping psychotherapy trainees' potential: insights from training program directors.

The role of the therapist has received growing attention in psychotherapy research, suggesting that training effectiveness may also depend on the person of the trainees, with relevant implications in terms of candidate selection or tailoring training to the person. In the present study, we focused on how and how much psychotherapy training can be effective in fostering trainees' characteristics associated with successful therapists and contrast trainees' characteristics that could represent limitations as therapists. The aim was to explore training program directors' perspectives on individual trainees' limitations and strengths, and on the effectiveness of training in shaping successful therapists. To this aim, we interviewed 14 training program directors with different psychotherapy approaches. Audio recordings of these semi-structured interviews were transcribed verbatim and analysed using NVivo software. According to our findings, transversally to different psychotherapy approaches, trainees' self-awareness and the ability to embrace uncertainty were recognized as the main characteristics of good trainees. Four training elements were fre- quently mentioned by directors as effective in shaping trainees' development: experiential learning, the use of paradigm that pri- oritizes self-awareness (instead of technique-focused approaches), the centrality of supervision and inter-vision, the trainer-trainee relationship. These insights highlight the importance of trainees' self-awareness and the ability to embrace uncertainty as potential personal variables that may influence the effectiveness of future psychotherapists and suggest giving attention to experiential learning and training relationships as crucial elements of psychotherapists' development during the training.

Assessing and recovering Alzheimer's disease: a comparative analysis of standard neuropsychological approaches and virtual reality interventions with the use of digital storytelling.

Background: Alzheimer's disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder that predominantly affects the elderly population. Traditional assessment methods, including neuropsychological tests like the MMSE, have been the cornerstone of AD diagnosis for decades. These methods are grounded in a wealth of research and clinical experience, providing a robust framework for understanding the cognitive deficits of AD. The evolution of AD assessment and rehabilitation has recently been tackled with the introduction of Virtual Reality (VR) technologies. Objectives: To evaluate the use of storytelling and reminiscence therapy in virtual reality programs as a complementary and enhancing modality alongside standard assessment and rehabilitation for Alzheimer's patients. To explore how regular interaction with VR narratives can slow cognitive decline or improve relevant features of cognitive functioning over the time. To propose a new assessment and rehabilitative tool based on the use of VR and digital storytelling. Method: A comparative analysis of Standard Neuropsychological Approaches and Virtual Reality Interventions in patients with Alzheimer disorder was carried out. A literature overview on the empirical studies between 2019 and 2024 was conducted. Results: We propose a new VR-based setup mediated by the use of storytelling for the assessment and recovery of AD. Conclusion: The employment of storytelling within VR programs for the assessment and rehabilitation of Alzheimer's disease can positively impact both the cognitive and emotional realms of patients, with beneficial outcomes on caregivers' and families' burden. The successful implementation of this approach requires careful consideration of accessibility, data interpretation, and standard validation protocols.

Decoding acceptance and reappraisal strategies from resting state macro networks.

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.

Group psychological counseling to contrast academic burnout: a research protocol for a randomized controlled trial.

Academic burnout is a condition characterized by exhaustion, cynicism, a distant attitude toward studying, and diminished self-efficacy in academic activities. Preliminary scientific findings indicate that interventions designed to alleviate work burnout also hold promise for mitigating academic burnout, however clear evidence based on randomized controlled trials is still missing. This research protocol describes a randomized controlled trial aimed at evaluating the efficacy of an online group psychological intervention to contrast academic burnout. Participants with high levels of burnout will be assigned to a psychological counseling group or a waiting list control group. The research comprises several phases: (T0) Screening, Recruitment, and Randomization; (T1) Baseline assessment (pre-intervention); (T2) Outcome Assessment (post-intervention); and (T3) Follow-up Assessment (3 months post-intervention). The primary outcomes include burnout symptoms, general wellbeing, and academic achievement. Additionally, secondary variables such as effort-reward imbalances, satisfaction/frustration of basic psychological needs, intrapersonal and interpersonal emotion regulation, coping strategies, and social support will be examined. The psychological intervention strategies will encompass psychoeducation, self-awareness enhancement, cognitive restructuring, and promotion of social support. This research protocol is an initial step toward evidence-based psychological interventions to treat academic burnout.

Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach.

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.

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.

Comparing reappraisal and acceptance strategies to understand the neural architecture of emotion regulation: a meta-analytic approach.

Introduction: In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of executive functions. A case in point is acceptance-based strategies. Method: To better understand neural commonalities and differences behind different emotion regulation processes, in the present study, we applied the Activation Likelihood Estimation (ALE) method to perform a meta-analysis on fMRI studies investigating task-related activity of reappraisal and acceptance. Both increased and decreased brain activity was taken into account in the contrast and conjunction analysis between the two strategies. Results: Results showed increased activity in left-inferior frontal gyrus and insula for both strategies, and decreased activity in the basal ganglia for reappraisal, and decreased activity in limbic regions for acceptance. Discussion: These findings are discussed in the context of a model of common and specific neural mechanisms of emotion regulation that support and expand the previous dual-routes models. We suggest that emotion regulation may rely on a core inhibitory circuit, and on strategy-specific top-down and bottom-up processes distinct for different strategies.

"Holding in Anger" as a Mediator in the Relationship between Attachment Orientations and Borderline Personality Features.

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.

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.

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features.

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.

The gradient model of brain organization in decisions involving "empathy for pain".

Influential models of cortical organization propose a close relationship between heteromodal association areas and highly connected hubs in the default mode network. The "gradient model" of cortical organization proposes a close relationship between these areas and highly connected hubs in the default mode network, a set of cortical areas deactivated by demanding tasks. Here, we used a decision-making task and representational similarity analysis with classic "empathy for pain" stimuli to probe the relationship between high-level representations of imminent pain in others and these areas. High-level representations were colocalized with task deactivations or the transitions from activations to deactivations. These loci belonged to 2 groups: those that loaded on the high end of the principal cortical gradient and were associated by meta-analytic decoding with the default mode network, and those that appeared to accompany functional repurposing of somatosensory cortex in the presence of visual stimuli. These findings suggest that task deactivations may set out cortical areas that host high-level representations. We anticipate that an increased understanding of the cortical correlates of high-level representations may improve neurobiological models of social interactions and psychopathology.

Attachment orientations and emotion regulation: new insights from the study of interpersonal emotion regulation strategies.

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.

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.

Difficulties in interpersonal regulation of emotions (DIRE) questionnaire: Psychometric Properties of the Italian Version and Associations with psychopathological symptoms.

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.

Psychotherapist Trainees' Quality of Life: Patterns and Correlates.

While psychotherapists are trained to improve their clients' quality of life, little work has examined the quality of life experienced by psychotherapist trainees themselves. Yet their life satisfactions and stresses would plausibly affect both their ability to learn new skills and conduct psychotherapy. Therefore, in the Society for Psychotherapy Research Interest Section on Psychotherapist Development and Training study, we investigated the patterns of self-reported life quality and their correlates in a multinational sample of 1,214 psychotherapist trainees. A comprehensive questionnaire was used at the outset of trainings to assess trainees' professional background, current life situation, personal characteristics, family background, and social and national origin. The findings indicated 54.3% of trainees' lives could be characterized as fortunate or happy (i.e., experiencing great life satisfaction and not much stress), whereas 14.3% could be characterized as clearly distressed or troubled (i.e., experiencing great life stress and not much satisfaction). The strongest correlates of high life stress, a contributor to poor life quality, were economic insecurity, self-protectiveness, and attachment-related anxiety in relationships, and economic or psychological hardship in childhood. In turn, greater wellbeing was most strongly associated with a warm and open interpersonal style, being married, having sufficient economic means, and material and emotional security in childhood. While the results indicate the majority of therapists experience a relatively good quality of life, the findings also suggest potential targets for increasing trainees' life quality when it may be deficient, such as those on a societal level (e.g., availability of low-cost student loans), training program level (e.g., promoting supportive supervision, positive between-trainee relationships and group collaboration), and individual level (e.g., personal therapy and learning self-care), in order to promote effective learning and therapy practice.

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 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.

Case Report: Individualization of Intensive Transactional Analysis Psychotherapy on the Basis of Ego Strength.

In intensive transactional analysis psychotherapy (ITAP), intensity is obtained with both technical expedients and the relational manner with the patient. In ITAP, the therapist modulates pressure and support commensurately to the patients' ego strength. In the present article, we contrast two clinical cases of young adults in which ego strength produced different therapy outcomes and processes. We present excerpts of the psychotherapy process that illustrates technical aspects of ITAP as well as the therapist's attitude that we describe as holding. We show quantitative therapy outcomes consisting of effects size values of changes in Clinical Outcome in Routine Evaluation-Outcome Measure scores in baseline, treatment, and follow-up phases and qualitative outcome evaluated with the Change Interview at the end of the therapy. In the patient with high ego strength, we observed a rapid improvement and a complete recovery at the end of the therapy, whereas the results of the patient with low ego strength were less consistent (more fluctuations in Clinical Outcome in Routine Evaluation-Outcome Measure scores including deterioration but good qualitative outcome). We conclude that quantitative and qualitative outcome data, together with process observations, are required to have a complete picture of therapy effectiveness. Moreover, we conclude that qualitative ego strength is not a limitation for the use of expressive therapy such as ITAP, but rather, it is an important variable that should be considered to dose confrontations and support.

Psychotherapists' perception of their clinical skills and in-session feelings in live therapy versus online therapy during the COVID-19 pandemic: a pilot study.

During the coronavirus disease 2019 (COVID-19) pandemic, many psychotherapists who were used to seeing their patients in face-toface setting adapted to providing therapies online. In the present pilot study, we investigated therapist current experiences of online therapy compared to live therapy. Twenty-nine therapists completed Clinical Skills, Difficulties in Practice, and in-sessions feelings of Flow, Boredom and Anxiety of the Trainee Current Progress Report, giving a score for each item in two different conditions: Live Therapy and Online Therapy. Compared to Live Therapy, in Online Therapy therapists reported significantly less Clinical Skills, whereas Difficulties in Practice did not differ in the considered conditions. With regard to in-sessions feeling, therapists reported significantly lower scores of Boring and higher scores of Flow in Live Therapy compared to Online Therapy, whereas Anxiety did not differed in the considered conditions.

Interpersonal Emotion Regulation: From Research to Group Therapy.

The concept of interpersonal emotion regulation (IER) refers to a variety of processes in which emotion regulation occurs as part of live social interactions and includes, among others, also those interpersonal interactions in which individuals turn to others to be helped or to help the others in managing emotions. Although IER may be a concept of interest in group therapy, specific theoretical insights in this field appear to be missed. In this article, we firstly provide a review of IER definitions, of classifications of IER strategies, and of IER clinical conceptualizations. Afterwards, we discuss the relevance of considering IER for group therapy, both in terms of non-specific group therapeutic factors and of group therapy techniques promoting adaptive emotion regulation, underlining the potentially relevant role of IER behaviors as intrinsically involved in group experience.