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

Childhood adversity modulates structural brain changes in borderline personality but not in major depression disorder

Adverse childhood experiences (ACEs) negatively affect the function and structure of emotion brain circuits, increasing the risk of various psychiatric disorders. It is unclear if ACEs show disorder specificity with respect to their effects on brain structure. We aimed to investigate whether the structural brain effects of ACEs differ between patients with major depression (MDD) and borderline personality disorder (BPD). These disorders share many symptoms but likely have different etiologies. To achieve our goal, we obtained structural 3T-MRI images from 20 healthy controls (HC), 19 MDD patients, and 18 BPD patients, and measured cortical thickness and subcortical gray matter volumes. We utilized the Adverse Childhood Experiences (ACE) questionnaire to quantify self-reported exposure to childhood trauma. Our findings suggest that individuals with MDD exhibit a smaller cortical thickness when compared to those with BPD. However, ACEs showed a significantly affected relationship with cortical thickness in BPD but not in MDD. ACEs were found to be associated with thinning in cortical regions involved in emotional behavior in BPD, whereas HC showed an opposite association. Our results suggest a potential mechanism of ACE effects on psychopathology involving changes in brain structure. These findings highlight the importance of early detection and intervention strategies.

Exploring the connection between childhood trauma, dissociation, and borderline personality disorder in forensic psychiatry: a comprehensive case study

This case study examines the complex relationship between childhood trauma, dissociation, and Borderline Personality Disorder (BPD) within the context of forensic psychiatry. It focuses on a young murder defendant named "Paul," who has experienced various traumatic events, including childhood maltreatment and domestic violence. These experiences have led to dissociative states marked by high emotional intensity, particularly of an aggressive nature, and impaired impulse control, resulting in violent behavior during dissociative episodes. The study employs advanced assessment tools like Raven's Standard Progressive Matrices (SPM), the Millon Clinical Multiaxial Inventory-III (MCMI-III), and the Level of Service/Case Management Inventory (LS/CMI) to gain a comprehensive understanding of Paul's psychopathological condition, risk factors, and rehabilitation needs. The LS/CMI assessment highlights a high risk of recidivism, mainly influenced by family relationships, educational challenges, interpersonal connections, and aggressive tendencies. To address the multifaceted needs of individuals like Paul, the study emphasizes the importance of using transdiagnostic models for trauma and dissociation. This approach informs tailored treatment programs that include processing past traumatic experiences, improving self-identity, nurturing healthy relational patterns, and enhancing emotional regulation. Although this study is based on a single case, it serves as a model for integrating assessment tools and theoretical-clinical models in the field of forensic psychiatry. Understanding the intricate dynamics of childhood trauma, dissociation, and BPD is crucial for making informed decisions, conducting risk assessments, and developing rehabilitation programs within the justice system. Future research should expand the scope of cases and further validate assessment tools to advance our understanding of this complex relationship.

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.

"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.

A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists

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.

Alteration of surface morphology and core features in adolescents with borderline personality disorder

BACKGROUND

Accurate early diagnosis of adolescent borderline personality disorder (BPD) is critical for prompt treatment. The aim of this study was to assess the alteration of brain surface morphology and to evaluate its relationship with core features in adolescent BPD.

METHODS

A total of 52 adolescents with BPD aged 12-17 years and 39 age- and sex-matched healthy controls (HCs) were prospectively enrolled into the study. Brain magnetic resonance imaging (MRI) was obtained with both 3D-T1 weighted structural sequence and resting-state functional data. The structural data was analyzed for surface morphology parameters including the local gyrification index (LGI), mean curvature and surface area. The functional MRI data was analyzed for seed-based functional connectivity (FC). Correlative analysis of surface morphology and core features of adolescent BPD was performed.

RESULTS

Adolescents with BPD showed the following altered surface morphology in the limbic-cortical circuit when compared to the HCs: (1) reduced LGI in the left fusiform and right superior temporal gyrus; (2) reduced mean curvature in the left precentral gyrus and right rostral anterior cingulate cortex, and increased mean curvature in the bilateral pericalcarine; and (3) reduced surface area in the left paracentral gyrus, left pars triangularis, right insula and right lateral orbitofrontal gyrus (P < 0.05, FWE correction). In addition, these brain regions with altered surface morphology were significantly correlated with several core features including the mood instability, self-identity problems, and non-suicidal self-injury behavior in adolescents with BPD (P < 0.05). Furthermore, there was enhanced functional connectivity among these altered brain regions within the limbic-cortical circuit (voxel P < 0.001, cluster P < 0.05, FWE corrected).

CONCLUSIONS

Adolescents with BPD had significant alterations of brain surface morphology in the limbic-cortical circuit, which was correlated with core BPD features. These results implicated the surface morphology parameters and FC alterations may potentially serve as neuroimaging biomarkers for adolescents with BPD.

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.

Altered brain activity and childhood trauma in Chinese adolescents with borderline personality disorder

BACKGROUND

Childhood trauma may cause borderline personality disorder (BPD). The aim of this study was to assess functional alteration and its association with childhood trauma in Chinese adolescents with BPD.

METHODS

A total of 187 adolescents with BPD aged 12-17 years and 207 age and gender- matched healthy controls (HCs) were enrolled into this study. The sample consisted of 50 adolescents with BPD and 21 HCs underwent brain resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data was analyzed for both neural activity as indicated by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC). Clinical assessment for childhood trauma, impulsivity, and depression was also performed. Correlative analysis of functional alterations with childhood trauma assessment were performed.

RESULTS

Adolescents with BPD had significantly higher rate of all assessed childhood trauma than the HC group (P < 0.001). Most adolescents with BPD (61.5 %) had emotional neglect, which was the most commonly seen type of childhood trauma. Compared with HCs, adolescents with BPD showed decreased ALFF in the cortical regions including the left superior frontal gyrus and right middle occipital gyrus, and default mode network (DMN) regions including the left angular gyrus and medial superior frontal gyrus. Adolescents with BPD also showed enhanced ALFF in the limbic system (left hippocampus, insula, thalamus) (P < 0.05, FWE correction, cluster size ≥100). There were significant correlations between the insula ALFF and childhood trauma assessment for emotional neglect, physical abuse and physical neglect (P < 0.01). Moreover, adolescents with BPD showed increased FC between the left insula and right cortical regions (voxel P < 0.001, cluster P < 0.05, FWE correction).

LIMITATIONS

The sample size was small. This cohort had patients with more severe BPD symptoms and some had comorbidities such as anxiety and obsessive-compulsive disorder.

CONCLUSIONS

There were alterations of brain activity as indicated by ALFF in the limbic - cortical circuit and DMN regions in adolescents with BPD and the activity in the left insula was correlated with emotional neglect. In addition, the FC between the left insula and the limbic - prefrontal circuit was enhanced. These results implicate that the functional alterations of insula may serve as a potential neuroimaging biomarker for adolescents with BPD who suffered from childhood trauma.

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.

Neuroanatomical predictors of real‐time fMRI ‐based anterior insula regulation. A supervised machine learning study

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.

Therapist reactions to patient personality: A pilot study of clinicians’ emotional and neural responses using three clinical vignettes from in treatment series

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.