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.

Facial emotion recognition deficits and alexithymia in borderline, narcissistic, and histrionic personality disorders

Previous studies that aimed to support emotion recognition deficits and alexithymia in B cluster personality disorders have mainly focused on borderline personality disorder (BPD), and resulted in mixed findings. In our study we examine emotion recognition and alexithymia in patients with histrionic (HPD), narcissistic (NPD) and borderline (BPD) personality disorders compared to each other and healthy controls. Furthermore, the possibility is investigated that it is not the type of PD but the severity of psychopathology which predicts the severity of emotion recognition deficits and alexithymia. Patients with HPD, NPD, BPD and healthy controls (N = 20 for each group) were examined by using the Ekman 60 Faces Test (FEEST) and the Toronto Alexithymia Scale (TAS-20). To measure the extent and severity of psychopathology, the Symptom-Checklist-90 Revised (SCL-90-R) was used. Patient groups performed significantly worse compared to healthy controls on the Ekman test and TAS-20, while we found no significant differences among patient groups in emotion recognition and alexithymia. Furthermore, higher scores on the SCL-90-R predicted poorer emotion recognition performance and higher alexithymic features. The empirical data supports the conclusion that the severity of psychopathology plays an important role in predicting emotion recognition deficits and alexithymia in borderline, narcissistic, and histrionic personality disorders.