According to previous research, borderline personality disorder (BPD) is associated with high cost-of-illness. However, there is still a shortage of cost-of-illness-studies assessing costs from a broad societal perspective, including direct and indirect costs. Further, there are considerable differences in the results among the existing studies. In the present study, 167 German men and women seeking specialized outpatient treatment for BPD were included. We assessed societal cost-of-illness bottom-up through structured face-to-face interviews and encompassed a wide range of cost components. All costs were calculated for the 2015 price level. Cost-of-illness amounted to € 31,130 per patient and year preceding disorder-specific outpatient treatment. € 17,044 (54.8%) were direct costs that were mostly related to hospital treatment. Indirect costs amounted to € 14,086 (45.2%). Within indirect costs, costs related to work disability were the most crucial cost driver. The present study underlines the tremendous economic burden of BPD. According to the present study, both the direct and indirect costs are of significant importance for the societal costs associated with BPD. Besides the need for more disorder-specific treatment facilities for men and women with BPD, we assume that education and employment are topics that should be specifically targeted and individually supported at an early stage of treatment.Trial Registration: German Clinical Trial Registration, DRKS00011534, Date of Registration: 11/01/2017, retrospectively registered.

Emotion instability in borderline personality disorder (BPD) has been associated with an impaired fronto-limbic inhibitory network. However, functional connectivity (FC) underlying altered emotion regulation in BPD has yet to be established. Here, we used resting-state fMRI to investigate enduring effects of effortful emotion regulation on the amygdala intrinsic FC in BPD. In this multicenter study, resting-state fMRI was acquired before and after an emotion regulation task in 48 BPD patients and 39 non-patient comparison individuals. The bilateral amygdalae were used as a seed in the whole-brain FC analysis and two-way mixed ANOVA to test whether BPD patients exhibited weaker post-task increase in the amygdala intrinsic FC with the prefrontal cortex (PFC), compared to non-patients. Subsequently, we explored whether the results are common for personality disorders characterized by emotional problems, using additional data of 21 cluster-C personality disorder patients. In contrast to non-patients, BPD patients failed to show increased post-task amygdala resting-state FC with the medial, dorsolateral, ventrolateral PFC, and superior temporal gyrus, but surprisingly exhibited decreased FC with the posterior cingulate cortex and increased FC with the superior parietal lobule. In BPD patients, the emotion regulation task failed to increase resting-state amygdala FC with brain regions essential for effortful emotion regulation, which suggests: (a) altered cognitive control typically used to indirectly alleviate distress by reinterpreting the meaning of emotional stimuli; (b) impaired direct regulation of emotional responses, which might be common for personality disorders;

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.