Borderline Personality Disorder Classification Based on Brain Network Centrality During Emotion Regulation

Background: Borderline Personality Disorder is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the key 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. Methods: 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 then entered as separate models in a nested cross-validated linear support vector machine classification analysis. Results: 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 analysis 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. Conclusions: 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 analyses. 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.