Borderline personality disorder classification based on brain network measures during emotion regulation

Henk Cremers*, Linda van Zutphen, Sascha Duken, Gregor Domes, Andreas Sprenger, Lourens Waldorp, Arnoud Arntz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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
Original languageEnglish
Pages (from-to)1169–1178
Number of pages10
JournalEuropean Archives of Psychiatry and Clinical Neuroscience
Issue number6
Early online date2 Dec 2020
Publication statusPublished - Sept 2021


  • Borderline personality disorder
  • Classification
  • HUBS
  • Machine learning
  • Network measures
  • Networks analysis
  • Phasic vs
  • tonic brain connectivity


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