Diagnosis of borderline personality disorder based on Cyberball social exclusion task and resting-state fMRI: using machine learning approach as an auxiliary tool

Abstract

ABSTRACT To diagnose patients with borderline personality disorder (BPD) based on Cyberball social exclusion task and resting-state functional magnetic resonance imaging (fMRI) using machine learning approach. In the current study, the researchers used fMRI images to examine social brain function and learning in BPD. Thirty-six participants completed the ‘Cyberball’ task. Data questionnaire and features extracted from fMRI data were used to diagnose BPD. In this study, three statistical models were used to diagnosing BPD, and the best model was introduced based on appropriate criteria. Also, important features are identified by the models. Totally, 20 people had BPD and 16 were healthy. 83.3% were women and 16.7% men. Logistic Lasso Regression (LLR) was the best model for the diagnosis of patients with BPD. Physical abuse, sexual abuse and the use of antidepressants and antipsychotic drugs were selected as important features by the models. Due to the structure of the machine learning models used in the study, there is no need to feature selection stage and important features can be identified in the models. Also, the diagnosis of BPD has been done with high accuracy, so that clinical physicians can diagnose BPD with all available information, including questionnaire information and fMRI data.