Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis.

INTRODUCTION

Borderline personality disorder (BPD) is one of the most frequently diagnosed disorders in psychiatric settings. Beyond the categorical diagnosis, borderline personality traits (BPT) are common in the general population and vary along a continuum from mild to severe. While prior research has reported functional connectivity alterations in the default mode network (DMN), the salience network (SN), and the central-executive network (CEN) in patients with BPD, the impairment of these networks in subclinical BPT remain underexplored. To fill this gap, this study aims to investigate dynamic functional connectivity alterations associated with BPT in a subclinical population. We expect to find abnormal connectivity inside the DMN, the SN and in regions ascribed to mentalization processes associated with BPT. We also expect these networks to be associated with psychological symptoms experienced by borderline patients such as impulsivity and anger issues, as well as lack of self-control and neuroticism among others.

METHOD

An unsupervised machine learning method known as Group-ICA, was applied to resting state fMRI images of 200 individuals to predict BPT from the temporal variability of independent macro networks.

RESULTS

Results indicated abnormal dynamic functional connectivity inside the SN including areas implicated in emotional reactivity and sensitivity, and in a network that partially overlaps with the DMN, including regions involved in social cognition and mind reading. Specifically, the higher the BPT, the higher the temporal variability inside the SN, and the lower the temporal variability in a network that includes DMN and mentalization regions. Notably, the BOLD variability of the SN correlated with neuroticism, anger problems, lack of self- control, and distorted inner dialogue, all symptoms displayed by individuals with borderline personality.

DISCUSSION

These findings indicate that abnormalities in resting state networks are visible in subclinical populations with varying degrees of borderline traits, with impaired DMN and SN. These insights may pave the way for designing interventions to prevent the development of the full disorder.