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

作者：Alessandro Grecucci, Miriam Langerbeck, Richard Bakiaj, Parisa Ahmadi Ghomroudi, Davide Rivolta, Xiaoping Yi, Irene Messina · 发表于：Frontiers in Human Neuroscience · 年份：2025 · DOI：10.3389/fnhum.2025.1589440 · 被引用次数：9 · 研究领域：Functional Brain Connectivity Studies、Personality Disorders and Psychopathology、Mental Health Research Topics

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 min...