Faculty of Psychology and Neuroscience (FPN), Maastricht University, Netherlands.
Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.
Neuroimage Clin. 2023;40:103530. doi: 10.1016/j.nicl.2023.103530. Epub 2023 Oct 14.
Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.
边缘型人格障碍（BPD）是临床环境中诊断最多的疾病之一。除了完全诊断出的疾病外，边缘型人格特质（BPT）在普通人群中也很常见。先前的研究已经调查了 BPD 的神经相关性，但没有调查 BPT 的神经相关性。本文使用一种称为核脊回归（KRR）的监督机器学习方法来构建预测模型，研究了亚临床人群中 BPT 的神经相关性。此外，我们还想确定涉及 BPD 的相同脑区是否也涉及亚临床 BPT。最近，人们试图描述静息状态衍生的宏观网络在 BPD 中的特定作用，强调了默认模式网络的作用。然而，目前尚不清楚这是否适用于亚临床人群。最后，我们想验证这样一个假设，即预测 BPT 的相同电路也可以预测戏剧型人格特质。戏剧型人格有时被认为是 BPD 的一种较轻形式，两者之间的鉴别诊断可能很困难。这是第一次将 KRR 应用于 135 个人的结构图像，以基于整个大脑、先前发现可以正确分类 BPD 的电路以及五个宏观网络来预测 BPT。在全脑水平上，结果表明，额叶和顶叶区域以及 Heschl 区、丘脑、扣带和脑岛能够预测边缘特质。当仅考虑源于 BPD 研究的大脑电路限制区域时，BPT 预测会增加，这证实了亚临床和临床样本之间的大脑结构存在一定重叠。在所有五个宏观网络中，只有 DMN 成功预测了 BPD，这证实了之前关于其在 BPD 中的作用的观察结果。戏剧型特质不能通过 BPT 电路来预测。这些结果对 BPD 的诊断和人格的维度模型具有启示意义。
