Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
Biol Psychiatry. 2024 Sep 15;96(6):486-494. doi: 10.1016/j.biopsych.2024.02.1016. Epub 2024 Mar 7.
Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents.
We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity.
Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA p = .001) and older adolescents (HCP-D p = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth.
Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.
边缘型人格障碍（BPD）的症状通常在青春期表现出来，但这些使人衰弱的症状与功能性大脑网络的发展之间的潜在关系尚不清楚。在这里，我们旨在研究大样本的年轻成年人和青少年中，功能连接的多元模式如何与边缘型人格特质相关。
我们使用了来自 HCP-YA（人类连接组计划青年）（n=870，年龄 22-37 岁，457 名女性）和 HCP-D（人类连接组计划发展）（n=223，年龄 16-21 岁，121 名女性）的年轻成年人和青少年的功能磁共振成像数据。一个经过验证的 BPD 代理评分是从 NEO 五因素量表中得出的。在 HCP-YA 中，使用交叉验证和嵌套超参数调整的岭回归模型对预测 BPD 评分的区域功能连接进行了训练和测试。在没有进一步调整的情况下，将训练好的模型应用于 HCP-D 中的数据进行测试。最后，我们测试了与 BPD 相关的连接模式如何与连接的年龄相关变化一致。
多元功能连接模式在年轻成年人（HCP-YA p=0.001）和年龄较大的青少年（HCP-D p=0.001）的未见数据中显著预测了 BPD 评分。区域预测能力具有异质性；最具预测性的区域位于与情绪调节和执行功能相关的功能系统中，包括腹侧注意力网络。最后，预测 BPD 评分的区域功能连接模式与与青少年发展相关的模式一致。
发育敏感区域的功能连接个体差异与边缘型人格特质有关。
