Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.
J Neurosci Res. 2022 Dec;100(12):2187-2200. doi: 10.1002/jnr.25121. Epub 2022 Sep 7.
There is solid evidence for the prominent involvement of the central autonomic and default mode systems in shaping personality. However, whether functional connectivity of these systems can represent neural correlates and predictors of individual variation in personality traits is largely unknown. Resting-state functional magnetic resonance imaging data of 215 healthy young adults were used to construct the sympathetic (SN), parasympathetic (PN), and default mode (DMN) networks, with intra- and internetwork functional connectivity measured. Personality factors were assessed using the five-factor model. We examined the associations between personality factors and functional network connectivity, followed by performance of personality prediction based on functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. All personality factors (neuroticism, extraversion, conscientiousness, and agreeableness) other than openness were significantly correlated with intra- and internetwork functional connectivity of the SN, PN, and DMN. Moreover, the CPM models successfully predicted conscientiousness and agreeableness at the individual level using functional network connectivity. Our findings may expand existing knowledge regarding the neural substrates underlying personality.
有确凿的证据表明，中枢自主和默认模式系统在塑造个性方面起着重要作用。然而，这些系统的功能连接是否可以代表人格特质个体差异的神经相关性和预测因素，在很大程度上尚不清楚。本研究使用 215 名健康年轻成年人的静息态功能磁共振成像数据构建了交感（SN）、副交感（PN）和默认模式（DMN）网络，并测量了网络内和网络间的功能连接。使用五因素模型评估人格因素。我们检查了人格因素与功能网络连接之间的关联，然后使用基于连接组的预测建模（CPM），这是一种最近开发的机器学习方法，基于功能连接进行人格预测。除开放性外，所有人格因素（神经质、外向性、尽责性和宜人性）均与 SN、PN 和 DMN 的网络内和网络间功能连接显著相关。此外，CPM 模型使用功能网络连接成功地预测了个体的尽责性和宜人性。我们的研究结果可能会扩展对人格的神经基础的现有认识。
