Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality,Journal of Neuroscience Research

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Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality
Journal of Neuroscience Research ( IF 2.9 ) Pub Date : 2022-09-07 , DOI: 10.1002/jnr.25121
Yating Li _{1,

2,

3} , Huanhuan Cai _{1,

2,

3} , Xueying Li _{1,

2,

3} , Yinfeng Qian _{1,

2,

3} , Cun Zhang _{1,

2,

3} , Jiajia Zhu _{1,

2,

3} , Yongqiang Yu _{1,

2,

3}

Affiliation

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.

中文翻译：

中央自主网络和默认模式网络的功能连通性代表了个体人格的神经关联和预测因子

有确凿的证据表明，中枢自主系统和默认模式系统在塑造人格方面发挥着重要作用。然而，这些系统的功能连通性是否可以代表个性特征的神经相关性和个体差异的预测因子在很大程度上是未知的。215 名健康年轻人的静息态功能磁共振成像数据被用于构建交感神经 (SN)、副交感神经 (PN) 和默认模式 (DMN) 网络，并测量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通性之间的关联，然后使用基于连接组的预测模型 (CPM) 基于功能连通性进行人格预测，最近开发的机器学习方法。除开放性外，所有人格因素（神经质、外向性、责任心和宜人性）都与 SN、PN 和 DMN 的网络内和网络间功能连通性显着相关。此外，CPM 模型使用功能性网络连接成功地预测了个人层面的责任心和宜人性。我们的发现可能会扩展有关人格背后的神经基质的现有知识。CPM 模型使用功能性网络连接成功地预测了个人层面的责任心和宜人性。我们的发现可能会扩展有关人格背后的神经基质的现有知识。CPM 模型使用功能性网络连接成功地预测了个人层面的责任心和宜人性。我们的发现可能会扩展有关人格背后的神经基质的现有知识。

更新日期：2022-09-07

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