Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis.
Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis.
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DOI：
10.1016/j.bbrc.2023.06.012
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发表时间：
2023-06
影响因子：
3.1
通讯作者：
Shen Zhi;Wentao Zhao;Ruiping Wang;Yue-hua Li;Xiao Wang;Sha Liu;Jing Li;Yong Xu
中科院分区：
文献类型：
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作者：
Shen Zhi;Wentao Zhao;Ruiping Wang;Yue-hua Li;Xiao Wang;Sha Liu;Jing Li;Yong Xu
静息状态下大脑的功能连接模式与个体的认知、情感、行为和社会交往密切相关，使其成为以无偏见的方式测量人格特质的重要研究方法，取代传统的纸笔测试。然而，由于大脑的动态特性，年龄引起的功能连接的变化是否可以稳定地映射到人格特质之前还没有研究过。本研究关注与人格特质显著相关的网络特征是否能有效区分不同人格特质的被试，以及这些网络特征在成年不同时期是否存在差异。该研究包括343名健康成年参与者，根据年龄阈值35分为成年早期和成年中期组。收集静息态功能磁共振成像（fMRI）和大五人格问卷。我们调查了人格特质和内在全脑功能连接体之间的关系。然后，我们使用支持向量机（SVM）来评估人格网络特征在区分成年早期样本中高分和低分受试者方面的表现，并在成年中期样本中进行交叉验证。此外，基于边缘的分析（NBS）被用来探讨人格网络的稳定性在两个年龄样本。研究结果表明，开放性人格特质对应的网络特征是稳定的，可以有效区分两个年龄样本中不同得分的被试。此外，这项研究发现，这些网络特征在成年的不同时期有一定程度的差异。这些发现为静息态功能连接模式在人格特质测量中的应用提供了新的证据和见解，并帮助我们更好地了解人类大脑的动态特征。
The functional connectivity patterns of the brain during resting state are closely related to an individual's cognition, emotion, behavior, and social interactions, making it an important research method to measure personality traits in an unbiased way, replacing traditional paper-and-pencil tests. However, due to the dynamic nature of the brain, whether the changes in functional connectivity caused by age can stably map onto personality traits has not been previously investigated. This study focuses on whether network features that are significantly related to personality traits can effectively distinguish subjects with different personality traits, and whether these network features vary across different periods of adulthood. The study included 343 healthy adult participants, divided into early adulthood and middle adulthood groups according to the age threshold of 35. Resting-state functional magnetic resonance imaging (fMRI) and the Big Five personality questionnaire were collected. we investigated the relationship between personality traits and intrinsic whole-brain functional connectome. We then used support vector machine (SVM) to evaluate the performance of personality network features in distinguishing subjects with high and low scores in the early-adulthood sample, and cross-validated in the mid-adulthood sample. Additionally, edge-based analysis (NBS) was used to explore the stability of personality networks across the two age samples. Our results show that the network features corresponding to openness personality trait are stable and can effectively differentiate subjects with different scores in both age samples. Furthermore, this study found that these network features vary to some extent across different periods of adulthood. These findings provide new evidence and insights into the application of resting-state functional connectivity patterns in measuring personality traits and help us better understand the dynamic characteristics of the human brain.