Personality–brain connection: Based on resting‐state functional magnetic resonance imaging data‐driven exploration

Hong Li, Junjie Wang
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Abstract

The personality–brain association mechanism has been a topic of interest in the field of neuroscience. Usually, the previous research strategy was to first group the population based on different personality traits, and then explore the brain mechanisms corresponding to different personality groups. At present, a “brain‐first” research strategy, which uses data‐driven approaches instead of personality traits to first group the population, has been adopted to further enhance study objectivity. Here, we used a data‐driven approach following the “brain‐first” research strategy to deeply mine the resting‐state brain functional magnetic resonance imaging data of 119 healthy participants, classified subjects into different groups based on brain image characteristics, and used the Sixteen Personality Factor Questionnaire to explain the variabilities of resting‐state brain characteristics between different groups. Finally, we have identified 3 personality–brain connections, including the privateness–left frontoparietal network, liveliness–sensory–motor network, and vigilance–sensory–motor network. Furthermore, we conclude that the above‐mentioned three personality factors are based on brain neural activity, independent of the subjective experience of the personality scale creator, and have stronger explanatory power of brain imaging features.
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个性与大脑连接:基于静息状态功能磁共振成像数据驱动的探索
人格-脑关联机制一直是神经科学领域的研究热点。通常,以往的研究策略是首先根据不同的人格特征对人群进行分组,然后探索不同人格群体对应的大脑机制。目前,为了进一步提高研究的客观性,研究人员采用了一种“大脑优先”的研究策略,即使用数据驱动的方法而不是人格特征来首先对人群进行分组。本研究采用数据驱动的方法,遵循“脑优先”的研究策略,对119名健康受试者的静息状态脑功能磁共振成像数据进行深度挖掘,并根据脑成像特征将受试者分为不同的组,使用16个人格因素问卷来解释不同组间静息状态脑特征的差异性。最后,我们确定了3个人格-大脑连接,包括隐私-左额顶叶网络,活跃-感觉-运动网络和警惕-感觉-运动网络。以上三种人格因素均以脑神经活动为基础,独立于人格量表制作者的主观经验,对脑成像特征具有较强的解释力。
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