Focusing on the Differences of Resting-State Brain Networks, Using a Data-Driven Approach to Explore the Functional Neuroimaging Characteristics of Extraversion Trait | |
Tian, Feng1; Wang, Junjie2; Xu, Cheng3; Li, Hong4; Ma, Xin5; Xin Ma | |
通讯作者邮箱 | maxinanding@vip.163.com |
心理所单位排序 | 5 |
摘要 | In recent years, functional magnetic resonance imaging (fMRI) has been widely used in studies that explored the personality-brain association. Researches on personality neuroscience have the potential to provide personality psychology with explanatory models that is, why people differ from each other rather than how they differ from each other (DeYoung and Gray, 2009). As one of the most important dimensions of personality traits, extraversion is the most stable core and a universal component in personality theory. The aim of the present study was to employ a fully data-driven approach to study the brain mechanism of extraversion in a sample of 111 healthy adults. The Eysenck Personality Questionnaire (EPQ) was used to measure the personality characteristics of all the subjects. We investigated whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their intrinsic connectivity networks (ICNs). The resultant subjects communities and the representative characteristics of ICNs were then associated to personality concepts. Finally, we found one ICN (salience network) whose subject community profiles exhibited significant associations with Extraversion trait. |
关键词 | personality traits resting-state fMRI data-driven salience network extraversion trait |
2018-03-05 | |
语种 | 英语 |
DOI | 10.3389/fnins.2018.00109 |
发表期刊 | FRONTIERS IN NEUROSCIENCE |
ISSN | 1662-453X |
卷号 | 12页码:1-8 |
期刊论文类型 | Article |
收录类别 | SCI |
WOS关键词 | DOMAIN CRITERIA RDOC ; INTRINSIC CONNECTIVITY NETWORKS ; INDEPENDENT COMPONENT ANALYSIS ; CHILDHOOD-ONSET SCHIZOPHRENIA ; DEFAULT NETWORK ; NIMH RESEARCH ; PERSONALITY ; REPRODUCIBILITY ; PSYCHOSIS ; EMOTION |
WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000426554600001 |
资助机构 | National Natural Science Foundation of China(81271482 ; National key research and development program(2016YFC1307004) ; 81571319 ; 61402318) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/26038 |
专题 | 健康与遗传心理学研究室 |
通讯作者 | Xin Ma |
作者单位 | 1.Shanxi Med Univ, Hosp 2, Dept Psychiat, Taiyuan, Shanxi, Peoples R China 2.Shanxi Med Univ, Clnibai Med Coll 1, Dept Psychiat, Hosp 1, Taiyuan, Shanxi, Peoples R China 3.Shanxi Prov Peoples Hosp, Dept Magnet Resonance Imaging, Taiyuan, Peoples R China 4.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 5.Capital Med Univ, Beijing Anding Hosp, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Tian, Feng,Wang, Junjie,Xu, Cheng,et al. Focusing on the Differences of Resting-State Brain Networks, Using a Data-Driven Approach to Explore the Functional Neuroimaging Characteristics of Extraversion Trait[J]. FRONTIERS IN NEUROSCIENCE,2018,12:1-8. |
APA | Tian, Feng,Wang, Junjie,Xu, Cheng,Li, Hong,Ma, Xin,&Xin Ma.(2018).Focusing on the Differences of Resting-State Brain Networks, Using a Data-Driven Approach to Explore the Functional Neuroimaging Characteristics of Extraversion Trait.FRONTIERS IN NEUROSCIENCE,12,1-8. |
MLA | Tian, Feng,et al."Focusing on the Differences of Resting-State Brain Networks, Using a Data-Driven Approach to Explore the Functional Neuroimaging Characteristics of Extraversion Trait".FRONTIERS IN NEUROSCIENCE 12(2018):1-8. |
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