NeuroImage：休息时大脑活动可预测人格特质

　　喜欢富于内省，缄默谨慎，静如止水的林黛玉；还是长于交际，活泼开朗，动如脱兔的史湘云？内向和外向作为一个人稳定而统一的人格品质，长久以来得到心理学家的重视。西南大学心理学部雷旭副教授带领的研究小组发现该特质与休息时大脑的活动有关。他们发现，大脑中一个叫默认模式网络的系统实质负责了内外向的编码：越内向的人默认模式网络的波动越有记忆性。目前，本项研究已在线发表于神经影像学期刊《神经成像》(NeuroImage)上。

　　在这项名为"默认模式网络编码外向人格特质"的研究中，研究生赵治瀛邀请到20位西南大学在校学生参与功能磁共振扫描。一个叫默认模式网络的活动引起了他的注意。该网络正如其名，即使在人处于休息状态(默认)时，它仍在持续不断的激活。已有研究表明，默认模式网络的异常可能导致从阿尔茨海默病到精神分裂症的各种疾病。应用信号长时记忆指数(Hurst)，赵治瀛进一步发现外向的人长时记忆指数都比较小。而内向的人长时记忆指数越大，表明大脑活动更多地受到过去状态的影响。

　　该研究认为，默认模式网络活动的长时记忆性在一定意义上体现了大脑自我参考的刷新和处理能力。信号记忆性越小的被试具有更好的在线信息处理能力，能够支持他们活泼好动，思维敏捷的需要，表现出外向性。反之则会使人更多的将兴趣指向自身内部，表现出自我反想、深思熟虑、疑虑困惑等特点。下一步，该小组希望深入探讨尽责性、随和性等其他人格特质是否也与休息时大脑特定区域的活动有关。想知道自己的人格特质是怎么样的吗？快来做个五分钟的功能磁共振扫描吧，答案就在你大脑活动的轨迹中。

　　本研究得到国家自然科学基金(31200857,31170981)和教育部人文社会科学研究青年基金(12YJC190015)的支持。

Extraversion is encoded by scale-free dynamics of default mode network

Abstract

Resting-state functional Magnetic Resonance Imaging (rsfMRI) is a powerful tool to investigate neurological and psychiatric diseases. Recently, the evidences linking the scaling properties of resting-state activity and the personality have been accumulated. However, it remains unknown whether personality is associated with the scale-free dynamics of default mode network (DMN) — the most widely studied network in the rsfMRI literatures. To investigate this question, we estimated the Hurst exponent, quantifying long memory of a time-series, in DMN of rsfMRI in 20 healthy individuals. The Hurst exponent in DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with the extraversion score of the revised Eysenck Personality Questionnaire. Specifically, longer memory in DMN corresponded to lower extraversion. We provide evidence for an association between individual differences in personality and scaling dynamics in DMN, whose alteration has been previously linked with introspective cognition. This association might arise from the efficient in online information processing. Our results suggest that personality trait may be reflected by the scaling property of resting-state networks.