Department of Psychology, Yale University, New Haven, CT 06520, USA.
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.
Soc Cogn Affect Neurosci. 2018 Feb 1;13(2):224-232. doi: 10.1093/scan/nsy002.
The personality dimensions of neuroticism and extraversion are strongly associated with emotional experience and affective disorders. Previous studies reported functional magnetic resonance imaging (fMRI) activity correlates of these traits, but no study has used brain-based measures to predict them. Here, using a fully cross-validated approach, we predict novel individuals' neuroticism and extraversion from functional connectivity (FC) data observed as they simply rested during fMRI scanning. We applied a data-driven technique, connectome-based predictive modeling (CPM), to resting-state FC data and neuroticism and extraversion scores (self-reported NEO Five Factor Inventory) from 114 participants of the Nathan Kline Institute Rockland sample. After dividing the whole brain into 268 nodes using a predefined functional atlas, we defined each individual's FC matrix as the set of correlations between the activity timecourses of every pair of nodes. CPM identified networks consisting of functional connections correlated with neuroticism and extraversion scores, and used strength in these networks to predict a left-out individual's scores. CPM predicted neuroticism and extraversion in novel individuals, demonstrating that patterns in resting-state FC reveal trait-level measures of personality. CPM also revealed predictive networks that exhibit some anatomical patterns consistent with past studies and potential new brain areas of interest in personality.
神经质和外向性的人格维度与情绪体验和情感障碍密切相关。先前的研究报告了这些特征与功能磁共振成像（fMRI）活动的相关性，但没有研究使用基于大脑的测量来预测这些特征。在这里，我们使用完全交叉验证的方法，从 fMRI 扫描期间参与者简单休息时观察到的功能连接（FC）数据预测新个体的神经质和外向性。我们将数据驱动技术——连接组预测建模（CPM）应用于 114 名 Nathan Kline Institute Rockland 样本的静息态 FC 数据和神经质和外向性评分（自我报告的 NEO 五因素量表）。在使用预定义的功能图谱将整个大脑分为 268 个节点后，我们将每个人的 FC 矩阵定义为每个节点对之间活动时间序列的相关系数集合。CPM 识别了由与神经质和外向性评分相关的功能连接组成的网络，并使用这些网络中的强度来预测被排除在外的个体的评分。CPM 预测了新个体的神经质和外向性，表明静息态 FC 中的模式揭示了人格的特质水平测量。CPM 还揭示了预测网络，这些网络显示出与过去研究一致的一些解剖模式和人格的潜在新感兴趣的大脑区域。
