Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals.
Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals.
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DOI：
10.1093/scan/nsy002
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发表时间：
2018-02-01
影响因子：
4.2
通讯作者：
Chun MM
中科院分区：
文献类型：
--
作者：
Hsu WT;Rosenberg MD;Scheinost D;Constable RT;Chun MM
神经质和外向性的人格维度与情感体验和情感障碍密切相关。以前的研究报告了功能性磁共振成像（fMRI）活动与这些特征相关，但没有研究使用基于大脑的测量来预测它们。在这里，我们使用一种完全交叉验证的方法，从功能连接（FC）数据中预测新个体的神经质和外向性，因为他们在fMRI扫描期间只是休息。我们将数据驱动技术——基于连接体的预测建模（CPM）应用于来自Nathan Kline Institute Rockland样本的114名参与者的静息状态FC数据和神经质和外向性得分（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划分为268个节点之后，我们将每个个体的FC矩阵定义为每对节点的活动时间过程之间的关联集。CPM确定了由与神经质和外向性得分相关的功能连接组成的网络，并利用这些网络中的强度来预测被遗忘者的得分。CPM预测了新个体的神经质和外向性，表明静息状态FC的模式揭示了性格的特质水平。CPM还揭示了预测网络表现出一些与过去研究一致的解剖模式，以及潜在的新的人格感兴趣的大脑区域。
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.
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