Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands.
Cereb Cortex. 2021 May 10;31(6):3006-3020. doi: 10.1093/cercor/bhaa407.
Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational "lesion" approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.
焦虑相关疾病在人类社会中高度普遍。能够识别出预示高特质焦虑的神经生物学标志物，有助于评估有精神疾病高风险的个体。在这里，我们将基于连接组的预测建模 (CPM) 应用于全脑静息态功能连接 (rsFC) 数据，以预测 76 名健康参与者的特质焦虑程度。使用 CPM 中的计算“损伤”方法，我们检查了确定的主要大脑区域的权重及其连接。结果表明，CPM 成功地基于全脑 rsFC 预测了个体的焦虑程度，尤其是边缘区域和前额叶皮层之间的 rsFC。该模型的预测能力从模拟的边缘区域损伤、边缘区域内的连接损伤以及边缘区域和前额叶皮层之间的连接损伤显著降低。重要的是，该神经模型推广到了一个独立的大样本（n=501）。这些发现强调了边缘系统和前额叶皮层在焦虑预测中的重要作用。我们的工作为基于连接组的建模在预测个体人格差异方面的有用性提供了证据，并表明其在识别精神病理学风险的人格因素方面具有潜力。
