Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.
School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.
Cereb Cortex. 2023 Feb 7;33(4):1412-1425. doi: 10.1093/cercor/bhac145.
Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.
强迫是强迫症（OCD）的核心症状之一。尽管许多研究已经探讨了强迫的神经机制，但没有研究使用基于大脑的测量来预测强迫。在这里，我们使用基于连接组的预测建模（CPM）来识别能够根据 57 名 OCD 患者全脑功能连接来预测强迫水平的网络。然后，我们应用 CPM 的计算损伤版本来检查特定大脑区域的重要性。我们还比较了 OCD 患者未受影响的一级亲属（UFDR）和健康对照组的预测网络强度。CPM 成功预测了个体强迫水平，并确定了与强迫呈正相关（主要是纹状体的皮质下区域和海马的边缘区域）和负相关（主要是额顶叶区域）的网络。当模拟前额叶皮层和小脑的损伤时，负模型的预测能力显著降低，支持这些区域对强迫预测的重要性。我们在 OCD 患者及其 UFDR 的负预测网络中发现了类似的网络强度模式，表明 CPM 具有识别精神病理学易感性标志物的潜力。
