Li Junjun, Zhang Dai, Ren Huan, Zhou Ke, Yang Yang
State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
iScience. 2025 Jul 7;28(8):113075. doi: 10.1016/j.isci.2025.113075. eCollection 2025 Aug 15.
Previous studies have shown that functional connectivity-based models can characterize individual differences in human behavior. However, the applicability of such models to skilled motor behavior remains largely unexplored. In this study, we employed a connectome-based predictive modeling (CPM) approach to predict individual differences in handwriting skills using handwriting task-related and resting-state functional magnetic resonance imaging (fMRI) data. Our results demonstrated that general functional connectivity (GFC) metrics, which capture shared features across task-evoked and resting-state functional connectivity, reliably reflect individual differences in handwriting speed. This predictive model involved multiple functional networks associated with motor, visual, and executive control processes. Furthermore, we found that the GFC-based model derived from handwriting task and resting-state data also predicted individual differences in reading ability, revealing both shared and distinct neural substrates underlying handwriting and reading skills. These findings highlight the potential of neuroimaging in the diagnosis of handwriting- and reading-related disorders.
先前的研究表明，基于功能连接的模型能够刻画人类行为中的个体差异。然而，此类模型在熟练运动行为方面的适用性在很大程度上仍未得到探索。在本研究中，我们采用基于脑连接组的预测建模（CPM）方法，利用与手写任务相关的以及静息态功能磁共振成像（fMRI）数据来预测个体在书写技能上的差异。我们的结果表明，一般功能连接（GFC）指标，它捕捉了任务诱发和静息态功能连接中的共同特征，能够可靠地反映个体在书写速度上的差异。这个预测模型涉及多个与运动、视觉和执行控制过程相关的功能网络。此外，我们发现从手写任务和静息态数据得出的基于GFC的模型也能预测个体在阅读能力上的差异，揭示了书写和阅读技能背后共同的和独特的神经基质。这些发现凸显了神经成像在诊断与书写和阅读相关障碍方面的潜力。
