Connectome-based predictive modeling of handwriting and reading using task-evoked and resting-state functional connectivity
Junjun Li,
Dai Zhang,
Huan Ren,
Ke Zhou,
Yang Yang
Affiliations
Junjun Li
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; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang 310018, China
Dai Zhang
Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Medical Imaging Research Center, Anhui Medical University, Hefei 230032, China
Huan Ren
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
Ke Zhou
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China; Corresponding author
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; Corresponding author
Summary: 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.
