Connectome-based predictive modeling of handwriting and reading using task-evoked and resting-state functional connectivity.

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.