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Connectome-based predictive modeling of handwriting and reading using task-evoked and resting-state functional
Junjun Li1,2,3, Dai Zhang4,5, Huan Ren1,2
1State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Iscience
|August 12, 2025
Summary
Functional connectivity models predict handwriting speed using brain imaging. This approach also reveals shared brain networks for handwriting and reading, aiding in diagnosing related disorders.
Area of Science:
- Neuroscience
- Cognitive Science
- Neuroimaging
Background:
- Functional connectivity models characterize individual behavior.
- Application to skilled motor behavior, like handwriting, is underexplored.
Purpose of the Study:
- To predict individual differences in handwriting skills using connectome-based predictive modeling (CPM).
- To explore the neural substrates underlying handwriting and reading abilities.
Main Methods:
- Used connectome-based predictive modeling (CPM) with functional magnetic resonance imaging (fMRI) data.
- Acquired both handwriting task-related and resting-state fMRI data.
- Utilized general functional connectivity (GFC) metrics.
Main Results:
- General functional connectivity (GFC) metrics reliably predicted individual differences in handwriting speed.
- The predictive model involved motor, visual, and executive control networks.
- The GFC model also predicted reading ability, indicating shared and distinct neural substrates for both skills.
Conclusions:
- General functional connectivity (GFC) shows potential for characterizing skilled motor behavior.
- Neuroimaging techniques can aid in diagnosing handwriting- and reading-related disorders.
