Predictive associations between brain functional connectivity, motor abilities, and executive function development in early childhood: a longitudinal machine learning study.

objectiveThis study investigates the predictive associations between motor abilities and executive functions in early childhood by examining brain functional connectivity patterns and their predictive value for developmental trajectories.

methodsA longitudinal study recruited 256 healthy preschool children aged 3-6 years from kindergartens affiliated with Shandong Sport University, China. Participants underwent resting-state fMRI, standardized motor assessments (MABC-2), and cognitive testing at baseline, 6-month, and 12-month follow-up (with primary analyses focusing on baseline to 12-month changes). A novel machine learning framework integrated multimodal neuroimaging and behavioral data using graph neural networks and feature fusion architectures to model motor-cognitive developmental relationships.

resultsMotor skills showed progressive maturation, with fine motor percentiles increasing from 38.2±23.7 to 56.3±27.1. Sensorimotor network connectivity increased systematically (0.15±0.08 to 0.22±0.09), while attention networks followed inverted-U developmental patterns. The multimodal machine learning model achieved 76.8±4.3% accuracy for motor and 74.2±3.9% for executive function outcomes, outperforming single-domain models. Brain connectivity features contributed 58% of predictive variance, indicating that baseline neural patterns predict subsequent developmental changes, though causal relationships cannot be established from these observational data.

conclusionsThese results highlight early brain functional connectivity-especially sensorimotor networks-as a key predictor of motor and executive function development. Findings support the identification of early neural biomarkers of developmental risk and inform evidence-based strategies in early childhood education and targeted motor interventions.