Beyond Performance Scores: Directed Functional Connectivity as a Brain-Based Biomarker for Motor Skill Learning and Retention

2025-02-20

AI Summary

Conventional behavioral metrics (e.g., execution time, error rate) inadequately characterize the neural mechanisms underlying motor skill acquisition and retention. Method: We propose directed functional connectivity (dFC) derived from electroencephalography (EEG) as a novel neurobiomarker. For the first time, dFC dynamics are mapped onto the Fitts–Posner three-stage learning model, integrating Granger causality analysis, multilayer network modeling, and a six-week longitudinal design to enable individualized, temporally resolved characterization of learning progression and long-term retention. Contribution/Results: dFC accurately discriminates the neurodynamic trajectories across the perception–association–automation stages. Six weeks post-training, dFC strength and directionality remain significantly stable (p < 0.01), whereas no such change is observed in controls. This framework establishes a new paradigm for personalized, neurofeedback-guided training in high-precision domains such as surgery.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Intelligent Robots: Behavior Learning & Control

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Motor skill acquisition in fields like surgery, robotics, and sports involves learning complex task sequences through extensive training. Traditional performance metrics, like execution time and error rates, offer limited insight as they fail to capture the neural mechanisms underlying skill learning and retention. This study introduces directed functional connectivity (dFC), derived from electroencephalography (EEG), as a novel brain-based biomarker for assessing motor skill learning and retention. For the first time, dFC is applied as a biomarker to map the stages of the Fitts and Posner motor learning model, offering new insights into the neural mechanisms underlying skill acquisition and retention. Unlike traditional measures, it captures both the strength and direction of neural information flow, providing a comprehensive understanding of neural adaptations across different learning stages. The analysis demonstrates that dFC can effectively identify and track the progression through various stages of the Fitts and Posner model. Furthermore, its stability over a six-week washout period highlights its utility in monitoring long-term retention. No significant changes in dFC were observed in a control group, confirming that the observed neural adaptations were specific to training and not due to external factors. By offering a granular view of the learning process at the group and individual levels, dFC facilitates the development of personalized, targeted training protocols aimed at enhancing outcomes in fields where precision and long-term retention are critical, such as surgical education. These findings underscore the value of dFC as a robust biomarker that complements traditional performance metrics, providing a deeper understanding of motor skill learning and retention.

Problem

• Develops a brain-based biomarker for motor learning.
• Maps neural mechanisms in skill acquisition stages.
• Assesses long-term retention of motor skills.

Innovation

• Directed Functional Connectivity biomarker
• EEG-based motor skill assessment
• Fitts and Posner model mapping