A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures.

Area of Science:

• Neuroscience
• Motor Control
• Biomedical Engineering

Background:

• Objective and frequent motor function assessment is crucial for personalized therapy.
• Brain functional connectivity (FC) offers a promising neurophysiological measure.
• Individualized approaches are needed due to variations in brain networks and subject capabilities.

Purpose of the Study:

• To investigate an individualized method for estimating longitudinal changes in motor skill using resting-state functional connectivity (rsFC).
• To assess the accuracy of EEG-based rsFC in quantifying subtle motor skill acquisition.
• To develop a pragmatic and objective tool for motor assessment.

Main Methods:

• Utilized electroencephalography (EEG) to measure resting-state functional connectivity (rsFC).
• Employed computer-based tracing tasks with spatial error as a quantifiable measure of motor skill.
• Applied Partial Least Squares (PLS) analysis for an individualized estimation of tracing error from rsFC changes.

Main Results:

• Achieved an average accuracy of 98% (SD 1.2%) in estimating longitudinal changes in tracing error.
• Demonstrated the potential of rsFC changes to reflect incremental improvements in motor skill.
• Validated the effectiveness of the individualized PLS approach.

Conclusions:

• EEG-based rsFC provides an accurate and objective measure for longitudinal motor assessment.
• The developed individualized method reduces reliance on expert examiners.
• Facilitates more frequent functional appraisals and personalized training program development.