Resting state fast brain dynamics predict interindividual variability in motor performance.

Area of Science:

• Neuroscience
• Motor Control
• Cognitive Science

Background:

• Motor learning involves rapid skill enhancement and subsequent offline gains.
• Brain network reorganization underlies motor learning and performance variability.
• Resting-state (RS) neural activity may reflect intrinsic motor abilities and plasticity.

Purpose of the Study:

• To investigate if transient, sub-second magnetoencephalography (MEG) network activity during resting-state (RS) relates to motor learning and performance.
• To determine if RS neural dynamics reflect early learning-related plasticity or interindividual differences in motor skills.

Main Methods:

• Magnetoencephalography (MEG) recorded resting-state (RS) activity before and after a motor learning task.
• Hidden Markov modelling (HMM) analyzed MEG power envelope signals to identify recurrent brain network states.
• Correlations were assessed between HMM temporal parameters of specific states and motor performance levels.

Main Results:

• Eight recurrent topographical brain network states were identified using HMM.
• Motor performance levels correlated with HMM temporal parameters in two specific RS states, both pre- and post-learning.
• No significant association was found between RS dynamics and offline changes in motor performance.

Conclusions:

• Spontaneous transient neural dynamics during resting-state exhibit a trait-like relationship with interindividual variations in motor abilities.
• Resting-state neural dynamics do not appear to be state-dependent or significantly modulated by short-term motor learning experiences.
• These findings suggest that intrinsic neural variability, rather than short-term plasticity, underlies stable individual differences in motor performance.