Author(s): KANE, D., SCHIPPER, K., HASSAN, M., DRAGANSKI, B., RUGGERI, P., BARRAL, J., Institution: UNIL , Country: SWITZERLAND, Abstract-ID: 2775

INTRODUCTION:
Motor skill acquisition represents a fundamental lifelong capacity. However, it is well established that this process varies across individuals. Indeed, there is considerable variation in the speed of acquisition, the quality of performance, the resilience, and the generalization of learning across individuals [1]. Multiple studies have shown that customized training to individual’s performance promotes longer retention and stronger transfer [2]. Learning triggers brain reorganization to allow long term retention [3]. It is supposed that this restructuration is mediate by Beta-band activity, dominated by bursts (high amplitude transient activity) within the motor cortex, is closely linked to movement-related processing. Yet its transient burst dynamics during long-term learning remain poorly understood [4]. By combining electroencephalography (EEG), burst-resolved metrics, and a longitudinal design, this study aimed to clarify how beta activity reorganizes in M1 with practice to support motor skill learning under adaptative training.
METHODS:
The study included 32 participants (16 f; age = 21 ± 2.46 years). The protocol lasted 13 sessions over 8 weeks, and EEGs were realized in session 2, 3, 9 to assess change in brain activity linked to practice. Participants completed a visuomotor training task and were either in an adaptive difficulty (AG) or non-adaptive training group (NAG). To increase EEG analysis precision, EEG-MRI co-registration and a neuronavigation system were used. A source level analysis was done to restrict beta bursts detection to M1. Session-related changes in transient beta activity were examined in individualized primary motor cortex (M1) regions during motor training. Source signals were filtered around peak beta frequency and bursts were detected using an optimized, data-driven threshold. Burst probability, post-movement timing variability, and amplitude were quantified across task phases.
RESULTS:
Both training conditions led to motor skill learning. AG showed significantly greater gains during training but a significant decline in retention performance. These changes reflected underlying burst dynamics: post movement bursts became more temporally confined and consistent, with increased probability and reduced timing variability across sessions for both groups. However, only the AG showed a session-related increase of amplitude.
CONCLUSION:
These results demonstrate that beta burst features reorganize with practice, providing a temporally precise neural readout of training progression and revealing how different learning conditions can shape cortical dynamics over time. AG burst amplitude increases can be due to more synchronized synaptic input in M1 and might be refinement of internal models.