Decoding motor expertise from fine-tuned oscillatory network organization

Amoruso, Lucia; Pusil, Sandra; Martin Garcia, Adolfo; Ibanez, Agustin

Abstract

Can motor expertise be robustly predicted by the organization of frequency-specific oscillatory brain networks? To answer this question, we recorded high-density electroencephalography (EEG) in expert Tango dancers and naives while viewing and judging the correctness of Tango-specific movements and during resting. We calculated task-related and resting-state connectivity at different frequency-bands capturing task performance (delta [delta], 1.5-4 Hz), error monitoring (theta [theta], 4-8 Hz), and sensorimotor experience (mu [mu], 8-13 Hz), and derived topographical features using graph analysis. These features, together with canonical expertise measures (i.e., performance in action discrimination, time spent dancing Tango), were fed into a data-driven computational learning analysis to test whether behavioral and brain signatures robustly classified individuals depending on their expertise level. Unsurprisingly, behavioral measures showed optimal classification (100%) between dancers and naives. When considering brain models, the task-based classification performed well (similar to 73%), with maximal discrimination afforded by theta-band connectivity, a hallmark signature of error processing. Interestingly, mu connectivity during rest outperformed (100%) the task-based approach, matching the optimal classification of behavioral measures and thus emerging as a potential trait-like marker of sensorimotor network tuning by intense training. Overall, our findings underscore the power of fine-tuned oscillatory network signatures for capturing expertise-related differences and their potential value in the neuroprognosis of learning outcomes.

Más información

Título según WOS: Decoding motor expertise from fine-tuned oscillatory network organization
Título de la Revista: HUMAN BRAIN MAPPING
Editorial: Wiley
Fecha de publicación: 2022
DOI:

10.1002/hbm.25818

Notas: ISI