English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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 naïves 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 [ δ ], 1.5–4 Hz), error monitoring (theta [ θ ], 4–8 Hz), and sensorimotor experience (mu [ μ ], 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 naïves. When considering brain models, the task‐based classification performed well (~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.

Decoding motor expertise from fine‐tuned oscillatory network organization