Back

TMS-EEG reveals causal dynamics of the premotor cortex during musical improvisation

Palhares, P. T.; D'Ambrosio, S.; Goncalves, O. F.

2025-10-05 neuroscience
10.1101/2025.10.04.680222 bioRxiv
Show abstract

Musical improvisation illustrates the brains capacity for flexible, creative motor control, yet the causal mechanisms underlying this complex behaviour remain poorly understood. We employed transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) to probe state-dependent cortical dynamics in the left dorsal premotor cortex (PMd) of professional jazz pianists (n = 3) during improvisation, sight-reading, and rest. This proof-of-concept study demonstrates the feasibility of combining perturbational neuroscience with ecologically valid musical performance. Multiple convergent analyses revealed distinct cortical signatures during improvisation: reduced local mean field power, decreased phase-locking of evoked responses, and preserved but gain-modulated early components as revealed by Correlated Components Analysis. These findings suggest that improvisation is characterized by attenuated PMd excitability and more variable response timings, while preserving the fundamental architecture of cortical responses. This perturbational signature supports a neural efficiency model of expertise whereby expert musicians achieve creative flexibility through training-induced streamlined, optimized cortical processing. Our results establish TMS-EEG as a powerful approach for investigating the causal dynamics of creative cognition and demonstrate how the brain reconfigures its response properties to support internally driven motor performance.

Published in Annals of the New York Academy of Sciences · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.