dc.contributor.author | Amoruso, Lucia | |
dc.contributor.author | Pusil, Sandra | |
dc.contributor.author | García, Adolfo Martín | |
dc.contributor.author | Ibañez, Agustín | |
dc.date.accessioned | 2022-07-20T12:20:43Z | |
dc.date.available | 2022-07-20T12:20:43Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Amoruso, L., Pusil, S., García, A. M., & Ibañez, A. (2022). Decoding motor expertise from fine-tuned oscillatory network organization. Human Brain Mapping, 43( 9), 2817– 2832. https://doi.org/10.1002/hbm.25818 | es_ES |
dc.identifier.citation | Human Brain Mapping | |
dc.identifier.issn | 1065-9471 | |
dc.identifier.uri | http://hdl.handle.net/10810/56959 | |
dc.description | First published: 11 March 2022 | es_ES |
dc.description.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 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. | es_ES |
dc.description.sponsorship | Basque Government; Consejo Nacional de
Investigaciones Científicas y Técnicas;
(CONICET) Ikerbasque, Basque Foundation for
Science; Spanish State Research Agency,
Grant/Award Number: SEV-2015-0490;
Programa Interdisciplinario de Investigaci on
Experimental en Comunicaci on y Cognici on
(PIIECC), Facultad de Humanidades, USACH;
ANID; FONDECYT Regular, Grant/Award
Numbers: 1210195, 1210176; Global Brain
Health Institute (GBHI) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | WILEY | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/SEV-2015-0490 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | action observation | es_ES |
dc.subject | brain networks | es_ES |
dc.subject | graph theory | es_ES |
dc.subject | hdEEG | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | motor expertise | es_ES |
dc.subject | resting-state | es_ES |
dc.title | Decoding motor expertise from fine-tuned oscillatory network organization | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. | es_ES |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/journal/10970193 | es_ES |
dc.identifier.doi | 10.1002/hbm.25818 | |