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Peer-reviewedCC BY 4.0Cerebellum (London, England)

Motor Expertise Combines Lower Task Engagement with Enhanced Error Sensitivity

  • Pavel Filip1,2,3
  • Lenka Marková4
  • Cecile Gallea5
  • Stéphane Lehéricy5
  • Ovidiu V. Lungu6
  • Shalom Michaeli3
  • Silvia Mangia3
  • Viktor Weiss7
  • Martin Bareš7
  • Lubomír Vojtíšek8
8 affiliations
  1. 1Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
  2. 2Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
  3. 3Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN USA
  4. 4Intensive Care, Rescue and Pain Medicine, Canton Hospital St.Gallen Clinic for Anaesthesiology, St. Gallen, Switzerland
  5. 5Institut du Cerveau–Paris Brain Institute–ICM, Inserm, CNRS, Sorbonne Université, 75651 Paris, France
  6. 6Department of Psychiatry and Addictology, University of Montreal, Montreal, Québec Canada
  7. 7First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne’s Teaching Hospital, Brno, Czech Republic
  8. 8Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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Abstract

Skilled motor behaviour relies on complex predictive control and error processing. Despite its indispensable position in our everyday lives, neurobiological mechanisms of motor skill acquisition remain poorly understood. The presented study evaluates the functional dynamics and microstructural substrate of expertise-related reorganisation in predictive timing circuits using multimodal functional and quantitative magnetic resonance imaging (MRI) data. Forty-one baseball/softball players, representing a natural model of optimised motor timing programs, were compared against forty-six non-athletic controls using a structural connectome-informed hierarchical Bayesian framework jointly modelling task-based functional MRI dynamics of feedforward predictive control and error-driven modulation. Expertise-related microstructural differences were then characterised using neurite orientation dispersion and density imaging and adiabatic T₁ρ mapping. In non-athletic controls, predictive control was found to engage a distributed cortico-subcortical sensorimotor, visuospatial, attentional and frontoparietal loops, while higher-order associative systems were relatively down-regulated. However, quantitative deviation from the task optimum was associated with lower response across this predictive network. Expertise was characterised by higher behavioural performance but also by attenuated prediction-related functional engagement alongside enhanced error-related modulation. Furthermore, neurite density and orientation dispersion indices were higher in experts across distributed cortical networks, while the cerebellum exhibited a divergent profile characterised by higher neurite density, lower orientation dispersion and faster T₁ρ relaxation. These findings indicate that expertise involves lower task engagement within an established architecture despite higher behavioural performance, together with enhanced error sensitivity and distributed microstructural differences. This pattern is consistent with more efficient neural dynamics complemented with underlying tissue alterations.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s12311-026-02100-4.

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Cite

BibTeXRIS
BibTeX
@article{filip2026motor,
  title = {{Motor Expertise Combines Lower Task Engagement with Enhanced Error Sensitivity}},
  author = {Pavel Filip and Lenka Marková and Cecile Gallea and Stéphane Lehéricy and Ovidiu V. Lungu and Shalom Michaeli and Silvia Mangia and Viktor Weiss and Martin Bareš and Lubomír Vojtíšek},
  journal = {Cerebellum (London, England)},
  year = {2026},
  month = nov,
  doi = {10.1007/s12311-026-02100-4},
  url = {https://doi.org/10.1007/s12311-026-02100-4},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Motor Expertise Combines Lower Task Engagement with Enhanced Error Sensitivity
AU  - Pavel Filip
AU  - Lenka Marková
AU  - Cecile Gallea
AU  - Stéphane Lehéricy
AU  - Ovidiu V. Lungu
AU  - Shalom Michaeli
AU  - Silvia Mangia
AU  - Viktor Weiss
AU  - Martin Bareš
AU  - Lubomír Vojtíšek
PY  - 2026
DA  - 2026/11/04
JO  - Cerebellum (London, England)
DO  - 10.1007/s12311-026-02100-4
UR  - https://doi.org/10.1007/s12311-026-02100-4
AB  - Skilled motor behaviour relies on complex predictive control and error processing. Despite its indispensable position in our everyday lives, neurobiological mechanisms of motor skill acquisition remain poorly understood. The presented study evaluates the functional dynamics and microstructural substrate of expertise-related reorganisation in predictive timing circuits using multimodal functional and quantitative magnetic resonance imaging (MRI) data. Forty-one baseball/softball players, representing a natural model of optimised motor timing programs, were compared against forty-six non-athletic controls using a structural connectome-informed hierarchical Bayesian framework jointly modelling task-based functional MRI dynamics of feedforward predictive control and error-driven modulation. Expertise-related microstructural differences were then characterised using neurite orientation dispersion and density imaging and adiabatic T₁ρ mapping. In non-athletic controls, predictive control was found to engage a distributed cortico-subcortical sensorimotor, visuospatial, attentional and frontoparietal loops, while higher-order associative systems were relatively down-regulated. However, quantitative deviation from the task optimum was associated with lower response across this predictive network. Expertise was characterised by higher behavioural performance but also by attenuated prediction-related functional engagement alongside enhanced error-related modulation. Furthermore, neurite density and orientation dispersion indices were higher in experts across distributed cortical networks, while the cerebellum exhibited a divergent profile characterised by higher neurite density, lower orientation dispersion and faster T₁ρ relaxation. These findings indicate that expertise involves lower task engagement within an established architecture despite higher behavioural performance, together with enhanced error sensitivity and distributed microstructural differences. This pattern is consistent with more efficient neural dynamics complemented with underlying tissue alterations.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s12311-026-02100-4.
SN  - 1473-4222
SN  - 1473-4230
ER  -