Resting-state cortical connectivity predicts motor skill acquisition
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Many studies have examined brain states in an effort to predict individual differences in the capacity for learning, with overall moderate results. The present study investigated how measures of cortical network function acquired at rest using dense-array EEG (256 leads) predict subsequent acquisition of a new motor skill. Brain activity was recorded in 17 healthy young subjects during 3min of wakeful rest prior to a single motor skill training session on a digital version of the pursuit rotor task. Practice was associated with significant gains in task performance (% time on target increased from 24% to 41%, p<0.0001). Using a partial least squares regression (PLS) model, coherence with the region of the left primary motor area (M1) in resting EEG data was a strong predictor of motor skill acquisition (R(2)=0.81 in a leave-one-out cross-validation analysis), exceeding the information provided by baseline behavior and demographics. Within this PLS model, greater skill acquisition was predicted by higher connectivity between M1 and left parietal cortex, possibly reflecting greater capacity for visuomotor integration, and by lower connectivity between M1 and left frontal-premotor areas, possibly reflecting differences in motor planning strategies. EEG coherence, which reflects functional connectivity, predicts individual motor skill acquisition with a level of accuracy that is remarkably high compared to prior reports using EEG or fMRI measures.
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Clusters of brain regions involved in motor learning can be recognized at rest and in-task.
“Clusters of brain regions involved in motor learning can be recognized, both at rest (Albert, Robertson, & Miall, 2009;Biswal, Yetkin, Haughton, & Hyde, 1995;Wu, Srinivasan, Kaur, & Cramer, 2014) and in-task (Hasan et al., 2013).”
From Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning (2016) · cites this paper as Wu, Srinivasan, Kaur, & Cramer, 2014
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- Authors
- Wu, J., Srinivasan, R., Kaur, A., & Cramer, S. C.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Cramer, Steven C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 91
- Pages
- 84-90
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3965590
- OpenAlex
- W1980592979
- Cited by
- 150
- References
- 56
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 8 · 2023: 10 · 2022: 18 · 2021: 13 · 2020: 14 · 2019: 13 · 2018: 20 · 2017: 9 · 2016: 21 · 2015: 5 · 2014: 2 · 2012: 1
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR000153)
- National Institute of Neurological Disorders and Stroke (R01NS059909)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K24HD074722)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- resting-state functional connectivity, motor skill learning, EEG coherence, high-density EEG, primary motor cortex, parietal cortex, visual-motor integration, motor planning, individual differences in learning, pursuit rotor task
- MeSH terms
- Adolescent, Adult, Analysis of Variance, Beta Rhythm, Cerebral Cortex, Data Interpretation, Statistical, Electroencephalography, Female, Humans, Learning, Male, Motor Cortex, Motor Skills, Neural Pathways, Parietal Lobe, Predictive Value of Tests, Psychomotor Performance, Least-Squares Analysis, Young Adult
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