The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Resting-state cortical connectivity predicts motor skill acquisition
Open accessAbstract
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, 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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Cites
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“Next, EEG data underwent an Infomax ICA decomposition (EEGLAB (Delorme and Makeig, 2004)).”
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · cited 3×
“Measures of connectivity, as compared to assessments of focal brain regions, have an improved ability to represent complexity in human cortical processing and as a result have a stronger relationship with many types of behavior (Bullmore and Sporns, 2009).”
InfluentialNunez, P. L., & Srinivasan, R. (2006) · doi:10.1093/acprof:oso/9780195050387.001.0001 · Cited by 2,531
Self-citationElectrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938 · cited 1×
“Combined EEG and fMRI studies have reported that specific combinations of EEG rhythms correspond with low frequency activity of specific resting-state networks (Mantini et al., 2007).”
Emerging concepts for the dynamical organization of resting-state activity in the brain
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798 · cited 2×
“Recent resting-state studies have provided new inroads for measuring differences in brain function in relation to behavior across individual subjects (Deco et al., 2011).”
Delorme, A., Sejnowski, T., & Makeig, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.004 · Cited by 1,759 · cited 1×
“Of the remaining components, amplitude topography, frequency spectra, and component time series were inspected to identify eye blinks, eye movements, and heart rhythms (Delorme et al., 2007), and were removed.”
Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review
Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.034 · Cited by 1,335
Andersson, C. A., & Bro, R. (2000) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(00)00071-x · Cited by 1,235 · cited 2×
“In the present study, the N-way toolbox (Andersson and Bro, 2000) was used to generate PLS models of resting EEG coherence predicting % Improvement across practice.”
Partial least squares analysis of neuroimaging data: applications and advances
McIntosh, A. R., & Lobaugh, N. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.020 · Cited by 1,084 · cited 2×
“Third, PLSmodeling is useful for defining relationships between brain function and behavior (Krishnan et al., 2011; McIntosh and Lobaugh, 2004), for example, decoding behavioral output with high accuracy using fewer variables than with other approaches (Chao et al.”
Brain connectivity related to working memory performance
Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3408-06.2006 · Cited by 853 · cited 1×
“Organization of brain networks at rest has also been correlated with subsequent behavioral performance (Hampson et al., 2006; Tambini et al., 2010).”
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Wulf, G., & Lewthwaite, R. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0999-9 · Cited by 1,238
Interpreting and Utilising Intersubject Variability in Brain Function
Seghier, M. L., & Price, C. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.03.003 · Cited by 306
Connectivity measures are robust biomarkers of cortical function and plasticity after stroke
Wu, J., Quinlan, E. B., Dodakian, L., McKenzie, A. L., Kathuria, N., Zhou, R. J., Augsburger, R., See, J., Le, V., Srinivasan, R., et al. (2015) · Brain · doi:10.1093/brain/awv156 · Cited by 220
“Furthermore, several recent studies demonstrate that individual differences in brain function at rest are predictive of subsequent performance (Hampson et al., 2006; Tambini et al., 2010; Wu et al., 2014).”
BackgroundMethodResultInfluentialSelf-citationDumontheil, I. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.04.012 · Cited by 186
Pain-free resting-state functional brain connectivity predicts individual pain sensitivity
Spisák, T., Kincses, B., Schlitt, F., Zunhammer, M., Schmidt‐Wilcke, T., Kincses, Z. T., & Bingel, U. (2020) · Nature Communications · doi:10.1038/s41467-019-13785-z · Cited by 135
Metadata
- 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
- 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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