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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
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · cited 1×
“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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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
Utility of EEG measures of brain function in patients with acute stroke
Wu, J., Srinivasan, R., Quinlan, E. B., Solodkin, A., Small, S. L., & Cramer, S. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00978.2015 · Cited by 136
Self-citationHordacre, B. G., Moezzi, B., & Ridding, M. C. (2018) · Human Brain Mapping · doi:10.1002/hbm.24079 · Cited by 139
“For example, estimates of high beta connectivity have been reported to predict response to tDCS and motor learning in healthy adults (Hordacre et al., 2017b; Wu et al., 2014).”
BackgroundResultLearning a common dictionary for subject-transfer decoding with resting calibration
Morioka, H., Kanemura, A., Hirayama, J.-I., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., & Ishii, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.015 · Cited by 113
“…patterns of functional connectivity, such as the default mode and the dorsal attention networks (Brookes et al., 2011; de Pasquale et al., 2012; Fox et al., 2005), and is likely intrinsic to individual brains (Baldassarre et al., 2012; Massar et al., 2014; Mueller et al., 2013; Wu et al., 2014).”
BackgroundFinger, H., Bönstrup, M., Cheng, B., Messé, A., Hilgetag, C. C., Thomalla, G., Gerloff, C. P., & Peter König, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005025 · Cited by 109
Somatosensory system integrity explains differences in treatment response after stroke
Ingemanson, M. L., Rowe, J. B., Chan, V., Wolbrecht, E. T., Reinkensmeyer, D. J., & Cramer, S. C. (2019) · Neurology · doi:10.1212/wnl.0000000000007041 · Cited by 107
“Data were preprocessed to remove extrabrain artifacts, as described previously.(29) Resting-state connectivity was estimated from EEG coherence in the high-beta (20–30 Hz) frequency band with electrodes overlying iM1, iS1, or iS2 as the seed region.”
MethodSelf-citationVukelić, M., & Gharabaghi, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.058 · Cited by 100
“The functional involvement of occipital regions might be linked to visuomotor transformation reflecting greater capacity for integration of multiple sensory modalities (Beuter and Modolo, 2009; Suminski et al., 2010; Wu et al., 2014).”
Bridging the gap between motor imagery and motor execution with a brain–robot interface
Bauer, R. H., Fels, M., Vukelić, M., Ziemann, U., & Gharabaghi, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.026 · Cited by 98
“Several measures of connectivity in resting state have been proposed to predict motor learning (Albert et al., 2009; Wu et al., 2014), task performance (Lee et al., 2011; Zhou et al., 2012) or personality traits (Langer et al., 2012; Putman, 2011).”
BackgroundMaso, F. D., Desormeau, B., Boudrias, M., & Roig, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.029 · Cited by 92
Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior
Bridwell, D. A., Cavanagh, J. F., Collins, A. G. E., Nunez, M. D., Srinivasan, R., Stober, S., & Calhoun, V. D. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00106 · Cited by 95
“In studies of motor learning, PLS models have been used with resting state EEG coherence to demonstrate that the functional connectivity of motor cortex can predict short-term motor learning (Wu et al., 2014).”
MethodSelf-citationNeuroplasticity: Insights from Patients Harboring Gliomas
Kong, N., Gibb, W. R., & Tate, M. C. (2016) · Neural Plasticity · doi:10.1155/2016/2365063 · Cited by 77
Brauchle, D., Vukelić, M., Bauer, R. H., & Gharabaghi, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00564 · Cited by 73
Upregulation of cortico-cerebellar functional connectivity after motor learning
Mehrkanoon, S., Boonstra, T. W., Breakspear, M. J., Hinder, M. R., & Summers, J. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.052 · Cited by 70
“Coherence within the primary motor area in resting-state EEG has been used to predict subsequent motor acquisition in single session motor skill learning (Wu et al., 2014).”
InfluentialInvestigating the Role of Alpha and Beta Rhythms in Functional Motor Networks
Athanasiou, A., Klados, M. A., Styliadis, C., Foroglou, N., Polyzoidis, K. S., & Bamidis, P. D. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.05.044 · Cited by 64
Predicting future learning from baseline network architecture
Mattar, M. G., Wymbs, N. F., Bock, A. S., Aguirre, G. K., Grafton, S. T., & Bassett, D. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.037 · Cited by 67
“The strength of individual 227 functional connections, or larger sets of connections, have been observed to correlate with individual differences in IQ 228 [55], visual orientation discrimination [56], working memory [52, 57], color knowledge [58], auditory stimulus detection 229 [59], pursuit rotor performance [60],…”
BackgroundCharacterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis
Moezzi, B., Pratti, L. M., Hordacre, B. G., Graetz, L. J., Berryman, C. F., Lavrencic, L. M., Ridding, M. C., Keage, H. A. D., McDonnell, M. D., & Goldsworthy, M. R. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.08.038 · Cited by 63
Shreve, L., Kaur, A., Vo, C., Wu, J., Cassidy, J. M., Nguyen, A., Zhou, R. J., Tran, T. B., Yang, D. Z., Medizade, A. I., et al. (2019) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2019.05.019 · Cited by 60
Self-citationRole of beta-band resting-state functional connectivity as a predictor of motor learning ability
Sugata, H., Yagi, K., Yazawa, S., Nagase, Y., Tsuruta, K., Ikeda, T., Nojima, I., Hara, M., Matsushita, K., Kawakami, K., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116562 · Cited by 59
Mary, A., Wens, V., Beeck, M. O. D., Leproult, R., Tiège, X. D., & Peigneux, P. (2016) · Human Brain Mapping · doi:10.1002/hbm.23428 · Cited by 54
A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity
Samdin, S. B., Ting, C., Ombao, H., & Salleh, S. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2580738 · Cited by 52
“To synthesize connectivity structures that reflect those in real EEG data, the population distributions for the VAR coefficients were estimated from the VAR fits of ten subjects from real EEG datasets [33] provided by Dr.”
MethodResting-state connectivity predicts visuo-motor skill learning
Manuel, A. L., Guggisberg, A. G., Thézé, R., Turri, F., & Schnider, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.003 · Cited by 52
“However, this result seems at odds with previous findings showing that increased EEG beta-band FC (and to a lesser extent alpha-band) correlates with training on a pursuit rotor task (Wu et al., 2014).”
InfluentialStatistical models for brain signals with properties that evolve across trials
Ombao, H., Fiecas, M. J. A., Ting, C., & Low, Y. F. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.061 · Cited by 54
“These experiments are designed to study cognitive tasks such as learning a new motor skill (Wu et al., 2014); learning a temporal sequence of odor in an olfaction experiment (Allen et al., 2016); behavioral adaptation (Sheth et al., 2012; Gorrostieta et al., 2012); learning to distinguish between…”
BackgroundParietofrontal network upregulation after motor stroke
Bönstrup, M., Schulz, R., Schön, G., Cheng, B., Feldheim, J., Thomalla, G., & Gerloff, C. P. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.03.006 · Cited by 51
“Conceivably, parietofrontal pathways are of high importance for post-stroke rehabilitation (Wu et al., 2014).”
BackgroundInfluentialZhang, R., Li, F., Zhang, T., Yao, D., & Xu, P. (2020) · Brain Science Advances · doi:10.26599/bsa.2020.9050021 · Cited by 55
Modeling Effective Connectivity in High-Dimensional Cortical Source Signals
Wang, Y., Ting, C., & Ombao, H. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2600023 · Cited by 48
Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities
Mary, A., Wens, V., Beeck, M. O. D., Leproult, R., Tiège, X. D., & Peigneux, P. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw286 · Cited by 52
“coherence between primary motor (M1), premotor and parietal cortices during the preceding resting state (Wu et al. 2014).”
BackgroundResultInfluentialVeldman, M. P., Maurits, N. M., Zijdewind, I., Maffiuletti, N. A., van Middelkoop, S., Mizelle, J. C., & Hortobágyi, T. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00860.2017 · Cited by 49
“In addition, the present data reinforce the importance of the frequency range in visuomotor learning processes (Mary et al. 2017; Veldman et al. 2018; Wu et al. 2014).”
BackgroundMethodResultMcGregor, H. E., & Gribble, P. L. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00275.2017 · Cited by 44
Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations
Chen, C., Cao, X., & Tian, L. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01282 · Cited by 46
Immink, M. A., Cross, Z. R., Chatburn, A., Baumeister, J., Schlesewsky, M., & Bornkessel‐Schlesewsky, I. (2021) · Human Movement Science · doi:10.1016/j.humov.2021.102829 · Cited by 48
Yuan, K., Chen, C., Wang, X., Chu, C. W., & Tong, R. K. (2021) · Brain Sciences · doi:10.3390/brainsci11010056 · Cited by 39
“Recent findings illustrated that coherence with 30 the primary motor area (M1) of the dominant hemisphere was a strong predictor of 31 motor skill acquisition and thus could provide more information than that predicted 32 by baseline behavior and demographics [85].”
Li, X.-M., Ma, R., Pang, L., Lv, W., Xie, Y., Chen, Y., Zhang, P., Chen, J., Wu, Q., Cui, G.-B., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-01373-4 · Cited by 34
Estimating Fugl-Meyer Upper Extremity Motor Score From Functional-Connectivity Measures
Riahi, N., Vakorin, V. A., & Menon, C. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2978381 · Cited by 37
“For the second stage, we implemented two different approaches; one was to average over all epochs [31] and the other was to use the”
MethodBauer, R. H., Fels, M., Royter, V., Raco, V., & Gharabaghi, A. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.06.020 · Cited by 37
“Furthermore, betaband connectivity of motor areas has been implied in predicting motor learning (Wu et al. 2014).”
BackgroundHordacre, B., Moezzi, B., Goldsworthy, M., Rogasch, N., Graetz, L., & Ridding, M. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13508 · Cited by 36
Age-Dependent Modulations of Resting State Connectivity Following Motor Practice
Solesio‐Jofre, E., Beets, I. A. M., Woolley, D. G., Pauwels, L., Chalavi, S., Mantini, D., & Swinnen, S. P. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00025 · Cited by 36
“Importantly, rs-FC has also been shown to correlate with motor improvement in young adults (Ma et al., 2011; Vahdat et al., 2011; Wu et al., 2014; Zhang et al., 2014), indicating that functional network reorganization can, to some extent, predict behavioral changes (Tambini et al., 2010; Deco and…”
BackgroundResultInfluentialBartsch, U., Simpkin, A. J., Demanuele, C., Wamsley, E. J., Marston, H. M., & Jones, M. W. (2019) · Schizophrenia · doi:10.1038/s41537-019-0086-8 · Cited by 38
Zhou, R. J., Hondori, H. M., Khademi, M., Cassidy, J. M., Wu, K. M., Yang, D. Z., Kathuria, N., Erani, F. R., Dodakian, L., McKenzie, A. L., et al. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00597 · Cited by 34
“The motor-parietal circuit targeted in the current study was identified in a prior study of healthy subjects (13) that identified predictors of training-related gains in visuomotor tracking skill, a behavior central to many forms of rehabilitation therapy after stroke.”
BackgroundMethodInfluentialSelf-citationFaiman, I., Pizzamiglio, S., & Turner, D. L. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.054 · Cited by 31
“TE The variable reduction method implemented in this study is inspired by PLS wrapper methods (Mehmood et al., 2012), in particular to the backward variable selection PLS (BVSPLS; Pierna et al., 2009) and to the threshold method on regression coefficients implemented by Wu et al. (2014).”
InfluentialElectroencephalographic identifiers of motor adaptation learning
Özdenizci, O., Yalçın, M., Erdoğan, A., Patoğlu, V., Grosse‐Wentrup, M., & Çetin, M. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa6abd · Cited by 28
The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure
Euán, C., Ombao, H., & Ortega, J. (2018) · Journal of Classification · doi:10.1007/s00357-018-9250-5 · Cited by 28
Effects of transcranial direct-current stimulation on laparoscopic surgical skill acquisition
Ciechanski, P., Cheng, A., Damji, O., Lopushinsky, S. R., Hecker, K. G., Jadavji, Z., & Kirton, A. (2018) · BJS Open · doi:10.1002/bjs5.43 · Cited by 28
Ombao, H., & Pinto-Orellana, M. A. (2022) · Econometrics and Statistics · doi:10.1016/j.ecosta.2022.10.005 · Cited by 24
An exploratory data analysis of electroencephalograms using the functional boxplots approach
Ngo, D., Sun, Y., Genton, M. G., Wu, J., Srinivasan, R., Cramer, S. C., & Ombao, H. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00282 · Cited by 21
“As demonstrated in Wu et al. (2014), the spectral features of the resting-state EEGs when combined with a partial least squares regression analysis, was predictive of an individual’s subsequent ability to acquire a novel motor skill.”
BackgroundSelf-citationTakeuchi, N., & Izumi, S. (2021) · Brain Sciences · doi:10.3390/brainsci11081095 · Cited by 20
“A study reported that beta-band resting-state functional connectivity between M1 and the parietal area is positively associated with motor skill acquisition [87].”
BackgroundMotor Skill Acquisition and Retention after Somatosensory Electrical Stimulation in Healthy Humans
Veldman, M. P., Zijdewind, I., Maffiuletti, N. A., & Hortobágyi, T. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00115 · Cited by 23
“For example, synchronization of neural oscillations in the gamma/theta band within the parietal region (Perfetti et al., 2011) and beta coherence between M1 and parietal areas (Wu et al., 2014) has been shown to correlate with the magnitude of motor learning.”
BackgroundVeldman, M. P., Maurits, N. M., Nijland, M., Wolters, N. E., Mizelle, J. C., & Hortobágyi, T. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.12.003 · Cited by 22
Lum, J. A. G., Clark, G. M., Bigelow, F. J., & Enticott, P. G. (2022) · Brain and Language · doi:10.1016/j.bandl.2022.105137 · Cited by 19
Wang, C., Chen, Y., Song, P., Yu, H., Du, J., Zhang, Y., & Sun, C. (2022) · Neural Plasticity · doi:10.1155/2022/7790730 · Cited by 19
“Connectivity in the alpha band has been proven to be associated with atDCS response strongly in stroke subjects, and high beta connectivity could predict response to tDCS and motor learning in healthy adults [24, 25].”
BackgroundEEG-based functional connectivity to analyze motor recovery after stroke: A pilot study
Eldeeb, S. M., Akçakaya, M., Sybeldon, M., Foldes, S. T., Santarnecchi, E., Pascual‐Leone, Á., & Sethi, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.12.022 · Cited by 19
Lum, J. A. G., Byrne, L. K., Barhoun, P., Hyde, C. E., Hill, A. T., Enticott, P. G., & Clark, G. M. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16059 · Cited by 18
“…before completing a task has repeatedly been shown to correlate with individual differences in subsequent skill acquisition and learning (e.g., Manuel et al., 2018; Mathewson et al., 2012; Mizuguchi et al., 2019; Stillman et al., 2013; Sugata et al., 2020; van Dyck et al., 2021; Wu et al., 2014).”
BackgroundZou, F., Lin, Y., Chen, S., Cao, L., Wang, H., Ye, B., Wang, Q., He, J., & Jia, J. (2020) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2020/8839491 · Cited by 18
“Previous research also suggested delta band power in M1 was related to a better motor status [21, 31].”
BackgroundQuantifying the performance of MEG source reconstruction using resting state data
Little, S. J., Bonaiuto, J. J., Meyer, S. S., López, J. D., Bestmann, S., & Barnes, G. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.030 · Cited by 17
Han, J., Embs, A., Nardi, F., Haar, S., & Faisal, A. A. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3530110 · Cited by 16
“These factors limit the ability to use these state-of-the-art models for identifying biomarkers during the human motor learning process since the process is heavily influenced by both functional connectivity and spatial connectivity [26], [27].”
Nackaerts, E., D’Cruz, N., Dijkstra, B. W., Gilat, M., Kramer, T., & Nieuwboer, A. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190071 · Cited by 16
Extended Interaction With a BCI Video Game Changes Resting-State Brain Activity
Singh, A. K., Wang, Y., King, J., & Lin, C. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.2985102 · Cited by 18
“The resting-state brain correlates with different aspects of the brain, such as motor learning [34], cognitive training [12], memory consolidation [21], drug addiction [35], and cognitive impairment [36].”
BackgroundPenalver-Andres, J. A., Buetler, K. A., Koenig, T. W., Müri, R. M., & Marchal–Crespo, L. (2022) · Brain Topography · doi:10.1007/s10548-022-00934-9 · Cited by 15
“…connectivity between motor and sensory integration-related brain regions—e.g., involving the Primary Motor Cortex (M1) and the Parietal Cortex (PC) (Wu et al 2014; Manuel et al 2018; Berti et al 2019) or other areas belonging to the Sensorimotor Network (SMN) (Hong et al 2019; Carter et al 2010;…”
BackgroundElectroencephalographic connectivity measures predict learning of a motor sequencing task
Wu, J., Knapp, F., Cramer, S. C., & Srinivasan, R. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00580.2017 · Cited by 16
“Those results suggested that increased PMC connectivity indicated an inefficient motor system for acquisition of a visuomotor integration task that emphasized parietal cortex activation (Cavaco et al. 2015; Wu et al. 2014).”
BackgroundMethodResultInfluentialSelf-citationFunctional independence in resting-state connectivity facilitates higher-order cognition
James, G. A., Kearney-Ramos, T. E., Young, J. A., Kilts, C. D., Gess, J. L., & Fausett, J. S. (2016) · Brain and Cognition · doi:10.1016/j.bandc.2016.03.008 · Cited by 15
The Effects of rTMS Combined with Motor Training on Functional Connectivity in Alpha Frequency Band
Jin, J., Wang, X., Li, Y., Jin, F., Liu, Z., & Yin, T. (2017) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2017.00234 · Cited by 16
“Researchers have reported that the connectivity of the resting cortex could predict motor skills, and the connectivity between the frontal and parietal cortices were significantly correlated with the ability to perform motor skills (Wu et al., 2014).”
BackgroundResultZang, Z., Geiger, L. S., Braun, U., Cao, H., Zangl, M., Schäfer, A., Moessnang, C., Ruf, M., Reis, J., Schweiger, J. I., et al. (2018) · Network Neuroscience · doi:10.1162/netn_a_00045 · Cited by 15
Herszage, J., Dayan, E., Sharon, H., & Censor, N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00076 · Cited by 13
Wang, C., Yang, X., Guo, D., Huo, W., Yu, N., & Zhang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-66464-5 · Cited by 13
“EEG-based measures of connectivity have been reported to predict motor gains in both healthy individuals and patients with stroke 16,17 .”
Spectral synchronicity in brain signals
Euán, C., Ombao, H., & Ortega, J. (2018) · Statistics in Medicine · doi:10.1002/sim.7695 · Cited by 13
Predicting individual differences in motor learning: A critical reviewPreprint
Ranganathan, R., Cone, S., & Fox, B. (2022) · PsyArXiv (OSF Preprints) · doi:10.1016/j.neubiorev.2022.104852 · Cited by 19
Song, Y., Adams, S. E., & Legon, W. (2020) · Cortex · doi:10.1016/j.cortex.2020.04.033 · Cited by 13
Posterior parietal cortical areas and recovery after motor stroke: a scoping review
Reibelt, A., Quandt, F., & Schulz, R. (2023) · Brain Communications · doi:10.1093/braincomms/fcad250 · Cited by 11
“18 One important structural substrate of these interactions is dense connections between M1, ventral PMC and anterior and caudal IPS via the superior longitudinal fascicles.”
BackgroundAerobic exercise and action observation priming modulate functional connectivity
Mark, J. I., Ryan, H. P., Fabian, K., DeMarco, K., Lewek, M. D., & Cassidy, J. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283975 · Cited by 11
Duan, Y., Hua, X., Zheng, M., Wu, J., Xing, X., Li, Y., & Xu, J. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-053991 · Cited by 10
Self-citationHome-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME II)
Casas, R., Sandison, M., Nichols, D., Martin, K., Phan, K., Chen, T., & Lum, P. S. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.773477 · Cited by 10
“Research also indicates that FC measures at rest are biomarkers of cortical function post-stroke (Zhu et al., 2010; Rehme et al., 2011; Dubovik et al., 2012; Wu et al., 2015).”
BackgroundFeature-based attentional tuning during biological motion detection measured with SSVEP
Hasan, R., Srinivasan, R., & Grossman, E. D. (2017) · Journal of Vision · doi:10.1167/17.9.22 · Cited by 9
“In a previous study, PLS methods successfully predicted acquisition of motor skill in a future task from brain connectivity during resting state (Wu et al., 2014).”
MethodSelf-citationManuel, A. L., Nicastro, N., Schnider, A., & Guggisberg, A. G. (2018) · Cortex · doi:10.1016/j.cortex.2018.06.005 · Cited by 9
Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection
Gao, J., Wang, W., & Zhang, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6184823 · Cited by 10
Maadooliat, M., Sun, Y., & Chen, T. (2018) · Statistics in Medicine · doi:10.1002/sim.7972 · Cited by 9
Veldman, M. P., Dolfen, N., Gann, M. A., Carrier, J., King, B. R., & Albouy, G. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.03.027 · Cited by 10
Regan, E. W., Fridriksson, J., Schaefer, S. Y., Rorden, C., Bonilha, L., VanGilder, J. L., & Stewart, J. C. (2020) · Experimental Brain Research · doi:10.1007/s00221-020-05964-y · Cited by 9
Neural correlates of motor learning: Network communication versus local oscillations
Mottaz, A., Savic, B., Allaman, L., & Guggisberg, A. G. (2024) · Network Neuroscience · doi:10.1162/netn_a_00374 · Cited by 9
“Indeed, several studies showed that resting-state FC in alpha and beta frequency bands predicts motor learning (Manuel et al., 2018; Mary et al., 2017; Van Dyck et al., 2021; Wu et al., 2014).”
BackgroundInfluentialRecognizing Tonal and Nontonal Mandarin Sentences for EEG-Based Brain–Computer Interface
Yang, S.-R., Jung, T., Lin, C., Huang, K., Wei, C.-S., Chiueh, H., Hsin, Y.-L., Liou, G.-T., & Wang, L. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3137251 · Cited by 9
“According to previous studies, emotion might affect brain activities at the rest state [53], [54], [75].”
BackgroundA Method for Using Neurofeedback to Guide Mental Imagery for Improving Motor Skill
Riahi, N., Ruth, W., D’Arcy, R. C. N., & Menon, C. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3218514 · Cited by 8
“Partial Least Square (PLS) algorithms [12], [42] were employed to select EEG electrode-pairs (channels) and frequency bands that correlated with changes in motor skill.”
BackgroundMethodInitial motor skill performance predicts future performance, but not learning
Abeles, D., Herszage, J., Shahar, M., & Censor, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-38231-5 · Cited by 11
Exploratory Analysis of Brain Signals through Low Dimensional Embedding
Wang, Y., Ting, C., Gao, X., & Ombao, H. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8716924 · Cited by 8
“Various analysis have been performed on the dataset, including using brain connectivity as predictor for ability of motor skill acquisitions [18], applying graphical model on frequency domain to study the functional connectivity [19] and the analysis of curves of log periodograms using functional boxplots [20].”
MethodResting state fast brain dynamics predict interindividual variability in motor performance
Roshchupkina, L., Wens, V., Coquelet, N., Tiège, X. D., & Peigneux, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-08767-z · Cited by 8
Kandaleft, D., Murayama, K., Roesch, E. B., & Sakaki, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-18543-8 · Cited by 11
Dynamic rewiring of electrophysiological brain networks during learning
Ruggeri, P., Miehlbradt, J., Kabbara, A., & Hassan, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00289 · Cited by 6
“4.2 Experimental setup and procedure We used the same experimental protocol developed by Bassett et al. (2013b) and previously employed in a large variety of studies (Bassett et al., 2015; Mattar et al., 2018; Wymbs and Grafton, 2015).”
BackgroundMethodResultInfluentialPixa, N. H., Berger, A., Steinberg, F., & Doppelmayr, M. M. (2018) · Journal of Cognitive Enhancement · doi:10.1007/s41465-018-0088-x · Cited by 8
Wansbrough, K., Marinovic, W., Fujiyama, H., & Vallence, A. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1425527 · Cited by 7
A dynamic directed transfer function for brain functional network-based feature extraction
Li, M., & Zhang, N. (2022) · Brain Informatics · doi:10.1186/s40708-022-00154-8 · Cited by 7
“(13-30Hz) ,with β1 or lower β band (13-21Hz) and β2 or higher β band (21-30Hz) included, has been reported to be good physiological predictors during motor imagery tasks comparing to other frequency bands [41].”
BackgroundTDCS effects on pointing task learning in young and old adults
Kaminski, E., Engelhardt, M., Hoff, M., Steele, C. J., Villringer, A., & Ragert, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-82275-4 · Cited by 8
Hooyman, A., Garbin, A. J., Fisher, B. E., Kutch, J. J., & Winstein, C. J. (2022) · Neuroscience Letters · doi:10.1016/j.neulet.2022.136753 · Cited by 7
Veldman, M. P., Maurits, N. M., Mantini, D., & Hortobágyi, T. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.03.051 · Cited by 7
“We limited the analyses to neural oscillations in the beta frequency range, as they are known to be involved in sensorimotor processing and motor learning (Bhatt et al., 2016; Pfurtscheller and Lopes, 1999; Pollok et al., 2014; Rossiter et al., 2014; Sugata et al., 2020; Veldman et al., 2018; Wu et al., 2014).”
MethodResultInfluentialMackenzie, A. K., Baker, J., Daly, R. C., & Howard, C. J. (2024) · Brain and Behavior · doi:10.1002/brb3.3434 · Cited by 6
“…were deemed an appropriate resting period to derive resting state alpha given previous literature (e.g., Gutmann et al., 2015; Lum et al., 2022; Wu et al., 2014) where increasing the duration would not offer increased precision in deriving PAF, and a short duration allows participants to…”
MethodClustering Brain Signals: a Robust Approach Using Functional Data Ranking
Chen, T., Sun, Y., Euán, C., & Ombao, H. (2020) · Journal of Classification · doi:10.1007/s00357-020-09382-1 · Cited by 6
“In the Supplementary Material, we provide the rank sum test (López-Pintado and Romo 2009) of the identified clusters and the clustering results of another subject in Wu et al. (2014).”
MethodInfluentialHordacre, B. G., Goldsworthy, M. R., Graetz, L. J., & Ridding, M. C. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02299-4 · Cited by 6
Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity
Maddaluno, O., Penna, S. D., Pizzuti, A., Spezialetti, M., Corbetta, M., de Pasquale, F., & Betti, V. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1766-23.2024 · Cited by 6
“In addition, resting state cortical connectivity predicts future motor skill and visual perceptual learning acquisition (Baldassarre et al., 2012; Wu et al., 2014; Dyck et al., 2021) and interindividual variability in motor performance (Roshchupkina et al., 2022).”
Background(2012) · Elsevier eBooks · doi:10.1016/c2011-0-09656-4 · Cited by 7
Commentary: Utility of EEG measures of brain function in patients with acute stroke
Hordacre, B. G., Rogasch, N. C., & Goldsworthy, M. R. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00621 · Cited by 6
“Furthermore, beta coherence in sensorimotor regions recorded at rest was found to be a strong predictor of motor learning (Wu et al., 2014).”
Background
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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