Neuroplasticity in Motor Learning Under Variable and Constant Practice Conditions-Protocol of Randomized Controlled Trial

Stanisław H Czyż, Jarosław Marusiak, Patrícia Klobušiaková, Zuzana Sajdlová, Irena Rektorová, Stanisław H Czyż, Jarosław Marusiak, Patrícia Klobušiaková, Zuzana Sajdlová, Irena Rektorová

Abstract

Background: There is numerous literature on mechanisms underlying variability of practice advantages. Literature includes both behavioral and neuroimaging studies. Unfortunately, no studies are focusing on practice in constant conditions to the best of our knowledge. Hence it is essential to assess possible differences in mechanisms of neuroplasticity between constant vs. variable practice conditions. The primary objectives of the study described in this protocol will be: (1) to determine the brain's structural and functional changes following constant and variable practice conditions in motor learning (structural and functional magnetic resonance imaging, MRI); (2) to determine the EEG activation and connectivity between cognitive, sensory, and motor cerebral cortex areas (central, temporal, parietal, occipital) in constant and variable practice conditions and as a function of practice time.

Methods: The study will follow the interventional (experimental) design with two arms (parallel groups). Fifty participants will be randomly assigned to two groups practicing in constant (CG) and variable conditions (VG). CG will be practicing only one pattern of step isometric contractions during unimanual index finger abduction, i.e., 90 trials in all training sessions, whereas VG will practice three different patterns. Each will be practiced 30 times per session in variable conditions. Resting-state fMRI, EEG (cortical networking), and motor task proficiency will be examined before (pre-) and after practice (post- and retentions tests).

Discussion: Findings will enhance our understanding of structural and functional neural changes following practice in constant and variable conditions. Therefore, the study can be considered pure (basic) research (clinical research in healthy individuals).

Clinical trial registration: Study registered at clinicaltrials.gov (ID# NCT04921072) on 9 June 2021. Last version update: 21 December 2021.The protocol has been prepared according to the complete SPIRIT checklist (http://www.spirit-statement.org/), although the item order has been modified in order to comply with the manuscript structure.

Keywords: motor learning; neuroplasticity; practice conditions; sensorimotor cortex activity; specificity of practice; variability of practice.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Czyż, Marusiak, Klobušiaková, Sajdlová and Rektorová.

Figures

Figure 1
Figure 1
Flowchart of the study.
Figure 2
Figure 2
Set-up for the unimanual index finger (UIFA) abduction motor task.
Figure 3
Figure 3
Exemplary scheme of the motor task displayed during tests and training. The final values of UIFA task during each SPSIC are not shown as we would like to ensure the novelty of the task to the participants.

References

    1. Ashburner J., Friston K. J. (2000). Voxel-based morphometry—the methods. Neuroimage 11, 805–821. 10.1006/nimg.2000.0582
    1. Beek P. J., Lewbel A. (1995). The science of juggling. Sci. Am. 273, 92–97.
    1. Behrens T. E. J., Berg H. J., Jbabdi S., Rushworth M. F. S., Woolrich M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain. Neuroimage 34, 144–155. 10.1016/j.neuroimage.2006.09.018
    1. Borson S., Scanlan J. M., Chen P., Ganguli M. (2003). The mini-cog as a screen for dementia: validation in a population-based sample. J. Am. Geriatr. Soc. 51, 1451–1454. 10.1046/j.1532-5415.2003.51465.x
    1. Boyke J., Driemeyer J., Gaser C., Büchel C., May A. (2008). Training-induced brain structure changes in the elderly. J. Neurosci. 28, 7031–7035. 10.1523/JNEUROSCI.0742-08.2008
    1. Breslin G., Hodges N. J., Steenson A., Williams A. M. (2012). Constant or variable practice: recreating the especial skill effect. Acta Psychol. (Amst). 140, 154–157. 10.1016/j.actpsy.2012.04.002
    1. Busk J., Galbraith G. (1975). EEG correlates of visual-motor practice in man. Electroencephalogr. Clin. Neurophysiol. 38, 415–422. 10.1016/0013-4694(75)90265-5
    1. Cheng M.-Y., Hung C.-L., Huang C.-J., Chang Y.-K., Lo L.-C., Shen C., et al. . (2015). Expert-novice differences in SMR activity during dart throwing. Biol. Psychol. 110, 212–218. 10.1016/j.biopsycho.2015.08.003
    1. Czyż S. H. (2021). Variability of practice, information processing and decision making—how much do we know? Front. Psychol. 12:639131. 10.3389/fpsyg.2021.639131
    1. Czyż S. H., Moss S. J. (2016). Specificity vs. generalizability: emergence of especial skills in classical archery. Front. Psychol. 7:1178. 10.3389/fpsyg.2016.01178
    1. Dean A., Sullivan K., Soe M. (2013). OpenEpi: open source epidemiologic statistics for public health. Available Online at: . Accessed April 23, 2019.
    1. Debowska W., Wolak T., Nowicka A., Kozak A., Szwed M., Kossut M. (2016). Functional and structural neuroplasticity induced by short-term tactile training based on braille reading. Front. Neurosci. 10:460. 10.3389/fnins.2016.00460
    1. Deeny S. P., Haufler A. J., Saffer M., Hatfield B. D. (2009). Electroencephalographic coherence during visuomotor performance: a comparison of cortico-cortical communication in experts and novices. J. Mot. Behav. 41, 106–116. 10.3200/JMBR.41.2.106-116
    1. Deeny S. P., Hillman C. H., Janelle C. M., Hatfield B. D. (2003). Cortico-cortical communication and superior performance in skilled marksmen: an EEG coherence analysis. J. Sport Exerc. Psychol. 25, 188–204. 10.1123/jsep.25.2.188
    1. Doyon J., Bellec P., Amsel R., Penhune V., Monchi O., Carrier J., et al. . (2009). Contributions of the basal ganglia and functionally related brain structures to motor learning. Behav. Brain Res. 199, 61–75. 10.1016/j.bbr.2008.11.012
    1. Draganski B., Gaser C., Busch V., Schuierer G., Bogdahn U., May A. (2004). Changes in grey, matter induced by training. Nature 427, 311–312. 10.1038/427311a
    1. Draganski B., Kherif F., Lutti A. (2014). Computational anatomy for studying use-dependant brain plasticity. Front. Hum. Neurosci. 8:380. 10.3389/fnhum.2014.00380
    1. Egner T., Gruzelier J. H. (2001). Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport 12, 4155–4159. 10.1097/00001756-200112210-00058
    1. Egner T., Gruzelier J. H. (2003). Ecological validity of neurofeedback: Modulation of slow wave EEG enhances musical performance. Neuroreport 14, 1221–1224. 10.1097/01.wnr.0000081875.45938.d1
    1. Fischl B., Salat D. H., Busa E., Albert M., Dieterich M., Haselgrove C., et al. . (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355. 10.1016/s0896-6273(02)00569-x
    1. Floyer-Lea A., Matthews P. M. (2005). Distinguishable brain activation networks for short- and long-term motor skill learning. J. Neurophysiol. 94, 512–518. 10.1152/jn.00717.2004
    1. Keetch K. M., Schmidt R. A., Lee T. D., Young D. E. (2005). Especial skills: their emergence with massive amounts of practice. J. Exp. Psychol. Hum. Percept. Perform. 31, 970–978. 10.1037/0096-1523.31.5.970
    1. Kim T., Kim H., Wright D. L. (2021a). Improving consolidation by applying anodal transcranial direct current stimulation at primary motor cortex during repetitive practice. Neurobiol. Learn. Mem. 178:107365. 10.1016/j.nlm.2020.107365
    1. Kim T., Wright D. L., Feng W. (2021b). Commentary: variability of practice, information processing and decision making—how much do we know? Front. Psychol. 12:685749. 10.3389/fpsyg.2021.685749
    1. Knapp C., Dixon W. (1952). Learning to juggle: II. a study of whole and part methods. Res. Q. 23, 398–401. 10.1080/10671188.1950.10624864
    1. Lelis-Torres N., Ugrinowitsch H., Apolinário-Souza T., Benda R. N., Lage G. M. (2017). Task engagement and mental workload involved in variation and repetition of a motor skill. Sci. Rep. 7:14764. 10.1038/s41598-017-15343-3
    1. Lin C. H., Yang H. C., Knowlton B. J., Wu A. D., Iacoboni M., Ye Y. L., et al. . (2018). Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation. Neuroimage 181, 1–15. 10.1016/j.neuroimage.2018.06.081
    1. Loffing F., Sölter F., Hagemann N. (2014). Left preference for sport tasks does not necessarily indicate left-handedness: sport-specific lateral preferences, relationship with handedness and implications for laterality research in behavioural sciences. PLoS One 9:e105800. 10.1371/journal.pone.0105800
    1. Lohse K. R., Wadden K., Boyd L. A., Hodges N. J. (2014). Motor skill acquisition across short and long time scales: a meta-analysis of neuroimaging data. Neuropsychologia 59, 130–141. 10.1016/j.neuropsychologia.2014.05.001
    1. Mang C. S., Borich M. R., Wadden K. P., Boyd L. A., Siengsukon C. F. (2020). “Motor skill learning and its neurophysiology,” in Skill Acquisition in Sport: Research, Theory and Practice, eds Hodges N. J., Williams A. M. (London, New York: Routledge; ), 293–312.
    1. McCracken H. D., Stelmach G. E. (1977). A Test of the schema theory of discrete motor learning. J. Mot. Behav. 9, 193–201. 10.1080/00222895.1977.10735109
    1. Nunez P. L., Silberstein R. B., Shi Z., Carpenter M. R., Srinivasan R., Tucker D. M., et al. . (1999). EEG coherency II: experimental comparisons of multiple measures. Clin. Neurophysiol. 110, 469–486. 10.1016/s1388-2457(98)00043-1
    1. Nunez P. L., Srinivasan R., Westdorp A. F., Wijesinghe R. S., Tucker D. M., Silberstein R. B., et al. . (1997). EEG coherency I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging and interpretation at multiple scales. Electroencephalogr. Clin. Neurophysiol. 103, 499–515. 10.1016/s0013-4694(97)00066-7
    1. Oldfield R. C. (1971). The assessment and analysis of handedness: the edinburgh inventory. Neuropsychologia 9, 97–113. 10.1016/0028-3932(71)90067-4
    1. Schmidt R. A. (1975). A schema theory of discrete motor skill learning. Psychol. Rev. 82, 225–260. 10.1080/02701367.1981.10607893
    1. Schmidt R. A. (2003). Motor schema theory after 27 years: reflections and implications for a new theory. Res. Q. Exerc. Sport 74, 366–375. 10.1080/02701367.2003.10609106
    1. Schmidt R. A., Lee T. D. (2011). Motor Control and Learning: a Behavioral Emphasis. 5th Edn. Champaign, IL: Human Kinetics.
    1. Smith S. M., Jenkinson M., Johansen-Berg H., Rueckert D., Nichols T. E., Mackay C. E., et al. . (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505. 10.1016/j.neuroimage.2006.02.024
    1. Van Rossum J. H. A. (1987). Motor Development and Practice: The Variability of Practice Hypothesis in Perspective. Amsterdam: Free University Press.
    1. Vernon D., Egner T., Cooper N., Compton T., Neilands C., Sheri A., et al. . (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int. J. Psychophysiol. 47, 75–85. 10.1016/s0167-8760(02)00091-0

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