Elsevier

NeuroImage

Volume 88, March 2014, Pages 32-40
NeuroImage

White matter microstructure changes induced by motor skill learning utilizing a body machine interface

https://doi.org/10.1016/j.neuroimage.2013.10.066Get rights and content

Highlights

  • White matter changes induced by motor training with a body-machine interface.

  • On average fractional anisotropy increased by 4.19% with training.

  • FA increases were mostly localized to non-dominant corticospinal tract.

  • Findings suggest functional reorganization associated with motor skill learning.

Abstract

The purpose of this study is to identify white matter microstructure changes following bilateral upper extremity motor skill training to increase our understanding of learning-induced structural plasticity and enhance clinical strategies in physical rehabilitation. Eleven healthy subjects performed two visuo-spatial motor training tasks over 9 sessions (2–3 sessions per week). Subjects controlled a cursor with bilateral simultaneous movements of the shoulders and upper arms using a body machine interface. Before the start and within 2 days of the completion of training, whole brain diffusion tensor MR imaging data were acquired. Motor training increased fractional anisotropy (FA) values in the posterior and anterior limbs of the internal capsule, the corona radiata, and the body of the corpus callosum by 4.19% on average indicating white matter microstructure changes induced by activity-dependent modulation of axon number, axon diameter, or myelin thickness. These changes may underlie the functional reorganization associated with motor skill learning.

Introduction

Performing complex motor skills is a fundamental component of ordinary human life. The ability to learn and modify motor skills is a requisite for adapting to an ever-changing environment (Davidson and Wolpert, 2003). Through practice, new motor skills are acquired and existing ones are continuously refined. Motor skill learning (acquisition, retention, and refinement of motor skills) relies on the capability of the nervous system to create new patterns of neural activation for accomplishing new tasks or for recovering motor functions lost to disability (Kantak and Winstein, 2012). This reorganization is a continuous process throughout life as the nervous system recruits the necessary neural components to optimize task performance and meet environmental demands.

Most models of learning have been developed around the Hebbian theory of plasticity (Dudai, 1989, Hebb, 1949). For healthy adults, the overall brain structure was seen as rather static and inert, and historically, the significance of learning induced-plastic changes at the structural level had been mostly disregarded (Fields, 2011). Recent advances in neural imaging have made in vivo characterization of the nervous system microstructure possible. Information gained from these technologies has advanced our understanding of the relationship between brain structure and learning, and recent studies have begun demonstrating that the brain at the structural level is a much more dynamic organ than we were previously aware.

Learning-induced structural changes of cortical and subcortical areas have been reported to occur in both gray matter and white matter. Using voxel-based morphometry, cross-sectional studies have identified regional differentiation of gray matter volume between expert and non-expert musicians (Bermudez and Zatorre, 2005, Gaser and Schlaug, 2003, Han et al., 2009), golfers (Jancke et al., 2009), and basketball players (Park et al., 2009). Additionally, the magnitude of these gray matter changes has further been shown to correlate with experience (e.g., years spent typing for professional typists) (Cannonieri et al., 2007, Maguire et al., 2000). Longitudinal studies have further strengthened the link between structural plasticity and learning (Boyke et al., 2008, Draganski et al., 2004, Driemeyer et al., 2008, Scholz et al., 2009, Taubert et al., 2010). Following 3 months of practicing a motor task (juggling), Draganski and coworkers demonstrated transient increases in gray matter volume in regions associated with visual motion processing (Draganski et al., 2004). Strikingly, Driemeyer and coworkers have reported structural changes after only 7 days of juggling practice (Driemeyer et al., 2008). Paralleling the changes seen in gray matter, several studies (Bengtsson et al., 2005, Han et al., 2009) have demonstrated regional differentiation of white matter tracts by using diffusion tensor imaging (DTI). DTI non-invasively measures the direction and rate of water diffusion within tissue. Water restricted by white matter fibers results in anisotropic diffusion along the axon. The common measure of diffusion anisotropy used in DTI studies is a normalized measure of the variance of the diffusion ellipsoid at each voxel called fractional anisotropy (FA) (Basser and Pierpaoli, 1996). The physiological parameters that affect the FA value include axon number, axon diameter, and myelin thickness of the white matter tissue (Beaulieu, 2009). DTI studies investigating motor skill learning-induced structural changes in white matter have employed juggling (Scholz et al., 2009) and balance (Taubert et al., 2010) tasks.

Here, we investigate learning-induced changes in brain connectivity following training with a body machine interface (BMI), where subjects learn to use the movement of their shoulders and upper arms to control a cursor on a computer screen to solve different tasks. The purpose of this study is to identify white matter changes by comparing FA values pre- and post-bilateral upper extremity motor skill training in healthy subjects. This will lead to a greater understanding of learning-induced structural plasticity and, specifically, the neural substrates responsible for the reorganization of residual motor ability. This information will potentially aid in enhancing clinical strategies in physical rehabilitation and facilitate the learning processes related to assistive devices used in impaired subjects.

Section snippets

Subjects

Eleven healthy, right-handed volunteers (mean age, 26 years; range: 22–35 years; 3 females), with no known history of motor impairment participated in this study, after obtaining written informed consent approved by the local ethics committee. Exclusion criteria were: professional musicians for potentially bilateral increased use of upper extremities and smokers for possible brain structure and functional changes induced by nicotine administration and addiction (Lee et al., 2013). One subject

Motor performance

Subjects improved their performance with training and the improvement was evident in all tasks. As shown in Fig. 2A, in the Tetris game the average rows cleared per minute increased from 1.4 ± 0.14 at baseline (session 1) to 4.8 ± 0.29 (mean ± SE) at the end of the training (session 9) (p < 0.001). The average driving speed of the virtual wheelchair measured during navigation under the same environment increased from a baseline speed of 3.7 ± 0.15 to 4.1 ± 0.09 miles per hour during the final phase of

Discussion

Our results show that 9 h of motor skill training over 3 to 4 weeks improved motor performance and induced mainly unilateral increases in FA values in distributed white matter structures over the right hemisphere (non-dominant motor tracts), especially along the corticospinal tract and corpus callosum. There was a 4.19% increase in FA value from 0.5327 to 0.5548, which is in line with the juggling study of Scholz et al. (2009). Furthermore, the degree of changes in FA is positively correlated

Conclusion

Although a growing number of structural neuroimaging studies have reported significant changes in gray matter density and white matter microstructure in the adult human brain following training, training-dependent structural plasticity in humans is still controversial (Draganski and Kherif, 2013, Erickson, 2013, Fields, 2013, Thomas and Baker, 2013a, Thomas and Baker, 2013b). Our findings provide further evidence suggesting that the practice of upper-body motions within the context of a novel

Acknowledgments

This study was supported by NICHHD grants 1R21HD053608-01A1, 1T32HD057845-01A2, and 1R01HD072080-01, NIDRR grant H133E120010, NCCAM grant 1F32AT007800-01, Neilsen Foundation, Brinson Foundation, FP7-PEOPLE-2012-CIG-334201 (REMAKE) and the Center for Translational Imaging, Department of Radiology, Northwestern University.

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