Drum Training induces MR visible changes in the Cerebellum and Cortex

Muriel M.K. Bruchhage¹, Ali Amad¹, Stephen B. Draper², Jade Seidman¹, Flavio Dell'Acqua³, Luis Lacerda³, Pedro Luque Laguna³, Ruth G. Lowry⁴, Andrew Robertson⁵, Marcus S. Smith⁴, and Steven C.R. Williams¹

¹Department of Neuroimaging, King's College London, The Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom, ²School of Sport and Exercise, University of Gloucestershire, Chichester, United Kingdom, ³NatBrainLab, Department of Neuroimaging, King's College London, The Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom, ⁴Department of Sport and Exercise, University of Chichester, Chichester, United Kingdom, ⁵Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University, London, United Kingdom

Synopsis

Cerebellar networks show long-term plasticity and motor training has been shown to change cerebellar microstructure and cortical thickness. We used a combination of neuroimaging measures to visualise plastic changes in drumming - a demanding multilimb training method: cerebellar lobular volume and shape analysis, cortical thickness and diffusion tensor imaging. Drum training reorganises and reshapes the posterior cerebellum, expanding to connected parietal and prefrontal cortical structures through the inferior cerebellar white matter pathway. Thus, it may offer a novel method for cerebellar and cortical plasticity, relevant as an intervention method for psychiatric disorders connected to cerebellar dysfunction, including autism spectrum disorder.

Introduction

The cerebellum is capable of experience-dependent plasticity and is highly involved in learning processes.¹ Indeed, cerebellar networks show long-term plasticity,² and motor training changes cerebellar microstructure. Cerebellar learning occurs in two stages, where cerebellar internal reorganisation is followed by plastic changes in the cerebral cortex.³ Specifically, motor training has been shown to increase posterior cerebellar volume and cortical thickness of parietal and frontal regions, and/ or change white matter microstructure of the inferior cerebellar peduncles.^4,5 Thus, we used a combination of measures to visualise plastic changes, namely shape analysis, cortical thickness and diffusion tensor imaging (DTI) in addition to traditional volume analysis on a demanding multilimb training method.

Methods

Sample. Thirty-one right-handed age- and gender-matched healthy volunteers (16-19 years) with no prior drumming experience and no psychiatric or neurological disorders were randomly assigned to a drumming or control group. Following the first drumming assessment and scanning session (T1), the drumming group took part in 3x30-minute drumming sessions per week for 8 weeks. Drumming was assessed on the participants’ ability to play two well known but not previously practiced songs. A High-Hat, Ride and Snare (HRS) measurement determined drumming precision. Both groups were scanned after the 8-week interval (T2).

Structural Analysis. Structural T1-weighted volumetric images were acquired with full-head coverage, 196 contiguous slices (1.2mm slice thickness), a 256 x 256 matrix, and a repetition time/echo time (TR/TE) of 7.3/3ms (FA =11°, FOV = 270mm²). Regional volume of the cerebellum was calculated using the SUIT toolbox⁶ of the SPM12 software. For shape analysis of the cerebellum, the FSL FIRST software version 5.0.86 was used.⁷ The FreeSurfer analysis suite version 5.3.0 was used to derive models of the cortical surface in each T1-weighted image. The results were inspected and checked for quality following the ENIGMA protocol (www.enigma.ini.usc.edu).

DTI Analysis. Diffusion Tensor Imaging data was acquired using a spin-echo echo-planar imaging (SE-EPI) sequence providing full-head coverage with the following parameters: TE=78.5ms, TR=12 RR interval, FOV=256x256, matrix=128x128, 72 slices with a thickness of 2mm making an isotropic voxel of 2.0x2.0x2.0mm. Diffusion weighting was applied along 60 uniformly distributed directions, 6 b0s and with a b-value of 1500s/mm² and preprocessing was done using ExploreDTI.⁸ Fractional Anisotropy (FA) and Mean Diffusivity (MD) maps were generated and TrackVis was used for manual dissection and quantification fibre tracts of the cerebellar tracts, using a manual two regions-of-interest selection method.⁹ Inclusion regions-of-interest were: medulla oblongata until the level of the dentate (inferior cerebellar peduncle); pontine nucleus until entrance of the cerebellar hemispheres (middle cerebellar peduncle); dentate nucleus until the entrance of the tegmentum in the mesencephalon (superior cerebellar peduncle).

Statistical Analysis. Repeated measures ANOVAs determined changes over time, while univariate ANCOVAs identified specific regions that significantly differed after drum training. Gender was included as covariate and results were Bonferroni corrected for multiple comparisons.

Results and Discussion

Drum training had a significant time x behaviour x group (F(3,24) = 3.415, p = .034) effect. To play on the beat, drumming requires translating visuospatial and temporal information into motor coordination, motor execution and planning actions. Left VIIIa volume increased (F(2,24) = 5.980, p = .012) and was positively correlated with drum precision (τ = .390, p = .042), indicating controlled action execution¹¹ remains a central active feature of drumming, which has been strengthened. Vermis Crus I (F(2,24) = 4.103, p = .039) and VIIIb volume (left: F(2,24) = 6.368, p = .009; right: F(2,24) = 5.199, p = .012) decreased, suggesting a process of automaticity and efficiency.¹² In a previous study using the same population, Crus I/II showed a decreased functional connectivity (FC) while cortical regions showed an increased FC,¹³ suggesting a plastic change affecting both cerebellar and cortical regions. FA increases (left ICP: F(2,24) = 5.583, p = .018; right ICP: F(2,24) = 4.466, p = .048) while MD decreases (right ICP: F(2,24) = 4.539, p = .045) in the inferior cerebellar peduncle, reflecting such plastic changes. Parietal and prefrontal cortical thickness regions responsible for planning actions¹⁴ (right precuneus: F(2,24) = 4.264, p = .050), motor sequencing¹⁵ (left paracentral gyrus: F(2,24) = 5.103, p = .026) and executive processing¹⁶ (right superior frontal gyrus: F(2,24) = 3.385, p = .049) increase after drum training.

Conclusion

Drum training reorganises and reshapes the posterior cerebellum through plasticity, expanding to connected parietal and prefrontal cortical structures through the inferior cerebellar white matter pathway. Thus, it may offer a novel method for cerebellar and cortical plasticity, relevant as an intervention method for psychiatric disorders connected to cerebellar dysfunction, including autism spectrum disorder.^17,18

Acknowledgements

The authors would like to thank The Waterloo Foundation, NIHR Biomedical Research Centre for Mental Health at the South London and the Maudsley NHS Foundation Trust and Institute of Psychiatry, Kings College London. M. B. has received funding from the European Community’s Seventh Framework (FP7/2007-2013) TACTICS. A. A. held a postdoctoral fellowship from the "Fondation Thérèse et René Planiol".

References

1. Bhanpuri, N. H., Okamura, A. M. & Bastian, A. J. Predicting and correcting ataxia using a model of cerebellar function. Brain 137, 1931–1944 (2014).

2. Ito, M. The molecular organization of cerebellar long-term depression. Nat Rev Neurosci 3, 896–902 (2002).

3. D'Angelo, E. The organization of plasticity in the cerebellar cortex: from synapses to control. Prog. Brain Res. 210, 31–58 (2014).

4. Chen, J. L., Penhune, V. B. & Zatorre, R. J. Moving on Time: Brain Network for Auditory-Motor Synchronization is Modulated by Rhythm Complexity and Musical Training. http://dx.doi.org/10.1162/jocn.2008.20018 20, 226–239 (2008).

5. Lotze, M., Scheler, G., Tan, H.-R. M., Braun, C. & Birbaumer, N. The musician's brain: functional imaging of amateurs and professionals during performance and imagery. NeuroImage 20, 1817–1829 (2003).

6. Diedrichsen, J. A spatially unbiased atlas template of the human cerebellum. NeuroImage 33, 127–138 (2006).

7. Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56, 907–922 (2011).

8. Leemans, A., Jeurissen, B. & Sijbers, J. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. 17th Annual Meeting of … (2009).

9. Wang, R., Benner, T., Sorensen, A. G. & Wedeen, V. J. Diffusion toolkit: A software package for diffusion imaging data processing and tractography (Abstract# 3720). (… Resonance Medicine. http://trackvis. …, 2007).

10. Conturo, T. E. et al. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci USA 96, 10422–10427 (1999).

11. Riedel, M. C. et al. Meta-analytic connectivity and behavioral parcellation of the human cerebellum. NeuroImage 117, 327–342 (2015).

12. Haslinger, B. et al. Reduced recruitment of motor association areas during bimanual coordination in concert pianists. Hum. Brain Mapp. 22, 206–215 (2004).

13. Amad, A. et al. Motor learning induces plasticity in the resting brain - drumming up a connection. (2015, under review).

14. Cavanna, A. E. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564–583 (2006).

15. Wiesendanger, R. & Wiesendanger, M. Cerebello-cortical linkage in the monkey as revealed by transcellular labeling with the lectin wheat germ agglutinin conjugated to the marker horseradish peroxidase. Exp Brain Res 59, 105–117 (1985).

16. Boisgueheneuc, du, F. et al. Functions of the left superior frontal gyrus in humans: a lesion study. Brain 129, 3315–3328 (2006).

17. Schmahmann, J. D. in Handbook of the Cerebellum and Cerebellar Disorders 1717–1751 (Springer Netherlands, 2013). doi:10.1007/978-94-007-1333-8_77

18. Stoodley, C. J. Distinct regions of the cerebellum show gray matter decreases in autism, ADHD, and developmental dyslexia. Front Syst Neurosci 8, 92 (2014).

Figures

Changes in cerebellar lobule volume and cortical thickness (T2-T1). Decreasing lobules in red (vermis Crus I, VIIIb) and increasing lobules (left VIIIa) and areas of cortical thickness (left paracentral gyrus, right precuneus and superior frontal gyrus) in green (MNI: x = -18, y = -1, z = 19).

Changes in volume and shape of the left cerebellar hemisphere. Left: decreasing lobules in red (vermis Crus I, VIIIb) and increasing lobules in green (left VIIIa). Middle and right: changes in shape with colours indicating F values and vector arrows showing direction of group differences.

Right inferior cerebellar peduncle (ICP; left) and the cut ICP tract (middle and right) to extract clean diffusion measurements avoiding crossing and complex white matter regions.

Included regions of interest defining the inferior cerebellar peduncle are shown in purple and green (MNI coordinates for regions of interest: green: x = 38, y = 70, z = 24; purple: x = 42, y = 66, z = 15).

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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