Skip to main content
Log in

Plasticity of the Superior and Middle Cerebellar Peduncles in Musicians Revealed by Quantitative Analysis of Volume and Number of Streamlines Based on Diffusion Tensor Tractography

  • Published:
The Cerebellum Aims and scope Submit manuscript

Abstract

This work was conducted to study the plasticity of superior (SCP) and middle (MCP) cerebellar peduncles in musicians. The cerebellum is well known to support several musically relevant motor, sensory and cognitive functions. Previous studies reported increased cerebellar volume and grey matter (GM) density in musicians. Here, we report on plasticity of white matter (WM) of the cerebellum. Our cohort included 10/10 gender and handedness-matched musicians and controls. Using diffusion tensor imaging, fibre tractography of SCP and MCP was performed. The fractional anisotropy (FA), number of streamlines and volume of streamlines of SCP/MCP were compared between groups. Automatic measurements of GM and WM volumes of the right/left cerebellar hemispheres were also compared. Musicians have significantly increased right SCP volume (p = 0.02) and number of streamlines (p = 0.001), right MCP volume (p = 0.004) and total WM volume of the right cerebellum (p = 0.003). There were no significant differences in right MCP number of streamlines, left SCP/MCP volume and number of streamlines, SCP/MCP FA values, GM volume of the right cerebellum and GM/WM volumes of the left cerebellum. We propose that increased volume and number of streamlines of the right cerebellar peduncles represent use-dependent structural adaptation to increased sensorimotor and cognitive functional demands on the musician’s cerebellum.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Munte TF, Altenmuller E, Jancke L. The musician’s brain as a model of neuroplasticity. Nat Rev Neurosci. 2002;3(6):473–8.

    PubMed  Google Scholar 

  2. Watanabe D, Savion-Lemieux T, Penhune VB. The effect of early musical training on adult motor performance: evidence for a sensitive period in motor learning. Exp Brain Res. 2007;176(2):332–40.

    Article  PubMed  Google Scholar 

  3. Abdul-Kareem IA et al. Increased gray matter volume of left pars opercularis in male orchestral musicians correlate positively with years of musical performance. J Magn Reson Imaging. 2011;33(1):24–32.

    Article  PubMed  Google Scholar 

  4. Sluming V et al. Voxel-based morphometry reveals increased gray matter density in Broca’s area in male symphony orchestra musicians. Neuroimage. 2002;17(3):1613–22.

    Article  PubMed  Google Scholar 

  5. Schneider P et al. Structural, functional, and perceptual differences in Heschl’s gyrus and musical instrument preference. Ann NY Acad Sci. 2005;1060:387–94.

    Article  PubMed  Google Scholar 

  6. Andersen BB, Korbo L, Pakkenberg B. A quantitative study of the human cerebellum with unbiased stereological techniques. J Comp Neurol. 1992;326(4):549–60.

    Article  PubMed  CAS  Google Scholar 

  7. Williams PL et al., editors. Gray’s anatomy. 38th ed. London: Churchill Livingstone; 1995.

    Google Scholar 

  8. Snell RS. Clinical neuroanatomy for medical students. 5th ed. Philadelphia: Lippincott Williams & Wilkins; 2001.

    Google Scholar 

  9. Glickstein M, May 3rd JG, Mercier BE. Corticopontine projection in the macaque: the distribution of labelled cortical cells after large injections of horseradish peroxidase in the pontine nuclei. J Comp Neurol. 1985;235(3):343–59.

    Article  PubMed  CAS  Google Scholar 

  10. Glickstein M, Doron K. Cerebellum: connections and functions. Cerebellum. 2008;7(4):589–94.

    Article  PubMed  Google Scholar 

  11. Middleton FA, Strick PL. Cerebellar projections to the prefrontal cortex of the primate. J Neurosci. 2001;21(2):700–12.

    PubMed  CAS  Google Scholar 

  12. Gao JH et al. Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Science. 1996;272(5261):545–7.

    Article  PubMed  CAS  Google Scholar 

  13. Kim SG, Ugurbil K, Strick PL. Activation of a cerebellar output nucleus during cognitive processing. Science. 1994;265(5174):949–51.

    Article  PubMed  CAS  Google Scholar 

  14. Doyon J et al. Experience-dependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci USA. 2002;99(2):1017–22.

    Article  PubMed  CAS  Google Scholar 

  15. Parsons LM et al. Lateral cerebellar hemispheres actively support sensory acquisition and discrimination rather than motor control. Learn Mem. 1997;4(1):49–62.

    Article  PubMed  CAS  Google Scholar 

  16. Matsumura M et al. Role of the cerebellum in implicit motor skill learning: a PET study. Brain Res Bull. 2004;63(6):471–83.

    Article  PubMed  Google Scholar 

  17. Stoodley CJ, Valera EM, Schmahmann JD. An fMRI study of intra-individual functional topography in the human cerebellum. Behav Neurol. 2010;23(1–2):65–79.

    PubMed  Google Scholar 

  18. Paradiso S et al. Cerebellar size and cognition: correlations with IQ, verbal memory and motor dexterity. Neuropsychiatry Neuropsychol Behav Neurol. 1997;10(1):1–8.

    PubMed  CAS  Google Scholar 

  19. Hutchinson S et al. Cerebellar volume of musicians. Cereb Cortex. 2003;13(9):943–9.

    Article  PubMed  Google Scholar 

  20. Schlaug G, Lee LHL, Thangaraj V. Macrostructural adaptation of the cerebellum in musicians. Soc Neurosci. 1998;24:842–7.

    Google Scholar 

  21. Gaser C, Schlaug G. Brain structures differ between musicians and non-musicians. J Neurosci. 2003;23(27):9240–5.

    PubMed  CAS  Google Scholar 

  22. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage. 2000;11(6 Pt 1):805–21.

    Article  PubMed  CAS  Google Scholar 

  23. Della Nave R et al. Brain white matter tracts degeneration in Friedreich ataxia. An in vivo MRI study using tract-based spatial statistics and voxel-based morphometry. Neuroimage. 2008;40(1):19–25.

    Article  PubMed  Google Scholar 

  24. Han Y et al. Gray matter density and white matter integrity in pianists’ brain: a combined structural and diffusion tensor MRI study. Neurosci Lett. 2009;459(1):3–6.

    Article  PubMed  CAS  Google Scholar 

  25. Roberts TP et al. Fiber density index correlates with reduced fractional anisotropy in white matter of patients with glioblastoma. AJNR Am J Neuroradiol. 2005;26(9):2183–6.

    PubMed  Google Scholar 

  26. Jones DK et al. Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia. Hum Brain Mapp. 2006;27(3):230–8.

    Article  PubMed  Google Scholar 

  27. Yu C et al. Plasticity of the corticospinal tract in early blindness revealed by quantitative analysis of fractional anisotropy based on diffusion tensor tractography. Neuroimage. 2007;36(2):411–7.

    Article  PubMed  Google Scholar 

  28. Rose SE et al. Loss of connectivity in Alzheimer’s disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 2000;69(4):528–30.

    Article  PubMed  CAS  Google Scholar 

  29. Schmithorst VJ, Wilke M. Differences in white matter architecture between musicians and non-musicians: a diffusion tensor imaging study. Neurosci Lett. 2002;321(1–2):57–60.

    Article  PubMed  CAS  Google Scholar 

  30. Imfeld A et al. White matter plasticity in the corticospinal tract of musicians: a diffusion tensor imaging study. Neuroimage. 2009;46(3):600–7.

    Article  PubMed  Google Scholar 

  31. Bengtsson SL et al. Extensive piano practicing has regionally specific effects on white matter development. Nat Neurosci. 2005;8(9):1148–50.

    Article  PubMed  CAS  Google Scholar 

  32. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97–113.

    Article  PubMed  CAS  Google Scholar 

  33. Basser PJ et al. In vivo fiber tractography using DT-MRI data. Magn Reson Med. 2000;44(4):625–32.

    Article  PubMed  CAS  Google Scholar 

  34. Schaechter JD, Perdue KL, Wang R. Structural damage to the corticospinal tract correlates with bilateral sensorimotor cortex reorganization in stroke patients. Neuroimage. 2008;39(3):1370–82.

    Article  PubMed  CAS  Google Scholar 

  35. Matsumoto R et al. Hemispheric asymmetry of the arcuate fasciculus: a preliminary diffusion tensor tractography study in patients with unilateral language dominance defined by Wada test. J Neurol. 2008;255(11):1703–11.

    Article  PubMed  CAS  Google Scholar 

  36. Reich DS et al. Quantitative characterization of the corticospinal tract at 3T. AJNR Am J Neuroradiol. 2006;27(10):2168–78.

    PubMed  CAS  Google Scholar 

  37. Salamon N et al. White matter fiber tractography and color mapping of the normal human cerebellum with diffusion tensor imaging. J Neuroradiol. 2007;34(2):115–28.

    Article  PubMed  CAS  Google Scholar 

  38. Wakana S et al. Fiber tract-based atlas of human white matter anatomy. Radiology. 2004;230(1):77–87.

    Article  PubMed  Google Scholar 

  39. Jellison BJ et al. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol. 2004;25(3):356–69.

    PubMed  Google Scholar 

  40. Thomas B et al. Quantitative diffusion tensor imaging in cerebral palsy due to periventricular white matter injury. Brain. 2005;128(Pt 11):2562–77.

    Google Scholar 

  41. Kim J et al. Decreased fractional anisotropy of middle cerebellar peduncle in crossed cerebellar diaschisis: diffusion-tensor imaging-positron-emission tomography correlation study. AJNR Am J Neuroradiol. 2005;26(9):2224–8.

    PubMed  Google Scholar 

  42. Good CD et al. Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage. 2001;14(3):685–700.

    Article  PubMed  CAS  Google Scholar 

  43. Lledo PM, Alonso M, Grubb MS. Adult neurogenesis and functional plasticity in neuronal circuits. Nat Rev Neurosci. 2006;7(3):179–93.

    Article  PubMed  CAS  Google Scholar 

  44. Woolf CJ, Salter MW. Neuronal plasticity: increasing the gain in pain. Science. 2000;288(5472):1765–9.

    Article  PubMed  CAS  Google Scholar 

  45. Pascual-Leone A et al. The plastic human brain cortex. Annu Rev Neurosci. 2005;28:377–401.

    Article  PubMed  CAS  Google Scholar 

  46. van Praag H et al. Running enhances neurogenesis, learning, and long-term potentiation in mice. Proc Natl Acad Sci USA. 1999;96(23):13427–31.

    Article  PubMed  Google Scholar 

  47. Anderson BJ, Alcantara AA, Greenough WT. Motor-skill learning: changes in synaptic organization of the rat cerebellar cortex. Neurobiol Learn Mem. 1996;66(2):221–9.

    Article  PubMed  CAS  Google Scholar 

  48. Kim HT et al. Specific plasticity of parallel fiber/Purkinje cell spine synapses by motor skill learning. Neuroreport. 2002;13(13):1607–10.

    Article  PubMed  Google Scholar 

  49. Kleim JA et al. Selective synaptic plasticity within the cerebellar cortex following complex motor skill learning. Neurobiol Learn Mem. 1998;69(3):274–89.

    Article  PubMed  CAS  Google Scholar 

  50. Pysh JJ, Weiss GM. Exercise during development induces an increase in Purkinje cell dendritic tree size. Science. 1979;206(4415):230–2.

    Article  PubMed  CAS  Google Scholar 

  51. Black JE et al. Learning causes synaptogenesis, whereas motor activity causes angiogenesis, in cerebellar cortex of adult rats. Proc Natl Acad Sci USA. 1990;87(14):5568–72.

    Article  PubMed  CAS  Google Scholar 

  52. Park IS et al. Evaluation of morphological plasticity in the cerebella of basketball players with MRI. J Korean Med Sci. 2006;21(2):342–6.

    Article  PubMed  Google Scholar 

  53. Frings M et al. Acquisition of simple auditory and visual sequences in cerebellar patients. Cerebellum. 2006;5(3):206–11.

    Article  PubMed  Google Scholar 

  54. Sergent J et al. Distributed neural network underlying musical sight-reading and keyboard performance. Science. 1992;257(5066):106–9.

    Article  PubMed  CAS  Google Scholar 

  55. Hund-Georgiadis M, von Cramon DY. Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals. Exp Brain Res. 1999;125(4):417–25.

    Article  PubMed  CAS  Google Scholar 

  56. Flament D et al. Functional magnetic resonance imaging of cerebellar activation during the learning of a visuomotor dissociation task. Hum Brain Mapp. 1996;4(3):210–26.

    Article  PubMed  CAS  Google Scholar 

  57. Doyon J, Penhune V, Ungerleider LG. Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia. 2003;41(3):252–62.

    Article  PubMed  Google Scholar 

  58. Koeneke S et al. Long-term training affects cerebellar processing in skilled keyboard players. Neuroreport. 2004;15(8):1279–82.

    Article  PubMed  Google Scholar 

  59. Imamizu H et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature. 2000;403(6766):192–5.

    Article  PubMed  CAS  Google Scholar 

  60. Parsons LM et al. Pitch discrimination in cerebellar patients: evidence for a sensory deficit. Brain Res. 2009;1303:84–96.

    Article  PubMed  CAS  Google Scholar 

  61. Bower JM, Kassel J. Variability in tactile projection patterns to cerebellar folia crus IIA of the Norway rat. J Comp Neurol. 1990;302(4):768–78.

    Article  PubMed  CAS  Google Scholar 

  62. Bower JM, Parsons LM. Rethinking the "lesser brain". Sci Am. 2003;289(2):50–7.

    Article  PubMed  Google Scholar 

  63. Belin P et al. The functional anatomy of sound intensity discrimination. J Neurosci. 1998;18(16):6388–94.

    PubMed  CAS  Google Scholar 

  64. Belin P et al. The neuroanatomical substrate of sound duration discrimination. Neuropsychologia. 2002;40(12):1956–64.

    Article  PubMed  Google Scholar 

  65. Lockwood AH et al. The functional anatomy of the normal human auditory system: responses to 0.5 and 4.0 kHz tones at varied intensities. Cereb Cortex. 1999;9(1):65–76.

    Article  PubMed  CAS  Google Scholar 

  66. Callan DE et al. Song and speech: brain regions involved with perception and covert production. Neuroimage. 2006;31(3):1327–42.

    Article  PubMed  Google Scholar 

  67. Zatorre RJ, Belin P. Spectral and temporal processing in human auditory cortex. Cereb Cortex. 2001;11(10):946–53.

    Article  PubMed  CAS  Google Scholar 

  68. Johnsrude IS, Penhune VB, Zatorre RJ. Functional specificity in the right human auditory cortex for perceiving pitch direction. Brain. 2000;123(Pt 1):155–63.

    Article  PubMed  Google Scholar 

  69. Paviour DC et al. Regional brain volumes distinguish PSP, MSA-P, and PD: MRI-based clinico-radiological correlations. Mov Disord. 2006;21(7):989–96.

    Article  PubMed  Google Scholar 

  70. Gama RL et al. Morphometry MRI in the differential diagnosis of parkinsonian syndromes. Arq Neuropsiquiatr. 2010;68(3):333–8.

    Article  PubMed  Google Scholar 

  71. Fischl B et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–55.

    Article  PubMed  CAS  Google Scholar 

  72. Walhovd KB et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging. 2005;26(9):1261–70. discussion 1275–8.

    Article  PubMed  Google Scholar 

  73. Pengas G et al. Comparative reliability of total intracranial volume estimation methods and the influence of atrophy in a longitudinal semantic dementia cohort. J Neuroimaging. 2009;19(1):37–46.

    Article  PubMed  Google Scholar 

  74. Tae WS et al. Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder. Neuroradiology. 2008;50(7):569–81.

    Article  PubMed  Google Scholar 

Download references

Conflict of Interest

The authors declare that there are no conflicts of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ihssan A. Abdul-Kareem.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Abdul-Kareem, I.A., Stancak, A., Parkes, L.M. et al. Plasticity of the Superior and Middle Cerebellar Peduncles in Musicians Revealed by Quantitative Analysis of Volume and Number of Streamlines Based on Diffusion Tensor Tractography. Cerebellum 10, 611–623 (2011). https://doi.org/10.1007/s12311-011-0274-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12311-011-0274-1

Keywords

Navigation