, Volume 57, Issue 3, pp 327–334 | Cite as

Diffusion tensor imaging parameters’ changes of cerebellar hemispheres in Parkinson’s disease

  • Enricomaria Mormina
  • Alessandro ArrigoEmail author
  • Alessandro Calamuneri
  • Francesca Granata
  • Angelo Quartarone
  • Maria F. Ghilardi
  • Matilde Inglese
  • Alessandro Di Rocco
  • Demetrio Milardi
  • Giuseppe P. Anastasi
  • Michele Gaeta
Functional Neuroradiology



Studies with diffusion tensor imaging (DTI) analysis have produced conflicting information about the involvement of the cerebellar hemispheres in Parkinson’s disease (PD). We, thus, used a new approach for the analysis of DTI parameters in order to ascertain the involvement of the cerebellum in PD.


We performed a fiber tract-based analysis of cerebellar peduncles and cerebellar hemispheres in 16 healthy subjects and in 16 PD patients with more than 5 years duration of disease, using a 3T MRI scanner and a constrained spherical deconvolution (CSD) approach for tractographic reconstructions. In addition, we performed statistical analysis of DTI parameters and fractional anisotropy (FA) XYZ direction samplings.


We found a statistically significant decrement of FA values in PD patients compared to controls (p < 0.05). In addition, extrapolating and analyzing FA XYZ direction samplings for each patient and each control, we found that this result was due to a stronger decrement of FA values along the Y axis (antero-posterior direction) (p < 0.01); FA changes along X and Z axes were not statistically significant (p > 0.05). We confirmed also no statistically significant differences of FA and apparent diffusion coefficient (ADC) for cerebellar peduncles in PD patients compared to healthy controls.


The DTI-based cerebellar abnormalities in PD could constitute an advance in the knowledge of this disease. We demonstrated a statistically significant reduction of FA in cerebellar hemispheres of PD patients compared to healthy controls. Our work also demonstrated that the use of more sophisticated approaches in the DTI parameter analysis could potentially have a clinical relevance.


MRI Probabilistic tractography Diffusion tensor imaging Parkinson’s disease FA 



We would like to thank Mount Sinai Hospital, New York, for the helpful collaboration.

Ethical standards and patient consent

We declare that all human studies have been approved by our Ethics Committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

Conflict of interest

All authors declare that they have no conflict of interest.


  1. 1.
    Lew M (2007) Overview of Parkinson’s disease. Pharmacotherapy 27(12 Pt 2):155S–160SCrossRefPubMedGoogle Scholar
  2. 2.
    Damier P, Hirsch EC, Agid Y et al (1999) The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson’s disease. Brain 122(Pt 8):1437–1448CrossRefPubMedGoogle Scholar
  3. 3.
    Blandini F, Nappi G, Tassorelli C et al (2000) Functional changes of the basal ganglia circuitry in Parkinson’s disease. Prog Neurobiol 62(1):63–88CrossRefPubMedGoogle Scholar
  4. 4.
    Wu T, Hallett M (2013) The cerebellum in Parkinson’s disease. Brain 136(Pt 3):696–709CrossRefPubMedGoogle Scholar
  5. 5.
    Yu H, Sternad D, Corcos DM et al (2007) Role of hyperactive cerebellum and motor cortex in Parkinson’s disease. Neuroimage 35(1):222–233CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Jankovic J, Kapadia AS (2001) Functional decline in Parkinson disease. Arch Neurol 58(10):1611–1615CrossRefPubMedGoogle Scholar
  7. 7.
    Nicoletti G, Lodi R, Condino F et al (2006) Apparent diffusion coefficient measurements of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson’s disease and progressive supranuclear palsy. Brain 129(Pt 10):2679–2687CrossRefPubMedGoogle Scholar
  8. 8.
    Kim HJ, Kim SJ, Kim HS et al (2013) Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson’s disease. Neurosci Lett 550:64–68CrossRefPubMedGoogle Scholar
  9. 9.
    Gattellaro G, Minati L, Grisoli M et al (2009) White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. AJNR Am J Neuroradiol 30(6):1222–1226CrossRefPubMedGoogle Scholar
  10. 10.
    Schwarz ST, Abaei M, Gontu V et al (2013) Diffusion tensor imaging of nigral degeneration in Parkinson’s disease: a region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin 3:481–488CrossRefPubMedCentralPubMedGoogle Scholar
  11. 11.
    Nicoletti G, Rizzo G, Barbagallo G et al (2013) Diffusivity of cerebellar hemispheres enables discrimination of cerebellar or parkinsonian multiple system atrophy from progressive supranuclear palsy-Richardson syndrome and Parkinson disease. Radiology 267(3):843–850CrossRefPubMedGoogle Scholar
  12. 12.
    Embleton KV, Haroon HA, Morris DM et al (2010) Distortion correction for diffusion-weighted MRI tractography and fMRI in the temporal lobes. Hum Brain Mapp 31:1570–1587CrossRefPubMedGoogle Scholar
  13. 13.
    Jones DK, Horsfield MA, Simmons A (1999) Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 42:515–525CrossRefPubMedGoogle Scholar
  14. 14.
    Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4):1459–1472CrossRefPubMedGoogle Scholar
  15. 15.
    Jones DK, Cercignani M (2010) Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 23:803–820CrossRefPubMedGoogle Scholar
  16. 16.
    Tournier JD, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22(1):53–66CrossRefGoogle Scholar
  17. 17.
    Pajevic S, Pierpaoli C (1999) Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med 42:526–540CrossRefPubMedGoogle Scholar
  18. 18.
    Descoteaux M, Deriche R, Knösche TR et al (2009) Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans Med Imaging 28:269–286CrossRefPubMedGoogle Scholar
  19. 19.
    Tournier JD, Calamante F, Connelly A (2011) Effect of step size on probabilistic streamlines: implications for the interpretation of connectivity analysis. Proc Intl Soc Mag Reson Med 19:2019Google Scholar
  20. 20.
    Alexander DC, Barker GJ (2005) Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. Neuroimage 27:357–367CrossRefPubMedGoogle Scholar
  21. 21.
    Leemans A, Jeurissen B, Sijbers J et al (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med 245:3537Google Scholar
  22. 22.
    Rosenblatt M (1956) Remarks on some nonparametric estimates of a density function. Ann Math Stat 27(3):832–837CrossRefGoogle Scholar
  23. 23.
    Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33(3):1065–1076CrossRefGoogle Scholar
  24. 24.
    Parker GJ, Luzzi S, Alexander DC et al (2005) Lateralization of ventral and dorsal auditory-language pathways in the human brain. Neuroimage 24:656–666CrossRefPubMedGoogle Scholar
  25. 25.
    Lebel C, Beaulieu C (2009) Lateralization of the arcuate fasciculus from childhood to adulthood and its relation to cognitive abilities in children. Hum Brain Mapp 30:3563–3573CrossRefPubMedGoogle Scholar
  26. 26.
    Ota M, Nakata Y, Ito K et al (2013) Differential diagnosis tool for parkinsonian syndrome using multiple structural brain measures. Comput Math Methods Med 2013:571289CrossRefPubMedCentralPubMedGoogle Scholar
  27. 27.
    Wang PS, Wu HM, Lin CP et al (2011) Use of diffusion tensor imaging to identify similarities and differences between cerebellar and Parkinsonism forms of multiple system atrophy. Neuroradiology 53(7):471–481CrossRefPubMedGoogle Scholar
  28. 28.
    Zhang K, Yu C, Zhang Y et al (2011) Voxel-based analysis of diffusion tensor indices in the brain in patients with Parkinson’s disease. Eur J Radiol 77(2):269–273CrossRefPubMedGoogle Scholar
  29. 29.
    Cochrane CJ, Ebmeier KP (2013) Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 80(9):857–864CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Alvarez-Linera J (2008) 3 T MRI: advances in brain imaging. Eur J Radiol 67(3):415–426CrossRefPubMedGoogle Scholar
  31. 31.
    Frayne R, Goodyear BG, Dickhoff P et al (2003) Magnetic resonance imaging at 3.0 Tesla: challenges and advantages in clinical neurological imaging. Invest Radiol 38(7):385–402PubMedGoogle Scholar
  32. 32.
    Vollmar C, O’Muircheartaigh J, Barker GJ et al (2010) Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners. Neuroimage 51(4):1384–1394CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    Chung AW, Thomas DL, Ordidge RJ et al (2013) Diffusion tensor parameters and principal eigenvector coherence: relation to b-value intervals and field strength. Magn Reson Imaging 31(5):742–747CrossRefPubMedGoogle Scholar
  34. 34.
    Wedeen VJ, Wang RP, Schmahmann JD et al (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 41(4):1267–1277CrossRefPubMedGoogle Scholar
  35. 35.
    Jbabdi S, Johansen-Berg H (2013) Tractography: where do we go from here? Brain Connect 1(3):169–183CrossRefGoogle Scholar
  36. 36.
    Tournier JD, Yeh CH, Calamante F et al (2008) Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage 42(2):617–625CrossRefPubMedGoogle Scholar
  37. 37.
    Okada T, Miki Y, Fushimi Y et al (2006) Diffusion-tensor fiber tractography: intraindividual comparison of 3.0-T and 1.5-T MR imaging. Radiology 238(2):668–678CrossRefPubMedGoogle Scholar
  38. 38.
    Tournier JD, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed 26(12):1775–1786CrossRefPubMedGoogle Scholar
  39. 39.
    Gallagher DA, Schapira AH (2009) Etiopathogenesis and treatment of Parkinson’s disease. Curr Top Med Chem 9(10):860–868PubMedGoogle Scholar
  40. 40.
    Piao YS, Mori F, Hayashi S et al (2003) Alpha-synuclein pathology affecting Bergmann glia of the cerebellum in patients with alpha-synucleinopathies. Acta Neuropathol 105(4):403–409PubMedGoogle Scholar
  41. 41.
    Wakabayashi K, Hayashi S, Yoshimoto M et al (2000) NACP/alpha-synuclein-positive filamentous inclusions in astrocytes and oligodendrocytes of Parkinson’s disease brains. Acta Neuropathol 99(1):14–20CrossRefPubMedGoogle Scholar
  42. 42.
    Mori F, Piao YS, Hayashi S et al (2003) Alpha-synuclein accumulates in Purkinje cells in Lewy body disease but not in multiple system atrophy. J Neuropathol Exp Neurol 62(8):812–819PubMedGoogle Scholar
  43. 43.
    Beaulieu C (2014) The biological basis of diffusion anisotropy. In: Johansen-Berg H, Behrens TEJ (eds) Diffusion MRI. From quantitative measurements to in-vivo neuroanatomy. Elsevier, Amsterdam, pp 155–178Google Scholar
  44. 44.
    Beaulieu C, Does MD, Snyder RE et al (1996) Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med 36:627–631CrossRefPubMedGoogle Scholar
  45. 45.
    Bartels AL, Leenders KL (2007) Neuroinflammation in the pathophysiology of Parkinson’s disease: evidence from animal models to human in vivo studies with [11C]-PK11195 PET. Mov Disord 22(13):1852–1856CrossRefPubMedGoogle Scholar
  46. 46.
    Bartels AL, Willemsen AT, Doorduin J et al (2010) [11C]-PK11195 PET: quantification of neuroinflammation and a monitor of anti-inflammatory treatment in Parkinson’s disease? Parkinsonism Relat Disord 16(1):57–59CrossRefPubMedGoogle Scholar
  47. 47.
    Watson MB, Richter F, Lee SK et al (2012) Regionally-specific microglial activation in young mice over-expressing human wildtype alpha-synuclein. Exp Neurol 237(2):318–334CrossRefPubMedCentralPubMedGoogle Scholar
  48. 48.
    Jellinger KA (2001) Cell death mechanisms in neurodegeneration. J Cell Mol Med 5(1):1–17CrossRefPubMedGoogle Scholar
  49. 49.
    Tievsky AL, Ptak T, Farkas J (1999) Investigation of apparent diffusion coefficient and diffusion tensor anisotrophy in acute and chronic multiple sclerosis lesions. AJNR Am J Neuroradiol 20(8):1491–1499PubMedGoogle Scholar
  50. 50.
    Nicoletti G, Tonon C, Lodi R et al (2008) Apparent diffusion coefficient of the superior cerebellar peduncle differentiates progressive supranuclear palsy from Parkinson’s disease. Mov Disord 23(16):2370–2376CrossRefPubMedGoogle Scholar
  51. 51.
    Blain CR, Barker GJ, Jarosz JM et al (2006) Measuring brain stem and cerebellar damage in parkinsonian syndromes using diffusion tensor MRI. Neurology 67(12):2199–2205CrossRefPubMedGoogle Scholar
  52. 52.
    Rizzo G, Martinelli P, Manners D et al (2008) Diffusion-weighted brain imaging study of patients with clinical diagnosis of corticobasal degeneration, progressive supranuclear palsy and Parkinson’s disease. Brain 131(Pt 10):2690–2700CrossRefPubMedGoogle Scholar
  53. 53.
    Parker GD, Marshall D, Rosin PL et al (2013) A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data. Neuroimage 65:433–448CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Enricomaria Mormina
    • 1
  • Alessandro Arrigo
    • 1
    Email author
  • Alessandro Calamuneri
    • 2
  • Francesca Granata
    • 1
  • Angelo Quartarone
    • 2
  • Maria F. Ghilardi
    • 3
  • Matilde Inglese
    • 3
  • Alessandro Di Rocco
    • 3
  • Demetrio Milardi
    • 1
    • 4
  • Giuseppe P. Anastasi
    • 1
  • Michele Gaeta
    • 1
  1. 1.Department of Biomedical Science and Morphological and Functional ImagesUniversity of MessinaMessinaItaly
  2. 2.Department of NeurosciencesUniversity of MessinaMessinaItaly
  3. 3.Mount Sinai HospitalNew YorkUSA
  4. 4.IRCCS Centro Neurolesi Bonino PulejoMessinaItaly

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