Skip to main content

Advertisement

Log in

Cerebellar Atrophy in Patients with Subcortical-Type Vascular Cognitive Impairment

  • Original Paper
  • Published:
The Cerebellum Aims and scope Submit manuscript

Abstract

Recent studies suggest that the role of the cerebellum extends into cognitive regulation and that subcortical vascular dementia (SVaD) can result in cerebellar atrophy. However, there has been no evaluation of the cerebellar volume in the preclinical stage of SVaD. We aimed to compare cerebellar volume among patients with amnestic mild cognitive impairment (aMCI) and subcortical vascular mild cognitive impairment (svMCI) and evaluate which factors could have contributed to the cerebellar volume. Participants were composed of 355 patients with aMCI, svMCI, Alzheimer's disease (AD), and SVaD. Cerebellar volumes were measured using automated methods. A direct comparison of the cerebellar volume in SVaD and AD groups showed that the SVaD group had a statistically smaller cerebellar volume than the AD group. Additionally, the svMCI group had a smaller cerebellar volume than the aMCI group, with the number of lacunes (especially in the supratentorial regions) being associated with cerebellar volume. Cerebellar volumes were associated with some neuropsychological tests, digit span backward and ideomotor apraxia. These findings suggest that cerebellar atrophy may be useful in differentiating subtypes of dementia and the cerebellum plays a potential role in cognition.

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

Similar content being viewed by others

References

  1. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82:239–59.

    Article  PubMed  CAS  Google Scholar 

  2. Roman GC, Erkinjuntti T, Wallin A, Pantoni L, Chui HC. Subcortical ischaemic vascular dementia. Lancet Neurol. 2002;1:426–36.

    Article  PubMed  Google Scholar 

  3. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–6.

    PubMed  CAS  Google Scholar 

  4. Dubois B, Albert ML. Amnestic MCI or prodromal Alzheimer's disease? Lancet Neurol. 2004;3:246–8.

    Article  PubMed  Google Scholar 

  5. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–8.

    Article  PubMed  CAS  Google Scholar 

  6. Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–92.

    Article  PubMed  CAS  Google Scholar 

  7. Frisoni GB, Galluzzi S, Bresciani L, Zanetti O, Geroldi C. Mild cognitive impairment with subcortical vascular features: clinical characteristics and outcome. J Neurol. 2002;249:1423–32.

    Article  PubMed  Google Scholar 

  8. Galluzzi S, Sheu CF, Zanetti O, Frisoni GB. Distinctive clinical features of mild cognitive impairment with subcortical cerebrovascular disease. Dement Geriatr Cogn Disord. 2005;19:196–203.

    Article  PubMed  Google Scholar 

  9. Debette S, Bombois S, Bruandet A, Delbeuck X, Lepoittevin S, Delmaire C, et al. Subcortical hyperintensities are associated with cognitive decline in patients with mild cognitive impairment. Stroke. 2007;38:2924–30.

    Article  PubMed  Google Scholar 

  10. Kim SH, Park JS, Ahn HJ, Seo SW, Lee JM, Kim ST, et al. Voxel-based analysis of diffusion tensor imaging in patients with subcortical vascular cognitive impairment: Correlates with cognitive and motor deficits. J Neuroimaging. 2010;21:317–24.

    Article  PubMed  Google Scholar 

  11. Baillieux H, De Smet HJ, Paquier PF, De Deyn PP, Marien P. Cerebellar neurocognition: insights into the bottom of the brain. Clin Neurol Neurosurg. 2008;110:763–73.

    Article  PubMed  Google Scholar 

  12. Kalashnikova LA, Zueva YV, Pugacheva OV, Korsakova NK. Cognitive impairments in cerebellar infarcts. Neurosci Behav Physiol. 2005;35:773–9.

    Article  PubMed  CAS  Google Scholar 

  13. Fox NC, Crum WR, Scahill RI, Stevens JM, Janssen JC, Rossor MN. Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images. Lancet. 2001;358:201–5.

    Article  PubMed  CAS  Google Scholar 

  14. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease. Neurology. 1984;34:939.

    Article  PubMed  CAS  Google Scholar 

  15. Busser J, Geldmacher DS, Herrup K. Ectopic cell cycle proteins predict the sites of neuronal cell death in Alzheimer's disease brain. J Neurosci. 1998;18:2801.

    PubMed  CAS  Google Scholar 

  16. Pantel J, Schroder J, Essig M, Jauss M, Schneider G, Eysenbach K, et al. In vivo quantification of brain volumes in subcortical vascular dementia and Alzheimer's disease. An MRI-based study. Dement Geriatr Cogn Disord. 1998;9:309–16.

    Article  PubMed  CAS  Google Scholar 

  17. Mielke R, Herholz K, Grond M, Kessler J, Heiss WD. Severity of vascular dementia is related to volume of metabolically impaired tissue. Arch Neurol. 1992;49:909–13.

    Article  PubMed  CAS  Google Scholar 

  18. Seo SW, Lee JM, Im K, Park JS, Kim SH, Kim ST, et al. Cortical thinning related to periventricular and deep white matter hyperintensities. Neurobiol Aging. 2012 (in press)

  19. Du AT, Schuff N, Chao LL, Kornak J, Ezekiel F, Jagust WJ, et al. White matter lesions are associated with cortical atrophy more than entorhinal and hippocampal atrophy. Neurobiol Aging. 2005;26:553–9.

    Article  PubMed  Google Scholar 

  20. Seo SW, Cho SS, Park A, Chin J, Na DL. Subcortical vascular versus amnestic mild cognitive impairment: comparison of cerebral glucose metabolism. J Neuroimaging. 2009;19:213–9.

    Article  PubMed  Google Scholar 

  21. Seo SW, Im K, Lee JM, Kim YH, Kim ST, Kim SY, et al. Cortical thickness in single-versus multiple-domain amnestic mild cognitive impairment. NeuroImage. 2007;36:289–97.

    Article  PubMed  Google Scholar 

  22. Erkinjuntti T, Inzitari D, Pantoni L, Wallin A, Scheltens P, Rockwood K, et al. Research criteria for subcortical vascular dementia in clinical trials. J Neural Transm Suppl. 2000;59:23–30.

    PubMed  CAS  Google Scholar 

  23. Sachdev P, Wen W, Shnier R, Brodaty H. Cerebral blood volume in T2-weighted white matter hyperintensities using exogenous contrast based perfusion MRI. J Neuropsychiatr Clin Neurosci. 2004;16:83.

    Article  Google Scholar 

  24. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143–55.

    Article  PubMed  Google Scholar 

  25. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18:192–205.

    Article  PubMed  CAS  Google Scholar 

  26. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87–97.

    Article  PubMed  CAS  Google Scholar 

  27. Zijdenbos A, Forghani R, Evans A. Automatic quantification of MS lesions in 3D MRI brain data sets: validation of insect. MICCAI. 1998;98:439–48.

    Google Scholar 

  28. Tohka J, Zijdenbos A, Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage. 2004;23:84–97.

    Article  PubMed  Google Scholar 

  29. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage. 2006;31:1116–28.

    Article  PubMed  Google Scholar 

  30. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. New Jersey: Pearson Prentice Hall; 2004.

    Google Scholar 

  31. Kang Y, Na D. Seoul neuropsychological screening battery. Incheon: Human Brain Research & Consulting Co.; 2003.

    Google Scholar 

  32. Schmahmann JD. The role of the cerebellum in cognition and emotion: personal reflections since 1982 on the dysmetria of thought hypothesis, and its historical evolution from theory to therapy. Neuropsychol Rev. 2010;20:236–60.

    Article  PubMed  Google Scholar 

  33. Heyder K, Suchan B, Daum I. Cortico-subcortical contributions to executive control. Acta Psychol. 2004;115:271–89.

    Article  Google Scholar 

  34. Jouvent E, Viswanathan A, Mangin JF, O'Sullivan M, Guichard JP, Gschwendtner A, et al. Brain atrophy is related to lacunar lesions and tissue microstructural changes in CADASIL. Stroke. 2007;38:1786–90.

    Article  PubMed  Google Scholar 

  35. Heyder K, Suchan B, Daum I. Cortico-subcortical contributions to executive control. Acta Psychol (Amst). 2004;115:271–89.

    Article  Google Scholar 

  36. Schmahmann JD, Pandya DN. The cerebrocerebellar system. Int Rev Neurobiol. 1997;41:31–60.

    Article  PubMed  CAS  Google Scholar 

  37. Infeld B, Davis SM, Lichtenstein M, Mitchell PJ, Hopper JL. Crossed cerebellar diaschisis and brain recovery after stroke. Stroke. 1995;26:90–5.

    Article  PubMed  CAS  Google Scholar 

  38. Tien R, Ashdown B. Crossed cerebellar diaschisis and crossed cerebellar atrophy: correlation of MR findings, clinical symptoms, and supratentorial diseases in 26 patients. Am J Roentgenol. 1992;158:1155–9.

    CAS  Google Scholar 

  39. Lin DD, Kleinman JT, Wityk RJ, Gottesman RF, Hillis AE, Lee AW, et al. Crossed cerebellar diaschisis in acute stroke detected by dynamic susceptibility contrast MR perfusion imaging. AJNR Am J Neuroradiol. 2009;30:710–5.

    Article  PubMed  CAS  Google Scholar 

  40. Tatsch K, Koch W, Linke R, Poepperl G, Peters N, Holtmannspoetter M, et al. Cortical hypometabolism and crossed cerebellar diaschisis suggest subcortically induced disconnection in CADASIL: an 18F-FDG PET study. J Nucl Med. 2003;44:862–9.

    PubMed  Google Scholar 

  41. Ravizza SM, McCormick CA, Schlerf JE, Justus T, Ivry RB, Fiez JA. Cerebellar damage produces selective deficits in verbal working memory. Brain. 2006;129:306.

    Article  PubMed  Google Scholar 

  42. Choi S, Na DL, Kang E, Lee K, Lee S, Na D. Functional magnetic resonance imaging during pantomiming tool-use gestures. Exp Brain Res. 2001;139:311–7.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported by a grant from the Korean Healthcare Technology R&D Project; Ministry for Health, Welfare and Family Affairs, Republic of Korea (A102065 & A070001); a Korean Science and Engineering Foundation (KOSEF) NRL program grant funded by the Korean government (MEST; R0A-2007-000-20068-0); and a Samsung Medical Center Clinical Research Development Program grant (CRL-108011 & CRS 110-14-1).

Disclosure

The authors declare no conflicts of interest, and all protocols described in this study were approved by the Institutional Review Board of the Samsung Medical Center.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sang Won Seo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yoon, C.W., Seo, S.W., Park, JS. et al. Cerebellar Atrophy in Patients with Subcortical-Type Vascular Cognitive Impairment. Cerebellum 12, 35–42 (2013). https://doi.org/10.1007/s12311-012-0388-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12311-012-0388-0

Keywords

Navigation