Skip to main content
Log in

Selective Cerebellar Atrophy Associates with Depression and Fatigue in the Early Phases of Relapse-Onset Multiple Sclerosis

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

Abstract

Cerebellar dysfunctions have been associated to depressive disorders and cognitive impairment in neurodegenerative diseases. The objective is to analyze the associations between cerebellar atrophy, depression, and fatigue in the early phases of relapse-onset multiple sclerosis (RRMS). Sixty-one RRMS patients and 50 healthy controls (HC) were enrolled and clinically evaluated by means of expanded disability status scale (EDSS), Rao’s brief repeatable battery of neuropsychological tests (BRB-NT), Delis-Kaplan executive function system sorting test, beck depression inventory II (BDI-II), and fatigue severity scale (FSS). The relationships between MRI variables and clinical scores were assessed. Depressed RRMS (dRRMS) had significantly lower Vermis Crus I volume compared with not depressed RRMS (ndRRMS) (p = 0.009). Vermis Crus I volume was lower in dRRMS suffering from fatigue than in ndRRMS without fatigue (p = 0.01). The hierarchical regression models which included demographic and clinical data (age, sex, and disease duration, FSS or BDI-II) and cerebellar volumes disclosed that cerebellar lobule right V atrophy explained an increase of 4% of the variability in FSS (p = 0.25) and Vermis Crus I atrophy explained an increase of 6% of variability in BDI-II (p = 0.049). Since clinical onset, atrophy of specific cerebellar lobules associates with important clinical aspects of RRMS. Cerebellar pathology may be one of the determinants of fatigue and depression that contribute to worsen disability in RRMS.

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

Similar content being viewed by others

References

  1. Gordon N. The cerebellum and cognition. Eur J Paediatr Neurol. 2007;11:232–4.

    PubMed  Google Scholar 

  2. Koziol LF, Budding D, Andreasen N, D’Arrigo S, Bulgheroni S, Imamizu H, et al. Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum. 2014;13(1):151–77.

    PubMed  PubMed Central  Google Scholar 

  3. Sirio C, Petracca M, Mormina E, Buyukturkoglu K, Podranski K, Heinig MM, et al. Cerebellar lobule atrophy and disability in progressive MS. J Neurol Neurosurg Psychiatry. 2017;88:1065–72.

    Google Scholar 

  4. Hoche F, Guell X, Vangel MG, Sherman JC, Schmahmann JD. The cerebellar cognitive affective/Schmahmann syndrome scale. Brain. 2018;141(1):248–70.

    PubMed  Google Scholar 

  5. Argyropoulos GPD, van Dun K, Adamaszek M, Leggio M, Manto M, et al. The cerebellar cognitive affective/Schmahmann syndrome: a task force paper. Cerebellum. 2019. https://doi.org/10.1007/s12311-019-01068-8.

    Google Scholar 

  6. Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain. 1998;121:561–79.

    PubMed  Google Scholar 

  7. Andersen K, Andersen BB, Pakkenberg B. Stereological quantification of the cerebellum in patients with Alzheimer’s disease. Neurobiol Aging. 2012;33:197. e11–197.e20.

    Google Scholar 

  8. Hoppenbrouwers SS, Schutter DJLG, Fitzgerald PB, Chen R, Daskalakis ZJ. The role of the cerebellum in the pathophysiology and treatment of neuropsychiatric disorders: a review. Brain Res Rev. 2008;59:185–200.

    CAS  PubMed  Google Scholar 

  9. Moroso A, Ruet A, Lamargue-Hamel D, Munsch F, Deloire M, Coupe P, et al. Posterior lobules of the cerebellum and information processing speed at various stages of multiple sclerosis. J Neurol Neurosurg Psychiatry. 2017;88:146–51.

    PubMed  Google Scholar 

  10. Schmahmann JD. The cerebellum and cognition. Neurosci Lett. 2019;688:62–75.

    CAS  PubMed  Google Scholar 

  11. Li W-K, Hausknecht MJ, Stone P, Mauk MD. Using a million cell simulation of the cerebellum: network scaling and task generality. Neural Netw. 2013;47:95–102.

    PubMed  Google Scholar 

  12. Salehpoor G, Rezaei S, Hosseininezhad M. Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: a bicenter study from north of Iran. Iran J Nurs Midwifery Res. 2014;19:593–9.

    PubMed  PubMed Central  Google Scholar 

  13. Amato MP, Ponziani G, Rossi F, Liedl CL, Stefanile C, Rossi L. Quality of life in multiple sclerosis: the impact of depression, fatigue and disability. Mult Scler. 2001;7(5):340–4.

    CAS  PubMed  Google Scholar 

  14. Feinstein A, Magalhaes S, Richard JF, Audet B, Moore C. The link between multiple sclerosis and depression. Nat Rev Neurol. 2014;10:507–17.

    PubMed  Google Scholar 

  15. Janardhan V, Bakshi R. Quality of life in patients with multiple sclerosis: the impact of fatigue and depression. J Neurol Sci. 2002;205(1):51–8.

    PubMed  Google Scholar 

  16. Hu M, Muhlert N, Robertson N, Winter M. Perceived fatigue and cognitive performance change in multiple sclerosis: uncovering predictors beyond baseline fatigue. Mult Scler Relat Disord. 2019;32:46–53.

    CAS  PubMed  Google Scholar 

  17. Frühwald S, Löffler-Stastka H, Eher R, Saletu B, Baumhackl U. Relationship between symptoms of depression and anxiety and the quality of life in multiple sclerosis. Wien Klin Wochenschr. 2001;113(9):333–8.

    PubMed  Google Scholar 

  18. Hildebrant H, Eling P. A longitudinal study on fatigue, depression, and their relation to neurocognition in multiple sclerosis. J Clin Exp Neuropsychol. 2014;36(4):410–7.

    Google Scholar 

  19. Christodoulou C, Melville P, Scherl WF, Macallister WS, Abensur RL, et al. Negative affect predicts subsequent cognitive change in multiple sclerosis. J Int Neuropsychol Soc. 2009;15(1):53–61.

    PubMed  Google Scholar 

  20. Berg D, Supprian T, Thomae J, Warmuth-Metz M, Horowski A, Zeiler B, et al. Lesion pattern in patients with multiple sclerosis and depression. Mult Scler. 2000;6(3):156–62.

    CAS  PubMed  Google Scholar 

  21. Bonavita S, Tedeschi G, Gallo A. Morphostructural MRI abnormalities related to neuropsychiatric disorders associated to multiple sclerosis. Mult Scler Int. 2013;2013:102454.

    PubMed  PubMed Central  Google Scholar 

  22. Feinstein A, O’Connor P, Akbar N, Moradzadeh L, Scott CJ, Lobaugh NJ. Diffusion tensor imaging abnormalities in depressed multiple sclerosis patients. Mult Scler. 2010;16(2):189–96.

    CAS  PubMed  Google Scholar 

  23. Rao SM, Reingold SC, Ron MA, Lyon-Caen O, Comi G. Workshop on neurobehavioral disorders in multiple sclerosis. Diagnosis, underlying disease, natural history, and therapeutic intervention, Bergamo, Italy, June 25-27, 1992. Arch Neurol. 1993;50(6):658–62.

    CAS  PubMed  Google Scholar 

  24. Reischies FM, Baum K, Bräu H, Hedde JP, Schwindt G. Cerebral magnetic resonance imaging findings in multiple sclerosis. Relation to disturbance of affect, drive, and cognition. Arch Neurol. 1988;45(10):1114–6.

    CAS  PubMed  Google Scholar 

  25. Glowinski J, Besson MJ, Chéramy A. Role of the thalamus in the bilateral regulation of dopaminergic and GABAergic neurons in the basal ganglia. CIBA Found Symp. 1984;107:150–63.

    CAS  PubMed  Google Scholar 

  26. Cummings JL. The neuroanatomy of depression. J Clin Psychiatry. 1993;54(Suppl):14–20.

    PubMed  Google Scholar 

  27. Mayberg HS. Limbic-cortical dysregulation: a proposed model of depression. J Neuropsychiatr Clin Neurosci. 1997;9(3):471–81.

    CAS  Google Scholar 

  28. Sabatini U, Pozzilli C, Pantano P, Koudriavtseva T, Padovani A, Millefiorini E, et al. Involvement of the limbic system in multiple sclerosis patients with depressive disorders. Biol Psychiatry. 1996;39(11):970–5.

    CAS  PubMed  Google Scholar 

  29. Nigro S, Passamonti L, Riccelli R, Toschi N, Rocca F, Valentino P, et al. Structural ‘connectomic’ alterations in the limbic system of multiple sclerosis patients with major depression. Mult Scler. 2015 Jul;21(8):1003–12.

    PubMed  Google Scholar 

  30. Rocca MA, Pravatà E, Valsasina P, Radaelli M, Colombo B, Vacchi L, et al. Hippocampal-DMN disconnectivity in MS is related to WM lesions and depression. Hum Brain Mapp. 2015;36(12):5051–63.

    PubMed  PubMed Central  Google Scholar 

  31. Thompson AJ, Banwell BL, Barkhof F, Coetzee T, Comi G, Correale J, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73.

    PubMed  Google Scholar 

  32. Kurtzke JF. Rating neurological impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983; 33: 1444 ± 1452.

    CAS  PubMed  Google Scholar 

  33. Amato MP, Portaccio E, Goretti B, Zipoli V, Ricchiuti L, De Caro MF, et al. The Rao’s Brief Repeatable Battery and Stroop test: normative values with age, education and gender corrections in an Italian population. Mult Scler. 2006;12(6):787–93.

    CAS  PubMed  Google Scholar 

  34. Beck AT, Steer RA, Brown GK. BDI-II: beck depression inventory manual. 2nd ed. San Antonio Tex: Psychological Corporation; 1996.

    Google Scholar 

  35. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46(10):1121–3.

    CAS  PubMed  Google Scholar 

  36. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Sean Ho S, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–28 www.itksnap.org.

    PubMed  Google Scholar 

  37. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97(20):11050–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26–41.

    CAS  PubMed  Google Scholar 

  39. D’Ambrosio A, Pagani E, Riccitelli GC, Colombo B, Rodegher M, et al. Cerebellar contribution to motor and cognitive performance in multiple sclerosis: an MRI sub-regional volumetric analysis. Mult Scler. 2017;23(9):1194–203.

    PubMed  Google Scholar 

  40. Amtmann D, Kim J, Chung H, Bamer AM, Askew RL, Wu S, et al. Comparing CESD-10, PHQ-9, and PROMIS depression instruments in individuals with multiple sclerosis. Rehabil Psychol. 2014;59(2):220–9.

    PubMed  PubMed Central  Google Scholar 

  41. Patten SB, Francis G, Metz LM, Lopez-Bresnahan M, Chang P, Curtin F. The relationship between depression and interferon beta-1a therapy in patients with multiple sclerosis. Mult Scler. 2005;11:175–81.

    CAS  PubMed  Google Scholar 

  42. Sacco R, Santangelo G, Stamenova S, Bisecco A, Bonavita S, Lavorgna L, et al. Psychometric properties and validity of Beck depression inventory II in multiple sclerosis. Eur J Neurol. 2016;23(4):744–50.

    CAS  PubMed  Google Scholar 

  43. Schippling S, O’Connor P, Knappertz V, Pohl C, Bogumil T, Suarez G, et al. Incidence and course of depression in multiple sclerosis in the multinational BEYOND trial. J Neurol. 2016;263:1418–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Solaro C, Trabucco E, Signori A, Martinelli V, Radaelli M, Centonze D, et al. Depressive symptoms correlate with disability and disease course in multiple sclerosis patients: an Italian multi-center study using the beck depression inventory. PLoS One. 2016;11(9):e0160261.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27(9):2349–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009;29(26):8586–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage. 2008;44(2):489–501.

    PubMed  Google Scholar 

  48. Schmahmann JD. From movement to thought: anatomic substrates of the cerebellar contribution to cognitive processing. Hum Brain Mapp. 1996;4:174–98.

    CAS  PubMed  Google Scholar 

  49. Haines DE, Dietrichs E, Mihailoff GA, McDonald EF. Cerebellar-hypothalamic axis: basis circuits and clinical observations. Int Rev Neurobiol. 1997;41:83–107.

    CAS  PubMed  Google Scholar 

  50. Middleton FA, Strick PL. Dendate output channels: motor and cognitive components. Prog Brain Res. 1997;114:553–66.

    CAS  PubMed  Google Scholar 

  51. Dum RP, Strick PL. An unfolded map of the cerebellar dentate nucleus and its projection to the cerebral cortex. J Neurophysiol. 2003;89:634–9.

    PubMed  Google Scholar 

  52. Schmahmann JD. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatr Clin Neurosci. 2004;16:367–78.

    Google Scholar 

  53. Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46(7):831–44.

    PubMed  PubMed Central  Google Scholar 

  54. Grimaldi G, Manto M. Topography of cerebellar deficits in humans. Cerebellum. 2012;11:336–51.

    PubMed  Google Scholar 

  55. Savini G, Pardini M, Catellazzi G, Lascialfari A, Chard D, D’Angelo E, et al. Default mode network structural integrity and cerebellar connectivity predict information processing speed deficit in multiple sclerosis. Front Cell Neurosci. 2019;13:21.

    PubMed  PubMed Central  Google Scholar 

  56. Van Geest Q, Boeschoten RE, Keijzer MJ, Steenwijk MD, Pouwels PJW, Twisk JWR, et al. Fronto-limbic disconnection in patients with multiple sclerosis and depression. Mult Scler. 2019;25(5):715–26.

    PubMed  Google Scholar 

  57. Carballedo A, Amico F, Ugwu I, Fagan AJ, Fahley C, Morris D, et al. Reduced fractional anisotropy in the uncinate fasciculus in patients with major depression carrying the met-allele of the Val66Met brain-derived neurotrophic factor genotype. Am J Med Genet B Neuropsychiatr Genet. 2012;159B(5):537–48.

    CAS  PubMed  Google Scholar 

  58. Cullen KR, Klimes-Dougan B, Muetzel R, et al. Altered white matter microstructure in adolescents with major depression: a preliminary study. J Am Acad Child Adolesc Psychiatry. 2010;49(2):173–183.e1.

    PubMed  PubMed Central  Google Scholar 

  59. Hanken K, Manousi A, Klein J, Kastrup A, Eling P, Hildebrandt H. On the relation between self-reported cognitive fatigue and the posterior hypothalamic-brainstem network. Eur J Neurol. 2016;23(1):101–9.

    CAS  PubMed  Google Scholar 

  60. Pessoa L, McMenamin B. Dynamic networks in the emotional brain. Neuroscientist. 2017;23(4):383–96.

    PubMed  Google Scholar 

  61. Suh JS, Schneider MA, Minuzzi L, MacQueen GM, Strother SC, et al. Cortical thickness in major depressive disorder: a systematic review and meta-analysis. Prog Neuro-Psychopharmacol Biol Psychiatry. 2019;10(88):287–302.

    Google Scholar 

  62. Castanheira L, Silvia C, Cheniaux E, Telles-Correira D. Neuroimaging correlates of depression-implications to clinical practice. Front Psych. 2019;10:703.

    Google Scholar 

  63. Hidalgo de la Cruz M, D’Ambrosio A, Valsasina P, Elisabetta Pagani E, Colombo B, Rodegher M, et al. Abnormal functional connectivity of thalamic sub-regions contributes to fatigue in multiple sclerosis. Mult Scler J. 2018;24(9):1183–95.

    Google Scholar 

  64. Heat RG. Modulation of emotion with a brain pacemaker. Treatment for intractable psychiatric illness. J Nerv Ment Dis. 1977;165(5):300–17.

    Google Scholar 

  65. Ramasamy DP, Benedict RH, Cox JL, Fritz D, Abdelrahman N, Hussein S, et al. Extent of cerebellum, subcortical and cortical atrophy in patients with MS: a case-control study. J Neurol Sci. 2009;282(1–2):47–54.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

Study concept and design: LA, MM, FS, FA, and GP. MRI analysis and interpretation: LA and AM. Critical revision of the manuscript: LA, MM, FA, and GP. Statistical analysis of the data: LA. Neuropsychological assessment administration and interpretation: RA. Study supervision: GP.

Corresponding author

Correspondence to Andrea Lazzarotto.

Ethics declarations

Conflict of Interest

LA has received travel grant from Roche, Sanofi-Genzyme, Merk serono, and Teva. MM has received travel grant from Sanofi-Genzyme, Merk serono, Teva, Biogen, and Mylan, and has received a personal fee from Novartis. FS has nothing to declare. ZF has received travel grant from Roche, Sanofi-Genzyme, Merk serono, Teva, Biogen, and Novartis. MA has received travel grant from Sanofi-Genzyme. AR reports grants and personal fees from Novartis, grants and personal fees from Biogen Idec, grants from Teva, and grants from Merck Serono during the conduct of the study. PD has nothing to declare. MA has nothing to declare. FA has received honoraria from Novartis, Teva, and Almirall. GP reports grants and personal fees from Merck Serono, grants and personal fees from Biogen Idec, grants and personal fees from Genzyme Sanofi, grants and personal fees from Bayer Schering Pharma, grants and personal fees from Novartis, grants and personal fees from Teva, grants from University of Padua, Department of Neurosciences DNS, grants from Veneto Region of Italy, grants from Italian Association for Multiple Sclerosis (AISM), and grants from the Italian Ministry of Public Health during the conduct of the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOC 3000 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lazzarotto, A., Margoni, M., Franciotta, S. et al. Selective Cerebellar Atrophy Associates with Depression and Fatigue in the Early Phases of Relapse-Onset Multiple Sclerosis. Cerebellum 19, 192–200 (2020). https://doi.org/10.1007/s12311-019-01096-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12311-019-01096-4

Keywords

Navigation