Journal of Neurology

, Volume 266, Issue 6, pp 1394–1404 | Cite as

Distinctive speech signature in cerebellar and parkinsonian subtypes of multiple system atrophy

  • Jan RuszEmail author
  • Tereza Tykalová
  • Giulio Salerno
  • Serena Bancone
  • Johara Scarpelli
  • Maria Teresa Pellecchia
Original Communication


Although motor speech disorders represent an early and prominent clinical feature of multiple system atrophy (MSA), the potential usefulness of speech assessment as a diagnostic tool has not yet been explored. This cross-sectional study aimed to provide a comprehensive, objective description of motor speech function in the parkinsonian (MSA-P) and cerebellar (MSA-C) variants of MSA. Speech samples were acquired from 80 participants including 18 MSA-P, 22 MSA-C, 20 Parkinson’s disease (PD), and 20 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on quantitative acoustic analysis of 14 speech dimensions. A mixed type of dysarthria involving hypokinetic, ataxic and spastic components was found in the majority of MSA patients independent of phenotype. MSA-P showed significantly greater speech impairment than PD, and predominantly exhibited harsh voice, imprecise consonants, articulatory decay, monopitch, excess pitch fluctuation and pitch breaks. MSA-C was dominated by prolonged phonemes, audible inspirations and voice stoppages. Inappropriate silences, irregular motion rates and overall slowness of speech were present in both MSA phenotypes. Speech features allowed discrimination between MSA-P and PD as well as between both MSA phenotypes with an area under curve up to 0.86. Hypokinetic, ataxic and spastic dysarthria components in MSA were correlated to the clinical evaluation of rigidity, cerebellar and bulbar/pseudobulbar manifestations, respectively. Distinctive speech alterations reflect underlying pathophysiology in MSA. Objective speech assessment may provide an inexpensive and widely applicable screening instrument for differentiation of MSA and PD from controls and among subtypes of MSA.


Multiple system atrophy Parkinson’s disease Atypical parkinsonism Dysarthria Speech disorder Acoustic analyses 



The authors thank the participants for their time and interest in the study. We are obliged to Hana Ruzickova for the perceptual evaluation of dysarthria. This study was supported by the OP VVV MEYS project “Research Center for Informatics” (Grant no. CZ.02.1.01/0.0/0.0/16_019/0000765), and by the Czech Ministry of Education (PROGRES-Q27/LF1).

Compliance with ethical standards

Conflicts of interest

The authors report no conflicts of interest.

Ethical standards

Each participant provided written, informed consent. The study received approval from an ethical standards committee on human experimentation, and has, therefore, been performed in accordance with the ethical standards established in the 1964 Declaration of Helsinki.


  1. 1.
    Fanciulli A, Wenning GK (2015) Multiple-system atrophy. N Engl J Med 372:249–263CrossRefGoogle Scholar
  2. 2.
    Gilman S, Wenning GK, Low PA et al (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71:670–676CrossRefGoogle Scholar
  3. 3.
    Kent RD, Kent JF, Weismer G, Duffy JR (2000) What dysarthrias can tell us about the neural control of speech. J Phon 28:273–302CrossRefGoogle Scholar
  4. 4.
    Ho AK, Iansek R, Marigliani C, Bradshaw J, Gates S (1998) Speech impairment in large sample of patients with Parkinson’s disease. Behav Neurol 11:131–137CrossRefGoogle Scholar
  5. 5.
    Kluin KJ, Gilman S, Lohman M, Junck L (1996) Characteristics of the dysarthria in multiple system atrophy. Arch Neurol 53:545–548CrossRefGoogle Scholar
  6. 6.
    Postuma RB, Lang AE, Gagnon JF, Pelletier A, Montplaisir JY (2012) How does parkinsonism start? Prodromal parkinsonism motor changes in idiopathic REM sleep behaviour disorder. Brain 135:1860–1870CrossRefGoogle Scholar
  7. 7.
    Hlavnicka J, Cmejla R, Tykalova T, Sonka K, Ruzicka E, Rusz J (2017) Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder. Sci Rep 7:12CrossRefGoogle Scholar
  8. 8.
    Hartelius L, Gustavsson H, Astrand M, Holmberg B (2006) Perceptual analysis of speech in multiple system atrophy and progressive supranuclear palsy. J Med Speech Lang Pathol 14:241–247Google Scholar
  9. 9.
    Kim Y, Kent RD, Kent J, Duffy JR (2010) Perceptual and acoustic features of dysarthria associated with multiple system atrophy. J Med Speech Lang Pathol 18:66–70Google Scholar
  10. 10.
    Sachin S, Shukla G, Goyal V et al (2008) Clinical speech impairment in Parkinson’s disease, progressive supranuclear palsy, and multiple system atrophy. Neurol India 56:122–126CrossRefGoogle Scholar
  11. 11.
    Saxena M, Behari M, Kumaran SS, Goyal V, Narang V (2014) Assessing speech dysfunction using BOLD and acoustic analysis in parkinsonism. Parkinsonism Relat Disord 20:855–861CrossRefGoogle Scholar
  12. 12.
    Huh YE, Park J, Suh MK et al (2015) Differences in early speech patterns between Parkinson variant of multiple system atrophy and Parkinson’s disease. Brain Lang 147:14–20CrossRefGoogle Scholar
  13. 13.
    Rusz J, Bonnet C, Klempir J, Tykalova T, Baborova E, Novotny M, Rulseh A, Ruzicka E (2015) Speech disorders reflect differing pathophysiology in Parkinson’s disease, progressive supranuclear palsy and multiple system atrophy. J Neurol 262:992–1001CrossRefGoogle Scholar
  14. 14.
    Tykalova T, Rusz J, Klempir J, Cmejla R, Ruzicka E (2017) Distinct patterns of consonant articulation among Parkinson’s disease, progressive supranuclear palsy and multiple system atrophy. Brain Lang 165:1–9CrossRefGoogle Scholar
  15. 15.
    Duffy JR (2013) Motor speech disorders: substrates, differential diagnosis and management, 3rd edn. Mosby, St. LouisGoogle Scholar
  16. 16.
    Weismer G (1997) Motor speech disorders. In: Hardcastle WJ, Laver J (eds) The handbook of phonetic sciences. Blackwell, Cambridge, pp 191–219Google Scholar
  17. 17.
    Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184CrossRefGoogle Scholar
  18. 18.
    Payan CA, Viallet F, Landwerhrmeyer BG et al (2011) Disease severity and progression in progressive supranuclear palsy and multiple system atrophy: validation of the NNIPPS-Parkinson plus scale. PLoS One 6:e22293CrossRefGoogle Scholar
  19. 19.
    Darley FL, Aronson AR, Brown JR (1969) Differential diagnostic patterns of dysarthria. J Speech Hear Res 12:246–269CrossRefGoogle Scholar
  20. 20.
    Darley FL, Aronson AR, Brown JR (1969) Cluster of deviant speech dimensions in the dysarthrias. J Speech Hear Res 12:462–496CrossRefGoogle Scholar
  21. 21.
    Rusz J, Cmejla R, Ruzickova H, Ruzicka E (2011) Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. J Acoust Soc Am 129:350–367CrossRefGoogle Scholar
  22. 22.
    Rusz J, Megrelishvili M, Bonnet C, Okujava M, Brozova H, Khatiashvili M, Sekhniashvili M, Janelidze M, Tolosa E, Ruzicka E (2014) A distinct variant of mixed dysarthria reflects parkinsonism and dystonia due to ephedrone abuse. J Neural Transm 121:655–664CrossRefGoogle Scholar
  23. 23.
    Hlavnicka J (2018) Automated analysis of speech disorders in neurodegenerative diseases. Ph.D. Thesis, Faculty of Electrical Engineering, Czech Technical University, Prague, CzechiaGoogle Scholar
  24. 24.
    Vogel AP, Fletcher J, Snyder PJ, Fredrickson A, Maruff P (2011) Reliability, stability, and sensitivity to change and impairment in acoustic measures of timing and frequency. J Voice 25:137–149CrossRefGoogle Scholar
  25. 25.
    Faul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39:175–191CrossRefGoogle Scholar
  26. 26.
    Wenning GK, Shlomo YB, Magalhaes M, Daniel SE, Quinn NP (1994) Clinical features and natural history of multiple system atrophy: an analysis of 100 cases. Brain 117:835–845CrossRefGoogle Scholar
  27. 27.
    Barbosa R, Lampreia T, Bugalho P (2016) The aetiology of idiopathic late onset cerebellar ataxia (ILOCA): clinical and imaging clues for a definitive diagnosis. J Neurol Sci 365:156–157  CrossRefGoogle Scholar
  28. 28.
    Kluin KJ, Gilman S, Markel DS, Koeppe RA, Rosenthal G, Junck L (1998) Speech disorders in olivopontocerebellar atrophy correlate with positron emission tomography findings. Ann Neurol 23:547–554CrossRefGoogle Scholar
  29. 29.
    Kollensperger M, Geser F, Seppi K et al (2008) Red flags for multiple system atrophy. Mov Disord 23:1093–1099CrossRefGoogle Scholar
  30. 30.
    Ozawa T, Sekiya K, Aizawa N, Terajima K, Nishizawa M (2016) Laryngeal stridor in multiple system atrophy: clinicopathological features and causal hypotheses. J Neurol Sci 361:243–249CrossRefGoogle Scholar
  31. 31.
    Koo DL, Lee JY, Joo EY, Hong SB, Nam H (2016) Acoustic characteristics of stridor in multiple system atrophy. PLoS One 11:e0153935CrossRefGoogle Scholar
  32. 32.
    Hunker C, Abbs J, Barlow S (1982) The relationship between parkinsonian rigidity and hypokinesia in the orofacial system: a quantitative analysis. Neurology 32:749–754CrossRefGoogle Scholar
  33. 33.
    Kim Y, Kent RD, Weismer G (2011) An acoustic study of the relationships among neurologic disease, dysarthria type, and severity of dysarthria. J Speech Lang Hear Res 54:417–429CrossRefGoogle Scholar
  34. 34.
    Miller N, Nath U, Noble E, Burn D (2017) Utility and accuracy of perceptual voice and speech distinctions in the diagnosis of Parkinson’s disease, PSP and MSA-P. Neurodegener Dis Manag 7:191–203CrossRefGoogle Scholar
  35. 35.
    Rusz J, Hlavnicka J, Tykalova T, Novotny M, Dusek P, Sonka K, Ruzicka E (2018) Smartphone allows capture of speech abnormalities associated with high risk of developing Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 26:1495–1507CrossRefGoogle Scholar
  36. 36.
    Tang CC, Poston KL, Eckert T et al (2010) Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol 9:149–158CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Circuit Theory, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.Department of Neurology and Centre of Clinical Neuroscience, First Faculty of MedicineCharles UniversityPragueCzech Republic
  3. 3.Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”University of SalernoSalernoItaly

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