Acoustic Speech Analytics Are Predictive of Cerebellar Dysfunction in Multiple Sclerosis

Abstract

Speech production relies on motor control and cognitive processing and is linked to cerebellar function. In diseases where the cerebellum is impaired, such as multiple sclerosis (MS), speech abnormalities are common and can be detected by instrumental assessments. However, the potential of speech assessments to be used to monitor cerebellar impairment in MS remains unexplored. The aim of this study is to build an objectively measured speech score that reflects cerebellar function, pathology and quality of life in MS. Eighty-five people with MS and 21 controls participated in the study. Speech was independently assessed through objective acoustic analysis and blind expert listener ratings. Cerebellar function and overall disease disability were measured through validated clinical scores; cerebellar pathology was assessed via magnetic resonance imaging, and validated questionnaires informed quality of life. Selected speech variables were entered in a regression model to predict cerebellar function. The resulting model was condensed into one composite speech score and tested for prediction of abnormal 9-hole peg test (9HPT), and for correlations with the remaining cerebellar scores, imaging measurements and self-assessed quality of life. Slow rate of syllable repetition and increased free speech pause percentage were the strongest predictors of cerebellar impairment, complemented by phonatory instability. Those variables formed the acoustic composite score that accounted for 54% of variation in cerebellar function, correlated with cerebellar white matter volume (r = 0.3, p = 0.017), quality of life (r = 0.5, p < 0.001) and predicted an abnormal 9HPT with 85% accuracy. An objective multi-feature speech metric was highly representative of motor cerebellar impairment in MS.

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References

  1. 1.

    Klevan G, Jacobsen CO, Aarseth JH, Myhr KM, Nyland H, Glad S, et al. Health related quality of life in patients recently diagnosed with multiple sclerosis. Acta Neurol Scand. 2014;129:21–6.

  2. 2.

    Kalincik T, Buzzard K, Jokubaitis V, et al. Risk of relapse phenotype recurrence in multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England). 2014;20:1511–22.

    Article  Google Scholar 

  3. 3.

    Schmahmann JD, Guell X, Stoodley CJ, Halko MA. The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci. 2019;42:337–64.

    CAS  Article  Google Scholar 

  4. 4.

    Kurtzke JF. Rating neurologic impairment in multiple sclerosis an expanded disability status scale (EDSS). Neurology. 1983;33:1444–52.

    CAS  Article  Google Scholar 

  5. 5.

    Schmitz-Hübsch T, Du Montcel ST, Baliko L, et al. Scale for the assessment and rating of ataxia: development of a new clinical scale. Neurology. 2006;66:1717–20.

    Article  Google Scholar 

  6. 6.

    Jacobsen C, Hagemeier J, Myhr KM, Nyland H, Lode K, Bergsland N, et al. Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry. 2014;85:1109–15.

  7. 7.

    Ilg W, Branscheidt M, Butala A, et al. Consensus paper: neurophysiological assessments of ataxias in daily practice. Cerebellum (London, England). 2018;17:628–53.

    CAS  Article  Google Scholar 

  8. 8.

    Riecker A, Mathiak K, Wildgruber D, Erb M, Hertrich I, Grodd W, et al. fMRI reveals two distinct cerebral networks subserving speech motor control. Neurology. 2005;64:700–6.

  9. 9.

    Folker JE, Murdoch BE, Rosen KM, Cahill LM, Delatycki MB, Corben LA, et al. Differentiating profiles of speech impairments in Friedreich’s ataxia: a perceptual and instrumental approach. Int J Lang Commun Disord. 2012;47:65–76.

  10. 10.

    Vogel AP, Rommel N, Oettinger A, Horger M, Krumm P, Kraus EM, et al. Speech and swallowing abnormalities in adults with POLG associated ataxia (POLG-A). Mitochondrion. 2017;37:1–7.

  11. 11.

    Vogel AP, Rommel N, Oettinger A, Stoll LH, Kraus EM, Gagnon C, et al. Coordination and timing deficits in speech and swallowing in autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). J Neurol. 2018;265:2060–70.

  12. 12.

    Vogel AP, Wardrop MI, Folker JE, et al. Voice in Friedreich ataxia. J Voice. 2017;31:243.e249–19.

  13. 13.

    Valentino P, Cerasa A, Chiriaco C, Nisticò R, Pirritano D, Gioia MC, et al. Cognitive deficits in multiple sclerosis patients with cerebellar symptoms. Mult Scler J. 2009;15:854–9.

  14. 14.

    Durisko C, Fiez JA. Functional activation in the cerebellum during working memory and simple speech tasks. Cortex. 2010;46:896–906.

  15. 15.

    Ackermann H, Mathiak K, Ivry RB. Temporal organization of “internal speech” as a basis for cerebellar modulation of cognitive functions. Behav Cogn Neurosci Rev. 2004;3:14–22.

    Article  Google Scholar 

  16. 16.

    Ravizza SM, McCormick CA, Schlerf JE, Justus T, Ivry RB, Fiez JA. Cerebellar damage produces selective deficits in verbal working memory. Brain. 2006;129:306–20.

    Article  Google Scholar 

  17. 17.

    Rodgers JD, Tjaden K, Feenaughty L, Weinstock-Guttman B, Benedict RH. Influence of cognitive function on speech and articulation rate in multiple sclerosis. J Int Neuropsychol Soc. 2013;19:173–80.

    Article  Google Scholar 

  18. 18.

    Arnett PA, Smith MM, Barwick FH, Benedict RH, Ahlstrom BP. Oralmotor slowing in multiple sclerosis: relationship to neuropsychological tasks requiring an oral response. J Int Neuropsychol Soc. 2008;14:454–62.

    Article  Google Scholar 

  19. 19.

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

  20. 20.

    Mathiowetz V, Weber K, Kashman N, Volland G. Adult norms for the nine hole peg test of finger dexterity. Occup Ther J Res. 1985;5:24–38.

    Article  Google Scholar 

  21. 21.

    Boonstra F, Perera T, Noffs G, et al. Novel functional MRI task for studying the neural correlates of upper limb tremor. Front Neurol. 2018;9:513.

    Article  Google Scholar 

  22. 22.

    Diedrichsen J, Balsters JH, Flavell J, Cussans E, Ramnani N. A probabilistic MR atlas of the human cerebellum. NeuroImage. 2009;46:39–46.

    Article  Google Scholar 

  23. 23.

    Noffs G, Perera T, Kolbe SC, Shanahan CJ, Boonstra FMC, Evans A, et al. What speech can tell us: a systematic review of dysarthria characteristics in multiple sclerosis. Autoimmun Rev. 2018;17:1202–9.

  24. 24.

    Hartelius L, Buder EH, Strand EA. Long-term phonatory instability in individuals with multiple sclerosis. J Speech Lang Hear Res. 1997;40:1056–72.

    CAS  Article  Google Scholar 

  25. 25.

    Vogel AP, Fletcher J, Maruff P. The impact of task automaticity on speech in noise. Speech Comm. 2014;65:1–8.

    Article  Google Scholar 

  26. 26.

    Vogel AP, Maruff P. Monitoring change requires a rethink of assessment practices in voice and speech. Logoped Phoniatr Vocol. 2014;39:56–61.

    Article  Google Scholar 

  27. 27.

    Tjaden K, Watling E. Characteristics of diadochokinesis in multiple sclerosis and Parkinson’s disease. Folia Phoniatr Logop. 2003;55:241–59.

    Article  Google Scholar 

  28. 28.

    Kent RD, Vorperian HK, Duffy JR. Reliability of the Multi-Dimensional Voice Program for the analysis of voice samples of subjects with dysarthria. Am J Speech Lang Pathol. 1999;8:129–36.

    Article  Google Scholar 

  29. 29.

    Vogel AP, Maruff P, Snyder PJ, Mundt JC. Standardization of pitch-range settings in voice acoustic analysis. Behav Res Methods. 2009;41:318–24.

    Article  Google Scholar 

  30. 30.

    Shue Y-L. The voice source in speech production: data, analysis and models. Los Angeles: University of California; 2010.

    Google Scholar 

  31. 31.

    Rosen KM, Goozée JV, Murdoch BE. Examining the effects of multiple sclerosis on speech production: does phonetic structure matter? J Commun Disord. 2008;41:49–69.

    Article  Google Scholar 

  32. 32.

    Wang YT, Kent RD, Duffy JR, Thomas JE. Analysis of diadochokinesis in ataxic dysarthria using the motor speech profile program. Folia Phoniatr Logop. 2009;61:1–11.

  33. 33.

    Boersma P. Praat, a system for doing phonetics by computer. Glot Int. 2001;5:341–345.

  34. 34.

    Rosen K, Murdoch B, Folker J, Vogel A, Cahill L, Delatycki M, et al. Automatic method of pause measurement for normal and dysarthric speech. Clin Linguist Phon. 2010;24:141–54.

  35. 35.

    Hobart J, Lamping D, Fitzpatrick R, Riazi A, Thompson A. The Multiple Sclerosis Impact Scale (MSIS-29): a new patient-based outcome measure. Brain. 2001;124:962–73.

    CAS  Article  Google Scholar 

  36. 36.

    Benninger MS, Ahuja AS, Gardner G, Grywalski C. Assessing outcomes for dysphonic patients. J Voice. 1998;12:540–50.

  37. 37.

    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29:1165–88.

    Article  Google Scholar 

  38. 38.

    Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci. 2015;16:159–72.

    CAS  Article  Google Scholar 

  39. 39.

    Hamilton F, Rochester L, Paul L, Rafferty D, O’Leary C, Evans J. Walking and talking: an investigation of cognitive–motor dual tasking in multiple sclerosis. Mult Scler. 2009;15:1215–27.

    CAS  Article  Google Scholar 

  40. 40.

    Rusz J, Benova B, Ruzickova H, Novotny M, Tykalova T, Hlavnicka J, et al. Characteristics of motor speech phenotypes in multiple sclerosis. Mult Scler Relat Disord. 2018;19:62–9.

  41. 41.

    Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci. 2017;11:555.

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the immeasurable contribution of participants who voluntarily donated their time and patience for science.

Funding

This study was funded by NHMRC fellowship grants number 1085461 and 1082910.

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Authors

Contributions

Adam Vogel was responsible for conception, organization and execution of the research project; critique and writing of the manuscript.

Andrew Evans was responsible for conception of the research project; review and critique of the manuscript.

Anneke van der Walt was responsible for conception, organization and execution of the research project; review and critique of the statistical analysis; review, critique and writing of manuscript.

Frederique Boonstra was responsible for conception, organization and execution of the research project; review, critique and writing of the manuscript.

Gustavo Noffs was responsible for conception, organization and execution of the research project; design and execution of the statistical analysis; writing of the first and subsequent drafts.

Helmut Butzkueven was responsible for conception of the research project; review and critique of the statistical analysis; review, critique and writing of the manuscript.

Jim Stankovich was responsible for design, critique and review of the statistical analysis.

Scott Kolbe was responsible for conception and organization of the research project; review and critique of the statistical analysis; review, critique and writing of the manuscript.

Thushara Perera was responsible for conception of the research project; design, review and critique of the statistical analysis; review, critique and writing of the manuscript.

Corresponding author

Correspondence to Gustavo Noffs.

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Conflict of Interest

Adam Vogel is Chief Science Officer of Redenlab Inc. He receives grant and fellowship funding from the National Health and Medical Research Council of Australia. Andrew Evans received honoraria from Novartis for giving presentations and providing consultancy services. He has participated in scientific advisory board meetings for Novartis, UCB Pharma, Allergan, and Boehringer Ingelheim. He has received conference travel support from Boehringer Ingelheim. Anneke van der Walt has received travel support and served on advisory boards for Novartis, Biogen, Merck Serono, Roche and Teva. She receives grant support from the National Health and Medical Research Council of Australia. Frederique M.C. Boonstra has nothing to disclose. Gustavo Noffs has nothing to disclose. Helmut Butzkueven served on scientific advisory boards for Biogen, Novartis and Sanofi-Aventis and received conference travel support from Novartis, Biogen and Sanofi Aventis. He serves on steering committees for trials conducted by Biogen and Novartis received research support from Merck, Novartis and Biogen. Scott Kolbe receives grant income from the National Health and Medical Research Council of Australia and has received honoraria from Novartis, Biogen and Merck. Thushara Perera has nothing to disclose.

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Noffs, G., Boonstra, F.M.C., Perera, T. et al. Acoustic Speech Analytics Are Predictive of Cerebellar Dysfunction in Multiple Sclerosis. Cerebellum (2020). https://doi.org/10.1007/s12311-020-01151-5

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Keywords

  • Multiple Sclerosis
  • disability evaluation
  • speech acoustics
  • magnetic resonance imaging
  • quality of life
  • biomarkers