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The efficacy of acoustic-based articulatory phenotyping for characterizing and classifying four divergent neurodegenerative diseases using sequential motion rates

  • Neurology and Preclinical Neurological Studies - Original Article
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Abstract

Despite the impacts of neurodegeneration on speech function, little is known about how to comprehensively characterize the resulting speech abnormalities using a set of objective measures. Quantitative phenotyping of speech motor impairments may have important implications for identifying clinical syndromes and their underlying etiologies, monitoring disease progression over time, and improving treatment efficacy. The goal of this research was to investigate the validity and classification accuracy of comprehensive acoustic-based articulatory phenotypes in speakers with distinct neurodegenerative diseases. Articulatory phenotypes were characterized based on acoustic features that were selected to represent five components of motor performance: Coordination, Consistency, Speed, Precision, and Rate. The phenotypes were first used to characterize the articulatory abnormalities across four progressive neurologic diseases known to have divergent speech motor deficits: amyotrophic lateral sclerosis (ALS), progressive ataxia (PA), Parkinson’s disease (PD), and the nonfluent variant of primary progressive aphasia and progressive apraxia of speech (nfPPA + PAOS). We then examined the efficacy of articulatory phenotyping for disease classification. Acoustic analyses were conducted on audio recordings of 217 participants (i.e., 46 ALS, 52 PA, 60 PD, 20 nfPPA + PAOS, and 39 controls) during a sequential speech task. Results revealed evidence of distinct articulatory phenotypes for the four clinical groups and that the phenotypes demonstrated strong classification accuracy for all groups except ALS. Our results highlight the phenotypic variability present across neurodegenerative diseases, which, in turn, may inform (1) the differential diagnosis of neurological diseases and (2) the development of sensitive outcome measures for monitoring disease progression or assessing treatment efficacy.

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Data availability

The data that support the findings of this study are available from MGH, New York University, University of Buffalo, University of Strathclyde, and University of Washington, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the respective institution.

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Acknowledgements

This work was supported by the following grants awarded by the National Institute on Deafness and Other Communication Disorders (NIDCD), part of the National Institute of Health (NIH): R01DC013547 (PI: Jordan R. Green), 24DC016312 (PI: Jordan R. Green), R01DC014296 (PI: Bradford C. Dickerson), and F31DC019556 (PI: Hannah P. Rowe). The authors would also like to thank Dr. Kris Tjaden from the University at Buffalo, Dr. John Sidtis from New York University, and Megan Quimby from Massachusetts General Hospital (MGH) for generously sharing their datasets and the University of Wisconsin–Madison for making the X-Ray Microbeam Speech Production (XRMB) Database available.

Funding

National Institute on Deafness and Other Communication Disorders, R01DC013547, Jordan R. Green, 24DC016312, Jordan R. Green, R01DC014296, Bradford C. Dickerson, F31DC019556, Hannah Rowe.

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Authors and Affiliations

  1. Department of Rehabilitation Sciences, MGH Institute of Health Professions, Charlestown, Boston, MA, USA

    Hannah P. Rowe & Jordan R. Green

  2. School of Healthcare Leadership, MGH Institute of Health Professions, Boston, MA, USA

    Perman Gochyyev

  3. Berkeley Evaluation and Assessment Research Center, University of California at Berkeley, Berkeley, CA, USA

    Perman Gochyyev

  4. Department of Biomedical Engineering, Worchester Polytechnic Institute, Worcester, MA, USA

    Adam C. Lammert

  5. Department of Speech and Language Therapy, University of Strathclyde, Glasgow, Scotland, UK

    Anja Lowit

  6. Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, USA

    Kristie A. Spencer

  7. Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Bradford C. Dickerson & James D. Berry

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Rowe, H.P., Gochyyev, P., Lammert, A.C. et al. The efficacy of acoustic-based articulatory phenotyping for characterizing and classifying four divergent neurodegenerative diseases using sequential motion rates. J Neural Transm 129, 1487–1511 (2022). https://doi.org/10.1007/s00702-022-02550-0

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