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

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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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          Author and article information

          Journal
          J Neural Transm (Vienna)
          Journal of neural transmission (Vienna, Austria : 1996)
          Springer Science and Business Media LLC
          1435-1463
          0300-9564
          Dec 2022
          : 129
          : 12
          Affiliations
          [1 ] Department of Rehabilitation Sciences, MGH Institute of Health Professions, Charlestown, Boston, MA, USA.
          [2 ] School of Healthcare Leadership, MGH Institute of Health Professions, Boston, MA, USA.
          [3 ] Berkeley Evaluation and Assessment Research Center, University of California at Berkeley, Berkeley, CA, USA.
          [4 ] Department of Biomedical Engineering, Worchester Polytechnic Institute, Worcester, MA, USA.
          [5 ] Department of Speech and Language Therapy, University of Strathclyde, Glasgow, Scotland, UK.
          [6 ] Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, USA.
          [7 ] Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
          [8 ] Department of Rehabilitation Sciences, MGH Institute of Health Professions, Charlestown, Boston, MA, USA. jgreen2@mghihp.edu.
          Article
          NIHMS1862557 10.1007/s00702-022-02550-0
          10.1007/s00702-022-02550-0
          9859630
          36305960
          c3fa3ff1-4e64-42e8-b8fe-04e5aec9811b
          History

          Domain knowledge,Differential diagnosis,Articulatory features,Acoustic analyses,Neurodegenerative disease

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