Department of Rehabilitation Sciences, MGH Institute of Health Professions, Charlestown, Boston, MA, USA.
School of Healthcare Leadership, MGH Institute of Health Professions, Boston, MA, USA.
J Neural Transm (Vienna). 2022 Dec;129(12):1487-1511. doi: 10.1007/s00702-022-02550-0. Epub 2022 Oct 28.
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.
尽管神经退行性变会对言语功能产生影响，但人们对于如何使用一系列客观测量方法全面描述由此产生的言语异常知之甚少。言语运动障碍的定量表型可能对识别临床综合征及其潜在病因、随时间监测疾病进展以及提高治疗效果具有重要意义。本研究旨在探讨具有不同神经退行性疾病的言语者全面基于声学的发音运动表型的有效性和分类准确性。基于代表运动表现五个组成部分的声学特征来描述发音运动表型：协调性、一致性、速度、精度和速率。这些表型首先用于描述四种进展性神经疾病的发音异常，这些疾病具有不同的言语运动缺陷：肌萎缩侧索硬化症（ALS）、进行性共济失调（PA）、帕金森病（PD）以及非流利型原发性进行性失语症和进行性构音障碍（nfPPA+PAOS）。然后，我们研究了发音表型在疾病分类中的效果。对 217 名参与者（即 46 名 ALS、52 名 PA、60 名 PD、20 名 nfPPA+PAOS 和 39 名对照者）在连续言语任务期间的音频记录进行了声学分析。结果表明，四个临床组具有明显的发音表型，并且除 ALS 外，所有组的表型都具有很强的分类准确性。我们的研究结果强调了神经退行性疾病之间存在的表型可变性，这反过来又可以为（1）神经疾病的鉴别诊断和（2）监测疾病进展或评估治疗效果的敏感结局指标的开发提供信息。
