Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.

IF 2.8
Justin Truong, Leif Simmatis, Timothy Pommée, Agessandro Abrahao, Kerry Adams, Marvin Chum, Colleen O'Connell, Angela Genge, Sanjay Kalra, Benjamin Ritsma, Kerri Schellenberg, Christen Shoesmith, Lorne Zinman, Gord Jewett, Yana Yunusova
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Abstract

Objective: Motor neuron diseases (MNDs) result in a spectrum of motor impairments, including considerable effects on speech function, which manifest as dysarthria-a motor speech disorder. Speech metrics are increasingly recognized as critical biomarkers with potential utility in disease diagnosis and phenotyping. This study aimed to (1) characterize acoustics of upper motor neuron (UMN) and lower motor neuron (LMN) dysarthria presentations in MNDs, and (2) identify relationships between bulbar disease severity scores and acoustic features, as these could collectively enable personalized approaches to management of these diseases.

Methods: Data from 16 individuals with primary lateral sclerosis (PLS) representing UMN disease, 14 individuals with spinal and bulbar muscular atrophy (SBMA) representing LMN disease, and 25 neurologically healthy individuals were analyzed. Clinical measures were also collected from PLS and SBMA groups. All participants were remotely recorded performing passage reading, rapid syllable repetition, and vowel phonation. Fifty-two acoustic features were extracted representing articulation, phonation, prosody, resonance, and overall speech timing. Features were compared using Kruskal-Wallis tests for between-group comparisons and Spearman correlations between acoustic features and clinical scores.

Results: Articulatory and prosodic features best differentiated PLS, SBMA and controls. Correlations were observed in the PLS group between the clinical score and various articulatory features, most notably those indexing tongue and jaw movements.

Conclusions: Our study demonstrated that acoustic assessment could capture fingerprints of dysarthrias associated with PLS and SBMA. These findings also demonstrate the potential for remote speech assessment to characterize diverse dysarthria profiles and pave the way for creating ways for personalized disease management approaches in clinical care and trials.

用自动声学分析鉴别上下运动神经元疾病。
目的:运动神经元疾病(mnd)导致一系列的运动障碍,包括对语言功能的相当大的影响,表现为构音障碍-一种运动语言障碍。语音测量越来越被认为是在疾病诊断和表型分析中具有潜在效用的关键生物标志物。本研究旨在(1)表征mds中上运动神经元(UMN)和下运动神经元(LMN)构音障碍表现的声学特征,(2)确定球疾病严重程度评分与声学特征之间的关系,因为这些特征可以共同实现这些疾病的个性化治疗方法。方法:对16例原发性侧索硬化症(PLS)、14例脊髓和球性肌萎缩症(SBMA)和25例神经系统健康者的资料进行分析。还收集了PLS组和SBMA组的临床指标。所有的参与者都被远程记录在阅读短文、快速音节重复和元音发音上。提取了52个声学特征,分别代表发音、韵律、共振和整体语音时间。使用Kruskal-Wallis检验进行组间比较,并使用Spearman检验比较声学特征与临床评分之间的相关性。结果:发音和韵律特征最能区分PLS、SBMA和对照组。在PLS组中观察到临床评分与各种发音特征之间的相关性,最明显的是那些指示舌和下颌运动的特征。结论:我们的研究表明,声学评估可以捕获与PLS和SBMA相关的构音障碍的指纹。这些发现也证明了远程语音评估对不同构音障碍特征的潜力,并为在临床护理和试验中创建个性化疾病管理方法铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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