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Automated profiling of spontaneous speech in primary progressive aphasia and behavioral-variant frontotemporal dementia: An approach based on usage-frequency
Cortex ( IF 3.6 ) Pub Date : 2020-09-22 , DOI: 10.1016/j.cortex.2020.08.027
Vitor C Zimmerer 1 , Chris J D Hardy 2 , James Eastman 3 , Sonali Dutta 4 , Leo Varnet 5 , Rebecca L Bond 2 , Lucy Russell 2 , Jonathan D Rohrer 2 , Jason D Warren 2 , Rosemary A Varley 1
Affiliation  

Language production provides important markers of neurological health. One feature of impairments of language and cognition, such as those that occur in stroke aphasia or Alzheimer's disease, is an overuse of high frequency, “familiar” expressions. We used computerized analysis to profile narrative speech samples from speakers with variants of frontotemporal dementia (FTD), including subtypes of primary progressive aphasia (PPA). Analysis was performed on language samples from 29 speakers with semantic variant PPA (svPPA), 25 speakers with logopenic variant PPA (lvPPA), 34 speakers with non-fluent variant PPA (nfvPPA), 14 speakers with behavioral variant FTD (bvFTD) and 20 older normal controls (NCs). We used frequency and collocation strength measures to determine use of familiar words and word combinations. We also computed word counts, content word ratio and a combination ratio, a measure of the degree to which the individual produces connected language. All dementia subtypes differed significantly from NCs. The most discriminating variables were word count, combination ratio, and content word ratio, each of which distinguished at least one dementia group from NCs. All participants with PPA, but not participants with bvFTD, produced significantly more frequent forms at the level of content words, word combinations, or both. Each dementia group differed from the others on at least one variable, and language production variables correlated with established behavioral measures of disease progression. A machine learning classifier, using narrative speech variables, achieved 90% accuracy when classifying samples as NC or dementia, and 59.4% accuracy when matching samples to their diagnostic group. Automated quantification of spontaneous speech in both language-led and non-language led dementias, is feasible. It allows extraction of syndromic profiles that complement those derived from standardized tests, warranting further evaluation as candidate biomarkers. Inclusion of frequency-based language variables benefits profiling and classification.



中文翻译:

原发性进行性失语症和行为变异性额颞叶痴呆中自发语音的自动分析:一种基于使用频率的方法

语言产生是神经健康的重要标志。语言和认知障碍的一个特征,例如在中风失语症或阿尔茨海默病中发生的那些,是过度使用高频“熟悉”表达。我们使用计算机分析来分析来自患有额颞叶痴呆 (FTD) 变体的演讲者的叙述性语音样本,包括原发性进行性失语症 (PPA) 的亚型。对来自 29 名具有语义变异 PPA (svPPA) 的说话者、25 名具有对数开放变异 PPA (lvPPA) 的说话者、34 名具有非流利变异 PPA (nfvPPA) 的说话者、14 名具有行为变异 FTD (bvFTD) 的说话者和 20 名说话者的语言样本进行了分析。较旧的正常对照(NC)。我们使用频率和搭配强度测量来确定熟悉单词和单词组合的使用。我们还计算了字数,内容词比率和组合比率,衡量个人产生关联语言的程度。所有痴呆亚型均与 NC 显着不同。最具区分性的变量是字数、组合比例和内容词比例,每个变量都将至少一个痴呆组与 NC 区分开来。所有 PPA 参与者,但不是 bvFTD 参与者,在实词、词组合或两者的水平上产生了明显更频繁的形式。每个痴呆组在至少一个变量上与其他组不同,语言产生变量与疾病进展的既定行为测量相关。使用叙述性语音变量的机器学习分类器在将样本分类为 NC 或痴呆症时达到 90% 的准确率,以及 59. 将样本与其诊断组匹配时的准确率为 4%。对语言主导和非语言主导的痴呆症中的自发言语进行自动量化是可行的。它允许提取与标准化测试得出的补充特征的综合征特征,作为候选生物标志物需要进一步评估。包含基于频率的语言变量有利于分析和分类。

更新日期:2020-10-30
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