Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA; Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA.
Neuropsychologia. 2019 Oct;133:107157. doi: 10.1016/j.neuropsychologia.2019.107157. Epub 2019 Aug 8.
Currently, variant subtyping in primary progressive aphasia (PPA) requires an expert neurologist and extensive language and cognitive testing. Spelling impairments appear early in the development of the disorder, and the three PPA variants (non-fluent - nfvPPA; semantic - svPPA; logopenic - lvPPA) reportedly show fairly distinct spelling profiles. Given the theoretical and empirical evidence indicating that spelling may serve as a proxy for spoken language, the current study aimed to determine whether spelling performance alone, when evaluated with advanced statistical analyses, allows for accurate PPA variant classification. A spelling to dictation task (with real words and pseudowords) was administered to 33 PPA individuals: 17 lvPPA, 10 nfvPPA, 6 svPPA. Using machine learning classification algorithms, we obtained pairwise variant classification accuracies that ranged between 67 and 100%. In additional analyses that assumed no prior knowledge of each case's variant, classification accuracies ranged between 59 and 70%. To our knowledge, this is the first time that all the PPA variants, including the most challenging logopenic variant, have been classified with such high accuracy when using information from a single language task. These results underscore the rich structure of the spelling process and support the use of a spelling task in PPA variant classification.
目前，原发性进行性失语症（PPA）的变体亚型需要专家神经科医生和广泛的语言和认知测试。拼写障碍在疾病的早期就出现了，据报道，三种 PPA 变体（非流利型 nfvPPA；语义性 svPPA；流畅性失语型 lvPPA）表现出相当明显的拼写特征。鉴于有理论和实证证据表明拼写可以作为口语的替代物，本研究旨在确定仅通过高级统计分析评估拼写表现是否可以准确地对 PPA 变体进行分类。对 33 名 PPA 患者进行了拼写听写任务（包括真实单词和假单词）：17 名 lvPPA、10 名 nfvPPA、6 名 svPPA。使用机器学习分类算法，我们获得了两两变体分类准确率在 67%至 100%之间。在不预先了解每个病例变体的额外分析中，分类准确率在 59%至 70%之间。据我们所知，这是第一次在使用单一语言任务的信息时，以如此高的准确率对所有 PPA 变体（包括最具挑战性的流畅性失语型）进行分类。这些结果强调了拼写过程的丰富结构，并支持在 PPA 变体分类中使用拼写任务。
