Machine learning in the clinical and language characterisation of primary progressive aphasia variants
Machine learning in the clinical and language characterisation of primary progressive aphasia variants
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DOI：
10.1016/j.cortex.2019.05.007
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发表时间：
2019-10-01
期刊：
影响因子：
3.6
通讯作者：
Ayala, Jose L.
中科院分区：
文献类型：
--
作者：
Matias-Guiu, Jordi A.;Diaz-Alvarez, Josefa;Ayala, Jose L.
简介：原发性进行性失语症（PPA）是一种神经退行性疾病的临床综合征，主要有三种变体：不流利，语义和逻辑缺失。然而，关于是否存在其他亚型存在一些争议。我们的目的是研究的语言和认知功能与一个新的建议的分类为PPA.Material和方法：68例PPA患者在疾病的早期阶段和20名健康对照进行了评估，一个全面的语言和认知协议。并进行F-18-FDG正电子发射断层扫描（PET）。患者根据FDG PET区域代谢进行分类，使用我们以前开发的算法的基础上的分层凝聚聚类分析与沃德的联系方法。发现了5种变体，其中非流畅和逻辑缺失变体分为2种亚型。机器学习技术被用来预测每个变量根据语言评估results.Results：不流利的类型1与较差的性能在重复的句子和阅读的不规则单词比不流利的类型2。相反，第二组表现出更高程度的言语失用症。1型逻辑缺失变异患者在动作命名方面的表现比2型逻辑缺失患者更差。语言评估预测PET为基础的亚型在86%-89%的情况下，使用聚类分析和主成分analysis.Conclusions：我们的研究支持存在的5种变体的PPA。这些变体在语言和FDG PET成像特征上显示出一些差异。使用语言测试数据的机器学习算法能够以相对较高的准确度预测5种PPA变体中的每一种，并实现PPA变体的自动化、机器辅助诊断的可能性。(C)2019爱思唯尔有限公司版权所有。
Introduction: Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA.Material and methods: Sixty-eight patients with PPA in early stages of the disease and 20 healthy controls were assessed with a comprehensive language and cognitive protocol. They were also evaluated with F-18-FDG positron emision tomography (PET). Patients were classified according to FDG PET regional metabolism, using our previously developed algorithm based on a hierarchical agglomerative cluster analysis with Ward's linkage method. Five variants were found, with both the non-fluent and logopenic variants being split into 2 subtypes. Machine learning techniques were used to predict each variant according to language assessment results.Results: Non-fluent type 1 was associated with poorer performance in repetition of sentences and reading of irregular words than non-fluent type 2. Conversely, the second group showed a higher degree of apraxia of speech. Patients with logopenic variant type 1 performed more poorly on action naming than patients with logopenic type 2. Language assessments were predictive of PET-based subtypes in 86%-89% of cases using clustering analysis and principal components analysis.Conclusions: Our study supports the existence of 5 variants of PPA. These variants show some differences in language and FDG PET imaging characteristics. Machine learning algorithms using language test data were able to predict each of the 5 PPA variants with a relatively high degree of accuracy, and enable the possibility of automated, machine-aided diagnosis of PPA variants. (C) 2019 Elsevier Ltd. All rights reserved.