Computational psycholinguistic analysis of speech samples in PPA and AD and FTD

摘要 原发性进行性失语症（PPA）是一种以异常为特征的神经退行性疾病 在语言中，其他认知过程最初相对较少。该综合征可能由几种原因引起 阿尔茨海默病（AD）和额颞叶变性（FTLD）等多种神经病理。 不同的神经病理学原因与疾病的特定变体有关。人士 PPA的非流利变体（nfvPPA）倾向于表现出努力的言语和语法缺失，在某些情况下伴有运动 言语功能障碍在句子重复和词汇提取方面的障碍表现在那些 PPA的逻辑减少变体（lvPPA）。在物体命名和单词理解方面的困难是在 具有PPA的语义变体（svPPA）的个体。虽然被广泛使用，但目前的分类系统 挑战的出现，个人与重叠的语言行为概况，并通过一个 语言轮廓和皮质萎缩模式的不一致对齐。此外，其中一些相同的 在AD或FTLD的非PPA临床表型患者中可以看到语言或解剖学异常。 也就是说，这些PPA亚型可能代表了一种对认知功能的多维谱进行分类的方法。 神经退行性病变引起的行为解剖异常;我们需要新的方法 量化这些异常，我们需要考虑替代分类方案。这里我们 引入一种新的方法来实现这两种可能性。自然的最新发展 语言处理（NLP）和机器学习（ML）现在已经使自动发现成为可能 和语言特征的分类。一旦建立，这些要素集就可以连接到分布 大脑皮层萎缩，从而使特定的语言行为异常和潜在的 神经网络这种分析PPA亚型的方法，以及它们与其他 AD和FTLD的临床类型，可以通过足够大量的语言样本来实现 以突出语言系统的产生和理解方面的方式收集。在 此外，这种分析需要使用最新一代的人工智能模型，称为 变压器网络其结果将是对PPA综合征和语言网络的新认识 它所影响的。在目标1中，我们将研究无监督人工智能模型的性能， 测量和分类PPA患者的语言异常。在目标2中，我们将研究这些 模型可用于测量和分类AD和FTD患者的语言异常。在目标3中，我们 评价PPA、AD和FTD中语言异常的这些自动测量的可靠性。通过 对PPA和其他形式的AD或FTLD患者的语言进行更细粒度的分析， 为了更好地了解这些痴呆症的重叠和可分离的特征， 从而导致改进的诊断分类和更好的诊断。

Abstract Primary Progressive Aphasia (PPA) is a clinical neurodegenerative syndrome characterized by abnormalities in language with initial relative sparing of other cognitive processes. The syndrome may result from several kinds of neuropathology, including Alzheimer's disease (AD) or Frontotemporal Lobar Degeneration (FTLD). The different neuropathological causes are associated with specific variants of the disease. Individuals with the non-fluent variant of PPA (nfvPPA) tend to show effortful speech and agrammatism, in some cases with motor speech dysfunction. Impairments in sentence repetition and lexical retrieval are exhibited by those with the logopenic variant of PPA (lvPPA). Difficulties in object naming and word comprehension are experienced in individuals with the semantic variant of PPA (svPPA). While widely used, the current system of classification is challenged by the occurrence of individuals with overlapping profiles of linguistic behavior and by an inconsistent alignment of linguistic profiles and patterns of cortical atrophy. In addition, some of these same linguistic or anatomic abnormalities can be seen in patients with non-PPA clinical phenotypes of AD or FTLD. That is, these PPA subtypes may represent one way of classifying a multidimensional spectrum of cognitive- behavioral anatomic abnormalities arising from a set of neurodegenerative pathologies; we need new ways of quantifying these abnormalities, and we need to consider alternative classification schemes. Here we introduce a new approach to accomplishing both of these possibilities. Recent developments in Natural Language Processing (NLP) and Machine Learning (ML) have now made possible the automated discovery and classification of linguistic features. Once established, these feature sets can be connected to distributions of cortical atrophy, thus enabling links between specific linguistic behavioral abnormalities and underlying neural networks. This approach to the analysis of PPA subtypes, and their contextualization with other clinical types of AD and FTLD, can be achieved through a sufficiently large number of language samples collected in ways that highlight both the production and comprehension aspects of the language system. In addition, such analyses require the use of the latest generation of artificial intelligence models, called transformer-networks. The result will be a new understanding of the PPA syndrome and the language network that it affects. In Aim 1, we will investigate the performance of an unsupervised artificial intelligence model for measuring and classifying language abnormalities in PPA patients. In Aim 2, we will investigate the how these models can be used to measure and classify language abnormalities in AD and FTD patients. In Aim 3, we will evaluate the reliability of these automated measures of language abnormalities in PPA, AD, and FTD. Through a finer-grained analysis of language in people with PPA and other forms of AD or FTLD, it should be possible to develop better understanding of the overlapping and dissociable features of these dementias, possibly leading to improved diagnostic classification and better prognostication.