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

PPA、AD 和 FTD 中语音样本的计算心理语言学分析

• 批准号: 10563169
• 负责人: BRADFORD C DICKERSON
• 金额: $ 23.93万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-04 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10563169
• 关键词: Affect; Agrammatism; Alzheimer' s Disease; Alzheimer' s disease pathology; Artificial Intelligence; Assessment tool; Atrophic; Behavior; Behavioral; Brain region; Categories; Characteristics; Classification; Classification Scheme; Clinical; Clinical assessments; Cognition; Cognitive; Comprehension; Computational Linguistics; Data; Deformity; Dementia; Development; Diagnostic; Dimensions; Disease; Exhibits; Frontotemporal Lobar Degenerations; Functional disorder; Generations; Grain; Impairment; Individual; Judgment; Language; Linguistics; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Modeling; Monitor; Motor; Names; Natural Language Processing; Nerve Degeneration; Outcome; Pathology; Patients; Pattern; Performance; Persons; Phenotype; Positron-Emission Tomography; Primary Progressive Aphasia; Production; Progressive Nonfluent Aphasias; Progressive Supranuclear Palsy; Psycholinguistics; Psychometrics; Research; Retrieval; Sampling; Semantics; Speech; Symptoms; Syndrome; System; Theoretical model; Time; Training; Variant; artificial intelligence method; automated analysis; behavioral variant frontotemporal dementia; cerebral atrophy; clinical phenotype; cognitive process; cohort; diagnostic criteria; experience; improved; innovation; lexical retrieval; machine learning method; neural network; neurodegenerative dementia; neuroimaging; neuropathology; novel strategies; phonology; prognostication; protein TDP-43; tau Proteins; unsupervised learning

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.

抽象的 主要进行性失语症（PPA）是一种以异常为特征的临床神经退行性综合征 在语言中，最初相对保留其他认知过程。该综合征可能是由几个 神经病理学的种类，包括阿尔茨海默氏病（AD）或额颞叶变性（FTLD）。 不同的神经病理学原因与该疾病的特定变异有关。有 PPA（NFVPPA）的非浮力变体倾向于表现出努力的言语和农业性，在某些情况下 语音功能障碍。句子重复和词汇检索中的障碍有障碍 PPA（LVPPA）的徽标变体。对象命名和单词理解的困难在 具有PPA（SVPPA）语义变体的个体。虽然广泛使用，但当前的分类系统是 受到语言行为重叠的个人的挑战 语言特征和皮质萎缩模式的不一致。另外，其中一些相同 在AD或FTLD的非PPA临床表型患者中可以看到语言或解剖异常。 也就是说，这些PPA亚型可能代表一种对认知多维频谱进行分类的一种方法 由一组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。我们在这里 引入一种新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）现已使自动发现成为可能 和语言特征的分类。建立后，这些功能集可以连接到分布 皮质萎缩，从而在特定的语言行为异常和基础之间建立联系 神经网络。这种分析PPA子类型的方法及其与其他的上下文化 可以通过足够多的语言样本来实现AD和FTLD的临床类型 以强调语言系统的生产和理解方面的方式收集。在 此外，此类分析需要使用最新一代人工智能模型，称为 变压器网络。结果将是对PPA综合征和语言网络的新理解 它会影响。在AIM 1中，我们将研究一个无监督的人工智能模型的性能 测量和分类PPA患者的语言异常。在AIM 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AIM 3中，我们将 评估PPA，AD和FTD中这些自动化语言异常措施的可靠性。通过 PPA和其他形式的AD或FTLD的人的语言分析，应该有可能 为了更好地理解这些痴呆症的重叠和可分离特征，可能 导致改进的诊断分类和更好的预后。