Use of machine learning to quantify cognitive function in AD, FTD, and DLB

使用机器学习来量化 AD、FTD 和 DLB 中的认知功能

• 批准号: 10288487
• 负责人: BRADFORD C DICKERSON
• 金额: $ 20.32万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288487
• 关键词: Alzheimer' s Disease; Artificial Intelligence; Biological Markers; Brain Diseases; Characteristics; Classification; Clinical; Cognition; Cognition Disorders; Cognitive; Collection; Computational Linguistics; Dementia; Development; Diagnostic; Disease; Elements; Emotions; Evaluation; Exhibits; Frontotemporal Lobar Degenerations; Generations; Grain; Hand; Impaired cognition; Impairment; Individual; Language; Learning; Lewy Body Dementia; Lewy Body Disease; Linguistics; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measurable; Measurement; Measures; Memory; Methods; Modeling; Monitor; Names; Natural Language Processing; Nerve Degeneration; Outcome; Pathology; Patients; Performance; Persons; Phenotype; Positron-Emission Tomography; Progressive Aphasias; Psychometrics; Research; Retrieval; Sampling; Semantics; Series; Speech; Syndrome; System; Testing; Theoretical model; Time; automated analysis; base; behavioral variant frontotemporal dementia; cerebral atrophy; clinical phenotype; cognitive function; cognitive testing; cohort; executive function; improved; innovation; machine learning method; neural network; neurodegenerative dementia; neuroimaging; novel strategies; patient response; performance tests; phonology; prognostic; syntax; tau Proteins; unsupervised learning

Project Abstract / Summary Cognitive assessment is a key element of the diagnostic evaluation of patients suspected of having early symptomatic stages of neurodegenerative brain diseases, including Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and Lewy Body Disease (LBD). As biomarkers mature, the field is separating clinical syndromes arising from these diseases from the neuropathologic changes themselves, leading to concerns about classification systems for the illnesses these diseases produce. Many tests typically employed in cognitive assessment are verbal, often implemented by an examiner asking the patient a question and the patient answering. These tests are typically scored by hand, with the examiner counting correct or incorrect answers and a simple score being generated against normative scores. Many of the tests still in use were developed 30+ years ago. An exciting array of recent advances in artificial intelligence methods has begun to enable the measurement and classification of language and other cognitive characteristics captured in audio recordings. 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 features measurable in speech samples. Once established, these feature sets can be connected to distributions of cortical atrophy, thus enabling links between specific cognitive abnormalities and underlying neural networks. This approach to the analysis of clinical dementia syndromes can be achieved through a sufficiently large number of cognitive test measures recorded as speech samples. In addition, such analyses require the use of the latest generation of artificial intelligence models, called transformer-networks, to be able to learn the unique ways in which individuals with cognitive impairment or dementia respond to questions requiring memory, executive function, emotion, or language. In Aim 1, we will investigate the validity of an unsupervised artificial intelligence model for measuring cognitive abnormalities in patients with AD, FTLD, or DLB against traditional clinical measures and against MRI measures of regional brain atrophy. In Aim 2, we will investigate the performance of an unsupervised artificial intelligence model for classifying cognitive abnormalities in AD, FTLD, and DLB patients into clusters. In Aim 3, we will evaluate the reliability of these automated measures of cognitive abnormalities in AD, FTLD, and DLB. Through a finer-grained analysis of cognition in people with AD, FTLD, or DLB, it should be possible to develop better understanding of the overlapping and dissociable features of these dementias, aiming for improved diagnostic classification and better monitoring.

项目摘要/摘要 认知评估是对疑似患有糖尿病的患者进行诊断评估的关键要素 神经退行性脑疾病的早期症状阶段，包括阿尔茨海默病（AD）， 额颞叶变性（FTLD）和路易体病（LBD）。随着生物标志物的成熟， 该领域正在将这些疾病引起的临床综合征与神经病理变化分开 这导致了对这些疾病产生的疾病分类系统的关注。 认知评估中通常采用的许多测试都是口头的，通常由一个 检查员问病人一个问题，病人回答。这些测试通常由 手，与考官计数正确或不正确的答案和一个简单的分数正在产生 与标准分数进行对比。许多仍在使用的测试是30多年前开发的。一个令人兴奋 人工智能方法的一系列最新进展已经开始使测量和 音频记录中捕获的语言和其他认知特征的分类。 在这里，我们介绍一种新的方法来实现这两种可能性。最近 自然语言处理（NLP）和机器学习（ML）的发展已经使 可以自动发现和分类语音样本中可测量的特征。一旦 建立，这些特征集可以连接到皮质萎缩的分布，从而使链接 特定认知异常和潜在神经网络之间的联系这种分析方法 的临床痴呆综合征可以通过足够大量的认知测试来实现 作为语音样本记录的测量。此外，这种分析需要使用最新的 一代人工智能模型，称为变压器网络，能够学习独特的 认知障碍或痴呆患者回答问题的方式， 记忆、执行功能、情感或语言。在目标1中，我们将研究 用于测量AD患者认知异常的无监督人工智能模型， FTLD或DLB，与传统临床测量和MRI测量的局部脑萎缩相比较。 在目标2中，我们将研究无监督人工智能模型的性能， 将AD、FTLD和DLB患者的认知异常分类成簇。在目标3中，我们 评估AD、FTLD和DLB中认知异常的这些自动测量的可靠性。 通过对AD、FTLD或DLB患者的认知进行更细粒度的分析， 更好地了解这些痴呆症的重叠和可分离的特征， 用于改进诊断分类和更好的监测。