An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populations

一种针对拉丁裔和英语人群中阿尔茨海默病和额颞叶痴呆的语言变化的自动化机器学习方法

• 批准号: 10662053
• 负责人: MARIA LUISA GORNO TEMPINI
• 金额: $ 177.31万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662053
• 关键词: Acoustics; Address; Adoption; Affect; Alzheimer' s Disease; Atrophic; Award; Biodiversity; Biological; Biological Factors; Brain; Certification; Classification; Climacteric; Clinical; Cognitive; Cognitive deficits; Collection; Complement; Country; Data; Dementia; Detection; Diagnosis; Diagnostic; Diagnostic tests; Differentiation Antigens; Disease; Early Diagnosis; Education; Ensure; Environment; Equity; Frontotemporal Dementia; Functional Magnetic Resonance Imaging; Funding; Glean; Grain; Grant; Image; Impaired cognition; Inequity; Language; Latin America; Latin American; Latino; Latino Population; Life; Linear Regressions; Linguistics; Machine Learning; Magnetic Resonance Imaging; Manuals; Measures; Methods; Minority; Monitor; Neurocognitive; Outcome; Paper; Participant; Pathology; Pattern; Persons; Population; Prevalence; Primary Progressive Aphasia; Procedures; Protocols documentation; Publications; Race; Reproducibility of Results; Research; Research Personnel; Semantics; Site; Solid; Source; Speech; Surveys; Syndrome; Testing; Time; Training; Underrepresented Populations; United States; Variant; Visualization; behavioral variant frontotemporal dementia; bilingualism; brain health; cerebral atrophy; cohort; cost; deep learning; deep learning algorithm; diagnostic value; digital; ethnoracial; forging; high dimensionality; improved; innovation; machine learning algorithm; minority communities; multimodal data; neural; neural correlate; neuroimaging; neuroprotection; novel; outreach; predictive marker; recruit; sex; skills; social culture; social health determinants; sound; standard measure; targeted treatment; tool; underserved minority

PROJECT SUMMARY Alzheimer's disease (AD) and frontotemporal dementia (FTD) are highly prevalent in Latinos, the largest and fastest-growing minority in the United States (US). Yet, due to financial and cultural inequities, this group is challenged to afford standard diagnostic and monitoring procedures. Also, research on Latinos lacks scalable, culturally valid tests and it rarely examines whether potential markers are robust across socio-biological profiles. Such issues can be tackled with low-cost automated speech and language analyses (ASLA). Participants are asked to produce natural speech, generating multiple acoustic (sound wave) and linguistic (e.g., semantic) data that can be digitally extracted and analyzed to identify diseases or predict neurocognitive disruptions. Yet, ASLA findings are minimal in Latinos. Also, most ASLA studies are small and very few ha differentiated between AD and FTD variants, compared ASLA with standard measures, accounted for socio-biological factors (e.g., sex, race, brain profile, bilingualism) or tested for validity across languages and dialects. This project will develop a novel ASLA framework to jointly address such challenges. To capture socio-biological diversity and meet requisites for robust machine and deep learning analyses, we will leverage 2740 participants. These encompass Spanish speakers from five Latin American countries (700 AD, 700 FTD, 800 controls), English speakers from the US (140 AD, 140 FTD, 160 controls), and US-based Latinos (30 AD, 30 FTD, 40 controls), including the main variants of each disease. This is possible due to a strategic partnership between UCSF and the Consortium to Expand Dementia Research in Latin America, a multi-funded network bringing a fully harmonized environment and a large, growing cohort. The Global Brain Health Institute, a dementia training hub at UCSF, hosts expert clinicians in all sites. Speech and language data will be gleaned through our new Toolkit to Examine Lifelike Language, a HIPPA-compliant app for speech collection, storage, and visualization, supported by a language battery and survey. Enrollees are characterized with demographic, clinical, cognitive, and social determinants of health measures, alongside MRI and fMRI. Our ASLA approach comprises top predicted markers for each syndrome, added fine-grained features, and embedding features. Novel machine and deep learning algorithms for high-dimensional settings will be used to pursue three aims. In Aim 1, we will employ machine and deep learning to reveal the ASLA markers that best identify AD and FTD syndromes; compare them with cognitive and imaging measures; and test them for generalizability from Spanish onto English (a typologically different language). In Aim 2, via linear regressions, we will use optimal ASLA markers to capture syndrome-specific patterns of cognitive dysfunction, brain atrophy, and connectivity. In Aim 3, using high-dimensional machine learning, we will test such markers for validity across diverse socio-biological profiles, dialects, and bilingual skills (null, low, high). We will forge an affordable, scalable approach to assist AD and FTD diagnosis in Latinos, at a time when disease-modifying therapies may emerge.

项目摘要 阿尔茨海默氏病（AD）和额颞痴呆（FTD）在拉丁美洲裔，最大和 美国（美国）增长最快的少数民族。但是，由于财务和文化不平等，该群体是 挑战为提供标准的诊断和监测程序。此外，对拉丁美洲人的研究缺乏可扩展的， 具有文化有效的测试，它很少研究潜在的标志物在社会生物学概况中是否具有鲁棒性。 可以通过低成本的自动语音和语言分析（ASLA）解决此类问题。参与者是 被要求产生自然语音，产生多个声学（声波）和语言（例如语义）数据 可以通过数字提取和分析，以鉴定疾病或预测神经认知破坏。但是，阿斯拉 在拉丁美洲人中的发现很少。同样，大多数ASLA研究很小，很少有AD区分AD 和FTD变体，将ASLA与标准措施进行了比较，涉及社会生物学因素（例如，性别， 种族，大脑概况，双语）或在语言和方言中测试了有效性。 该项目将开发一个新颖的ASLA框架，以共同解决此类挑战。捕获社会生物学 多样性和满足强大的机器和深度学习分析的必要条件，我们将利用2740名参与者。 这些涵盖了来自五个拉丁美洲国家（公元700，700英尺，800个控制）的西班牙语者， 来自美国的英语说话者（140 AD，140 FTD，160个控件）和总部位于美国的拉丁裔（30 AD，30 FTD，40 对照），包括每种疾病的主要变体。由于战略合作伙伴关系，这是可能的 UCSF和在拉丁美洲扩大痴呆症研究的联盟，这是一个由多资助的网络带来的 完全协调的环境和大型，增长的队列。全球大脑健康研究所，痴呆症培训 UCSF的枢纽，主持所有站点的专家临床医生。语音和语言数据将通过我们的新 工具包检查Lifelike语言，这是一种符合河马的应用程序，用于语音收集，存储和可视化， 由语言电池和调查支持。参与者的特征是人口统计学，临床，认知， 以及MRI和FMRI以及健康措施的社会决定因素。我们的ASLA方法包括顶部 预测每个综合征的标记，添加细粒度的特征和嵌入功能。新颖的机器 高维设置的深度学习算法将用于追求三个目标。 在AIM 1中，我们将使用机器和深度学习来揭示最能识别AD和FTD的ASLA标记 综合征；将它们与认知和成像措施进行比较；并测试西班牙语的普遍性 在英语上（一种类型不同的语言）。在AIM 2中，通过线性回归，我们将使用最佳ASLA 捕获认知功能障碍，脑萎缩和连通性的综合征特异性模式的标记。目标 3，使用高维机器学习，我们将测试此类标记的各种社会生物学的有效性 概况，方言和双语技能（无效，低，高）。我们将建立一种负担得起的可扩展方法来协助 在拉丁美洲裔中的AD和FTD诊断，在可能出现疾病改良疗法的时候。