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

项目摘要 阿尔茨海默病（AD）和额颞叶痴呆症（FTD）在拉丁美洲人中非常普遍，是最大的和最严重的痴呆症。 美国（US）增长最快的少数民族。然而，由于经济和文化上的不平等，这一群体 难以负担标准诊断和监测程序。此外，对拉丁美洲人的研究缺乏可扩展性， 文化上有效的测试，它很少检查潜在的标志物是否在社会生物学概况中是稳健的。 这些问题可以通过低成本的自动语音和语言分析（ASLA）来解决。参与者 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 这些信息可以被数字化提取和分析，以识别疾病或预测神经认知障碍。然而，ASLA 在拉丁美洲人中发现的很少。此外，大多数ASLA研究规模较小，很少有AD之间的差异。 和FTD变体，将ASLA与标准措施进行比较，说明了社会生物学因素（例如，性， 种族，大脑轮廓，双语）或测试跨语言和方言的有效性。 该项目将开发一个新的ASLA框架，以共同应对这些挑战。为了捕捉社会生物学 为了实现强大的机器和深度学习分析，我们将利用2740名参与者。 这些包括来自五个拉丁美洲国家的讲西班牙语的人（700 AD，700 FTD，800对照）， 来自美国的讲英语者（140 AD，140 FTD，160对照）和美国拉丁美洲人（30 AD，30 FTD，40 对照），包括每种疾病的主要变异。这是由于战略伙伴关系， UCSF和扩大拉丁美洲痴呆症研究联盟，一个多资金网络， 充分协调的环境和庞大的、不断增长的群体。全球脑健康研究所，一个痴呆症培训 在UCSF的枢纽，主机在所有网站的专家临床医生。语音和语言数据将通过我们新的 Toolkit to Examine Lifelike Language是一款符合HIPPA标准的语音收集、存储和可视化应用程序， 并辅以语言测试和调查。入选者的特征是人口统计学、临床、认知、 和健康措施的社会决定因素，以及MRI和fMRI。我们的ASLA方法包括 预测每个症状的标记，添加细粒度特征和嵌入特征。新型机器 用于高维设置的深度学习算法将用于追求三个目标。 在目标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标志物 综合征;将其与认知和成像措施进行比较;并测试西班牙语的概括性 英语（一种不同类型的语言）。在目标2中，通过线性回归，我们将使用最佳ASLA 标记物以捕获认知功能障碍、脑萎缩和连通性的综合征特异性模式。在Aim中 3，使用高维机器学习，我们将测试这些标记在不同社会生物学中的有效性 个人资料、方言和双语技能（空、低、高）。我们将制定一个负担得起的、可扩展的方法， 拉丁美洲人的AD和FTD诊断，此时可能出现疾病修饰疗法。

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.