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

项目总结 阿尔茨海默病(AD)和额颞痴呆(FTD)在拉丁裔中非常普遍，最大的和 美国增长最快的少数族裔。然而，由于经济和文化上的不平等，这一群体 面临负担不起标准诊断和监测程序的挑战。此外，对拉丁裔的研究缺乏可伸缩性、 在文化上有效的测试，它很少检查潜在的标记是否在社会生物学特征中是健壮的。 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。参与者为 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来识别疾病或预测神经认知障碍。然而，ASLA 拉丁裔的研究结果微乎其微。而且，大多数ASLA研究都是小规模的，很少有人区分AD 与ASLA和标准测量相比，FTD变体考虑了社会生物因素(例如，性别， 种族、大脑状况、双语能力)或测试跨语言和方言的有效性。 该项目将开发一个新的ASLA框架来共同应对这些挑战。捕捉社会生物学 为了实现多样性并满足强大的机器和深度学习分析的要求，我们将利用2740名参与者。 这些人包括来自五个拉丁美洲国家的讲西班牙语的人(公元700人，公元700人，对照800人)， 说英语的人来自美国(公元140年，公元140FTD，对照160)，以及美国的拉丁裔(公元30年，公元30年，公元40年) 对照)，包括每种疾病的主要变种。这是可能的，因为两国之间的战略合作伙伴关系 加州大学旧金山分校和扩大拉丁美洲痴呆症研究联盟，一个多资金网络，带来了 完全协调的环境和一个庞大的不断增长的队列。全球脑健康研究所，痴呆症培训 加州大学旧金山分校的Hub在所有地点都有专家临床医生。语音和语言数据将通过我们新的 研究栩栩如生的语言的工具包，这是一个符合HIPPA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、临床、认知、 以及健康措施的社会决定因素，以及核磁共振和功能核磁共振。我们的ASLA方法包括TOP 为每个症状预测标记，添加细粒度特征，并嵌入特征。新奇的机器 高维环境下的深度学习算法将用于实现三个目标。 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标记 症状；将它们与认知和成像测量进行比较；并测试它们在西班牙语中的概括性 英语(一种不同类型的语言)。在目标2中，通过线性回归，我们将使用最优ASLA 捕捉认知功能障碍、脑萎缩和连通性的特定症状模式的标记。在AIM 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 概况、方言和双语技能(空、低、高)。我们将打造一种经济实惠、可扩展的方法来帮助 拉美人的AD和FTD诊断，而此时可能会出现治疗疾病的方法。

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.