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.

项目总结