Computational modeling of language impairment and control in bilingual individuals with post-stroke aphasia and neurodegenerative disorders

中风后失语症和神经退行性疾病双语个体语言障碍和控制的计算模型

• 批准号: 10680656
• 负责人: Swathi Kiran
• 金额: $ 67.99万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-04 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680656
• 关键词: Adult; Age; Aphasia; Area; Behavior; Censuses; Child; Clinical; Clinical Services; Clinical Trials; Computer Models; Computer Simulation; Data; Dementia; Elderly; Exhibits; Familiarity; Foundations; Hispanic; Hispanic Populations; Human; Impairment; Incidence; Individual; Language; Lateral; Latino Population; Linguistics; Maps; Modeling; Names; Nature; Neurodegenerative Disorders; Neurologic; Outcome; Patients; Pattern; Population; Prediction of Response to Therapy; Process; Recovery; Rehabilitation Outcome; Rehabilitation therapy; Route; Semantic Dementias; Semantics; Specificity; Stroke; Structure; Survivors; System; Time; Translations; United States National Institutes of Health; Variant; Work; aged; bilingualism; flexibility; healthy aging; improved; innovation; insight; language impairment; language processing; lexical; lexical retrieval; natural language; nervous system disorder; post stroke; response; semantic processing; statistics; stroke patient; stroke recovery; stroke-induced aphasia

Per the 2020 US Census (data.census.gov), there are approximately 62 million Hispanic/Latino individuals living in the US, and of these 15% are individuals aged 55 years and older 2. The US Census also projects that by 2030, this Hispanic population will increase to 74 million and by this time the number of older adults will outnumber children. A separate but concerning statistic is that the WHO predicts a rising incidence of dementia (78 million individuals by 2030) 3 and strokes (70 million survivors by 2030) 4 suggesting that there is an increased urgency to provide clinical services for these populations. In order to do that, it is necessary to understand the interaction between bilingualism (in Spanish-English speaking Hispanic individuals) and neurological/neurodegenerative disorders. The problem is that bilingual speakers vary widely in how effectively they process their two languages and how these processes may break down in neurological disorders. Thus, to fully understand the nature of bilingual impairment (in stroke) and decline (in dementia), it would be necessary to conduct prohibitively large-scale cross-sectional and longitudinal examinations of hundreds of bilingual individuals with varying degrees of proficiency to accurately capture the variation in bilingual speakers. Our central hypothesis is that computational simulations of bilingual language processing (in healthy aging), language impairment and recovery (in stroke), and decline (in neurodegenerative disorders) is a powerful approach in lieu of such studies. Computational modeling makes it possible to study an adult bilingual language system that can vary by any language combination and proficiency at any single time point and characterize change over time. BILEX, our computational model for bilingual language processing, has an already-proven ability to simulate bilingual post-stroke aphasia (BPSA) and bilingual semantic dementia (BSD) as well as rehabilitation outcomes for post-stroke individuals. Computational simulations can, thus, be used to effectively represent not just known patient cases but also generalize to cases for which we do not yet have any patient data. Consequently, our specific aims are to explain how different types of observed impairments in BPSA and BSD arise. For that reason, we need to first characterize these impairments in more detail, and understand how they may arise using a computational structure of maps and connections (Aim 1). Armed with such an understanding, the second aim is to extend them over time, i.e. to explain how these mechanisms result in recovery after a stroke (in aphasia) and decline (in dementia) (Aim 2). The third aim, then, is to understand how these processes interact with language selection and control, by including subcortical conditional routing mechanisms to our BILEX model (Aim 3). Each aim allows for progressively more detailed characterizations and explanations of modeling impairment and recovery in bilingual post-stroke aphasia and decline in dementia. These sophisticated and comprehensive mechanistic explanations for language impairment, recovery and decline, and control and interference in bilingual adults with neurological disorders will pave the way for treatments for these populations.

根据2020年美国人口普查（data.census.gov），大约有6200万西班牙裔/拉丁裔人生活在美国。 在美国，其中15%是55岁及以上的人2。美国人口普查局还预测， 到2030年，西班牙裔人口将增加到7400万，到那时，老年人的数量将 比孩子多。另一个令人担忧的统计数据是，世界卫生组织预测痴呆症的发病率将上升 (78到2030年将有1000万人）3和中风（到2030年将有7000万幸存者）4，这表明 迫切需要为这些人群提供临床服务。为了做到这一点，有必要了解 双语（西班牙语-英语的西班牙裔个体）与 神经/神经退行性疾病。问题是，双语者在如何有效地 他们处理他们的两种语言，以及这些过程如何在神经系统疾病中被破坏。从而 充分了解双语障碍（中风）和下降（痴呆症）的性质，这将是必要的 对数百名双语者进行大规模的横向和纵向检查， 不同熟练程度的个人准确地捕捉双语说话者的变化。 我们的中心假设是双语语言处理的计算模拟（在健康老龄化中）， 语言障碍和恢复（中风），和下降（神经退行性疾病）是一个强大的 这种方法代替了这种研究。计算建模使研究成人双语语言成为可能 该系统可以在任何单个时间点根据任何语言组合和熟练程度而变化， 随时间而变化。BILEX，我们的双语语言处理计算模型，具有一个已经证明的 模拟双语中风后失语症（BPSA）和双语语义痴呆（BSD）的能力，以及 中风后患者的康复结果。因此，计算模拟可以有效地用于 不仅代表已知的患者病例，而且还概括到我们还没有任何患者的病例 数据因此，我们的具体目标是解释不同类型的观察到的BPSA和 BSD崛起出于这个原因，我们需要首先更详细地描述这些损伤，并了解如何 它们可能是使用映射和连接的计算结构而产生的（目标1）。装备了这样一个 第二个目的是随着时间的推移而扩展它们，即解释这些机制如何导致 中风后的恢复（失语症）和衰退（痴呆症）（目标2）。第三个目标是了解 这些过程与语言选择和控制相互作用，包括皮层下条件路由， 我们的BILEX模型的机制（目标3）。每一个目标都允许逐步更详细的特征描述， 解释中风后双语失语症的建模障碍和恢复以及痴呆的下降。 这些对语言障碍、恢复和 下降，控制和干预双语成人神经系统疾病将铺平道路， 治疗这些人群。