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.copus.gov)，大约有6200万西班牙裔/拉美裔美国人生活在 在美国，其中15%的人年龄在55岁及以上。美国人口普查局还预测，到 到2030年，拉美裔人口将增加到7400万，到那时，老年人的数量将 超过了孩子的数量。另一个单独但令人担忧的统计数据是，世卫组织预测痴呆症的发病率将会上升 (到2030年为7800万人)3和中风(到2030年为7000万幸存者)4表明 迫切需要为这些人群提供临床服务。要做到这一点，有必要了解 双语(说西班牙语和英语的西班牙裔人)和 神经/神经退行性疾病。问题是，说双语的人在效率上差异很大。 他们处理他们的两种语言，以及这些过程可能如何在神经障碍中被破坏。因此，要 充分了解双语障碍(中风)和衰退(痴呆症)的性质，这是必要的 对数百名双语学生进行大规模的横断面和纵向考试 不同熟练程度的人准确地捕捉到双语说话者的差异。 我们的中心假设是双语语言处理的计算模拟(在健康衰老中)， 语言障碍和恢复(在中风中)，和下降(在神经退行性疾病中)是一种强大的 替代这类研究的方法。计算建模使研究成人双语成为可能 该系统可以在任何单个时间点随任何语言组合和熟练程度而变化，其特征是 随着时间的推移而改变。我们的双语语言处理计算模型Bilex已经得到了验证 模拟双语中风后失语症(BPSA)和双语语义性痴呆(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BPSA和BPSA中观察到的不同类型的损害是如何 BSD出现了。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 它们可能使用地图和连接的计算结构来产生(目标1)。配备了这样一台 理解，第二个目标是随着时间的推移扩展它们，即解释这些机制如何导致 中风后恢复(失语症)和下降(痴呆症)(目标2)。那么，第三个目标是理解如何 这些过程通过包括皮层下条件路由与语言选择和控制交互 我们的Bilex模型的机制(目标3)。每个目标都允许逐步进行更详细的描述和 卒中后双语失语和痴呆症下降的建模障碍和恢复的解释。 这些复杂而全面的语言障碍、恢复和语言障碍的机制解释 对患有神经障碍的双语成年人进行控制和干预将为 针对这些人群的治疗。