Predicting rehabilitation outcomes in bilingual aphasia using computational modeling

使用计算模型预测双语失语症的康复结果

• 批准号: 9304164
• 负责人: Swathi Kiran
• 金额: $ 61.49万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9304164
• 关键词: Address; Age; Aging; Aphasia; Architecture; Area; Behavioral; Cabbage - dietary; California; Celery; Censuses; Chinese People; Climate; Communities; Competence; Complex; Computer Simulation; Custom; Foundations; Funding; Goals; Guidelines; Healthcare; Human; Immigration; Impairment; Individual; Intervention; Language; Lateral; Lesion; Linguistics; Longitudinal Studies; Maps; Massachusetts; Modeling; Multilingualism; Names; National Institute on Deafness and Other Communication Disorders; Nature; Neurologic; Outcome; Output; Patients; Pattern; Performance; Persons; Population; Protocols documentation; Recommendation; Recovery; Rehabilitation Outcome; Rehabilitation Research; Rehabilitation therapy; Research; Semantics; Strategic Planning; Stroke; Structure; Surface; Testing; Texas; Training; Treatment outcome; United States; United States National Institutes of Health; Work; age effect; aphasia rehabilitation; aphasic; base; bilingualism; disability; focal brain damage; health disparity; indexing; innovation; interest; language impairment; language processing; lexical; novel; patient oriented; patient population; post stroke; prevent; simulation; social; stroke rehabilitation; treatment planning; trend

Bilingualism is an exponentially increasing trend in today's world population due to mass immigration and globalization. Nonetheless, there are no guidelines for the optimal rehabilitation for bilingual patients with aphasia, and this poses a great challenge to reducing health disparities. One of NIDCD's Health Disparities 2009-2013 Strategic Research Plan includes a goal to better treat aphasia in bilingual individuals (http://www.nidcd.nih.gov/about/plans/strategic/pages/FY2009-13-HDplan.aspx). The current research on this topic, however, lacks specific recommendations on which languages should be trained in a bilingual aphasic individual and to what extent cross-language transfer occurs subsequent to rehabilitation. Factors contributing to the paucity of research in this area relate to the multitude of possible language combinations in a bilingual individual, the relative competency of the two languages of the bilingual individual and the effect of focal brain damage on bilingual language representation. It is, however, unfeasible to examine these issues without undertaking a large scale longitudinal study in this population. As a potential solution, in our previous work, we developed a computational model to simulate language recovery following rehabilitation in bilingual aphasia. Specifically, we trained, lesioned, and retrained a bilingual computational model in order to systematically characterize the effect of AoA, pre-stroke language proficiency, and post-stroke naming output on the rehabilitation outcomes. Results demonstrated that a computational model including these variables was able to capture the many complex profiles that surface in aphasia both before and after intervention. In addition, it was able to predict the extent of cross-language generalization. However, the work of using this model to fully understand bilingual aphasia rehabilitation has just begun. In the proposed project, we extend this work to now examine the predictive abilities of the model and identify the factors that can maximize cross-language generalization. We also extend the architecture and functionality of this model, which was originally developed to simulate Spanish-English bilingual language processing, to Chinese-English which is another language combination commonly encountered in the US and has a strong theoretical and experimental foundation in computational modeling. The proposed work is innovative, because it uses a computational model to predict optimal rehabilitation protocols to facilitate the greatest amount of language recovery in bilingual aphasia. The successful completion of this project is expected to have an important impact on rehabilitation of stroke and bilingual aphasia as well as on the applications of computational modeling.

由于大规模移民和移民，双语在当今世界人口中呈指数级增长趋势。 全球化。尽管如此，目前还没有关于双语患者最佳康复的指南。 失语症，这对减少健康差距提出了巨大的挑战。 NIDCD 的健康差异之一 2009-2013 战略研究计划包括更好地治疗双语个体失语症的目标 （http://www.nidcd.nih.gov/about/plans/strategic/pages/FY2009-13-HDplan.aspx）。目前这方面的研究 然而，该主题缺乏关于双语失语症患者应该训练哪些语言的具体建议 个体以及康复后跨语言迁移发生的程度。影响因素 该领域研究的缺乏与双语中可能存在的多种语言组合有关 个体、双语个体两种语言的相对能力以及局灶性大脑的影响 双语语言表征受损。然而，如果不考虑这些问题，就不可能研究这些问题。 对该人群进行大规模纵向研究。 作为一个潜在的解决方案，在我们之前的工作中，我们开发了一个计算模型来模拟语言 双语失语症康复后的恢复。具体来说，我们训练、损伤和再训练双语者 计算模型，以便系统地表征 AoA、笔画前语言能力的效果， 以及中风​​后康复结果的命名输出。结果表明，计算 包含这些变量的模型能够捕获失语症中出现的许多复杂特征 干预前和干预后。此外，它还能够预测跨语言泛化的程度。 然而，利用该模型全面理解双语失语症康复的工作才刚刚开始。在 拟议的项目，我们将这项工作扩展到现在检查模型的预测能力并确定 可以最大化跨语言泛化的因素。我们还扩展了架构和功能 该模型最初是为了模拟西班牙语-英语双语处理而开发的 汉英是美国另一种常见的语言组合，具有很强的 计算建模的理论和实验基础。 所提出的工作具有创新性，因为它使用计算模型来预测最佳康复 促进双语失语症患者最大程度语言恢复的协议。顺利完成 该项目预计将对中风和双语失语症的康复产生重要影响 就像计算建模的应用一样。