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 全球化。尽管如此，对于双语患者的最佳康复指南 失语症，这对减少健康差异构成了巨大的挑战。 NIDCD的健康差异之一 2009-2013战略研究计划包括一个更好地治疗双语个人失语症的目标 （http://www.nidcd.nih.gov/about/plans/strategic/pages/fy2009-13-hdplan.aspx）。当前的研究 但是，主题缺乏针对双语失语的培训哪些语言的具体建议 在康复之后发生的个人和跨语言转移的程度。促成因素 在这一领域的研究很少，与双语中的多种可能的语言组合有关 个人，双语个人两种语言的相对能力以及焦点大脑的影响 双语语言表示的损害。但是，在没有的情况下检查这些问题是不可行的 在该人群中进行大规模的纵向研究。 作为潜在解决方案，在我们以前的工作中，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，病变并重新训练了双语 计算模型为了系统地表征AOA的影响，中风前语言水平， 以及恢复结果的中风后命名输出。结果表明计算 包括这些变量在内的模型能够捕获失语症中浮出水面的许多复杂轮廓 干预前后。此外，它能够预测跨语言概括的程度。 但是，使用该模型充分了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 可以最大化跨语言概括的因素。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 中文英语是美国通常遇到的另一种语言组合 计算建模的理论和实验基础。 拟议的工作具有创新性，因为它使用计算模型来预测最佳康复 促进双语失语症中最大语言恢复的协议。成功完成 预计该项目也会对中风和双语失语症的康复产生重要影响 如计算建模的应用。