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.

双语是当今世界人口的指数增长趋势，这是由于大规模移民和 全球化尽管如此，目前还没有双语患者的最佳康复指南， 失语症，这对减少健康差距构成了巨大挑战。NIDCD的健康差距之一 2009-2013年战略研究计划包括一个目标，以更好地治疗失语症的双语个人 (http：//www.nidcd.nih.gov/about/plans/strategic/pages/FY2009-13-HDplan.aspx）。目前对此的研究 然而，该主题缺乏关于双语失语症患者应接受哪些语言训练的具体建议。 个人和在何种程度上跨语言迁移发生后康复。促成因素 这一领域研究的缺乏与双语语言中可能的语言组合的多样性有关。 双语个体的两种语言的相对能力和局灶脑效应 对双语表达的损害。然而，审查这些问题， 在这一人群中进行大规模的纵向研究。 作为一个潜在的解决方案，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，损伤，再训练一个双语者， 计算模型，以系统地表征AoA，中风前语言能力， 和卒中后命名输出对康复结果的影响。结果表明，计算 包括这些变量的模型能够捕捉到失语症中出现的许多复杂的特征， 干预前后。此外，它能够预测跨语言泛化的程度。 然而，利用这一模式充分了解双语失语症康复的工作才刚刚开始。在 建议的项目，我们扩展这项工作，现在检查模型的预测能力，并确定 这些因素可以最大限度地提高跨语言的泛化能力。我们还扩展了 该模型最初是为了模拟西班牙语-英语双语语言处理而开发的， 中英双语是另一种在美国常见的语言组合， 计算建模的理论和实验基础。 这项工作是创新的，因为它使用了一个计算模型来预测最佳康复 协议，以促进最大量的语言恢复双语失语症。圆满完成 本项目的研究成果对脑卒中和双语失语症的康复具有重要意义 就像计算机建模的应用一样。