Department of Computer Science, The University of Texas at Austin, Austin, TX, 78712, USA.
Department of Speech, Language and Hearing Sciences, Boston University, Boston, MA, 02215, USA.
Sci Rep. 2021 May 18;11(1):10497. doi: 10.1038/s41598-021-89443-6.
Predicting language therapy outcomes in bilinguals with aphasia (BWA) remains challenging due to the multiple pre- and poststroke factors that determine the deficits and recovery of their two languages. Computational models that simulate language impairment and treatment outcomes in BWA can help predict therapy response and identify the optimal language for treatment. Here we used the BiLex computational model to simulate the behavioral profile of language deficits and treatment response of a retrospective sample of 13 Spanish-English BWA who received therapy in one of their languages. Specifically, we simulated their prestroke naming ability and poststroke naming impairment in each language, and their treatment response in the treated and the untreated language. BiLex predicted treatment effects accurately and robustly in the treated language and captured different degrees of cross-language generalization in the untreated language in BWA. Our cross-validation approach further demonstrated that BiLex generalizes to predict treatment response for patients whose data were not used in model training. These findings support the potential of BiLex to predict therapy outcomes for BWA and suggest that computational modeling may be helpful to guide individually tailored rehabilitation plans for this population.
由于决定双语失语症患者（BWA）两种语言的缺陷和恢复的多种中风前和中风后因素，预测语言治疗结果仍然具有挑战性。模拟 BWA 语言损伤和治疗结果的计算模型可以帮助预测治疗反应并确定最佳治疗语言。在这里，我们使用 BiLex 计算模型来模拟 13 名接受其中一种语言治疗的西班牙-英语 BWA 的回顾性样本的语言缺陷和治疗反应的行为特征。具体来说，我们模拟了他们中风前的命名能力和中风后的命名障碍，以及他们在治疗和未治疗语言中的治疗反应。BiLex 在治疗语言中准确而稳健地预测了治疗效果，并在 BWA 的未治疗语言中捕捉到了不同程度的跨语言泛化。我们的交叉验证方法进一步表明，BiLex 可以推广到预测未用于模型训练的患者的治疗反应。这些发现支持 BiLex 预测 BWA 治疗结果的潜力，并表明计算模型可能有助于为该人群制定个性化的康复计划。
