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Title: Predicting language treatment response in bilingual aphasia using neural network-based patient models
Author: Grasemann, Uli
Peñaloza, Claudia
Dekhtyar, Maria
Miikkulainen, Risto
Kiran, Swathi
Keywords: Afàsia
Trastorns del llenguatge
Xarxes neuronals (Informàtica)
Language disorders
Speech therapy
Neural networks (Computer science)
Issue Date: 18-May-2021
Publisher: Nature Publishing Group
Abstract: Predicting language therapy outcomes in bilinguals with aphasia (BWA) remains challenging due to the multiple pre- and poststroke factors that determine the defcits 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 profle of language defcits and treatment response of a retrospective sample of 13 Spanish-English BWA who received therapy in one of their languages. Specifcally, 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 efects accurately and robustly in the treated language and captured diferent 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 fndings 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.
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It is part of: Scientific Reports, 2021, vol. 11, p. 10497
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ISSN: 2045-2322
Appears in Collections:Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)

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