Predicting language treatment response in bilingual aphasia using neural network-based patient models

Abstract 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...

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Autores principales:	Uli Grasemann, Claudia Peñaloza, Maria Dekhtyar, Risto Miikkulainen, Swathi Kiran
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d581e2a474345e08945d1f3d7e77c5e
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spelling	oai:doaj.org-article:7d581e2a474345e08945d1f3d7e77c5e2021-12-02T15:52:25ZPredicting language treatment response in bilingual aphasia using neural network-based patient models10.1038/s41598-021-89443-62045-2322https://doaj.org/article/7d581e2a474345e08945d1f3d7e77c5e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89443-6https://doaj.org/toc/2045-2322Abstract 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.Uli GrasemannClaudia PeñalozaMaria DekhtyarRisto MiikkulainenSwathi KiranNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Uli Grasemann Claudia Peñaloza Maria Dekhtyar Risto Miikkulainen Swathi Kiran Predicting language treatment response in bilingual aphasia using neural network-based patient models
description	Abstract 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.
format	article
author	Uli Grasemann Claudia Peñaloza Maria Dekhtyar Risto Miikkulainen Swathi Kiran
author_facet	Uli Grasemann Claudia Peñaloza Maria Dekhtyar Risto Miikkulainen Swathi Kiran
author_sort	Uli Grasemann
title	Predicting language treatment response in bilingual aphasia using neural network-based patient models
title_short	Predicting language treatment response in bilingual aphasia using neural network-based patient models
title_full	Predicting language treatment response in bilingual aphasia using neural network-based patient models
title_fullStr	Predicting language treatment response in bilingual aphasia using neural network-based patient models
title_full_unstemmed	Predicting language treatment response in bilingual aphasia using neural network-based patient models
title_sort	predicting language treatment response in bilingual aphasia using neural network-based patient models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d581e2a474345e08945d1f3d7e77c5e
work_keys_str_mv	AT uligrasemann predictinglanguagetreatmentresponseinbilingualaphasiausingneuralnetworkbasedpatientmodels AT claudiapenaloza predictinglanguagetreatmentresponseinbilingualaphasiausingneuralnetworkbasedpatientmodels AT mariadekhtyar predictinglanguagetreatmentresponseinbilingualaphasiausingneuralnetworkbasedpatientmodels AT ristomiikkulainen predictinglanguagetreatmentresponseinbilingualaphasiausingneuralnetworkbasedpatientmodels AT swathikiran predictinglanguagetreatmentresponseinbilingualaphasiausingneuralnetworkbasedpatientmodels
_version_	1718385580377636864

Predicting language treatment response in bilingual aphasia using neural network-based patient models

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