ArticleScientific reports2021
Predicting language treatment response in bilingual aphasia using neural network-based patient models.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Computational Models for Bilingual Aphasia: A Systematic Review of Language Deficit Research with a Focus on Code-Switching and Translation.Behavioral sciences (Basel, Switzerland) · 2026Review
- Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial.NPJ digital medicine · 2026Article
- Effectiveness of AI-Assisted Digital Therapies for Post-Stroke Aphasia Rehabilitation: A Systematic Review.Brain sciences · 2025Review
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- Cross-language interaction during sequential anomia treatment in three languages: Evidence from a trilingual person with aphasia.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Translanguaging in conversations for people with aphasia living in Greater Johannesburg, South Africa.The South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings · 2025Article
- Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.Stroke · 2025Article
- Disentangling neuroplasticity mechanisms in post-stroke language recovery.Brain and language · 2024Review
- Multilevel factors predict treatment response following semantic feature-based intervention in bilingual aphasia.Bilingualism (Cambridge, England) · 2024Article
- AI-assisted assessment and treatment of aphasia: a review.Frontiers in public health · 2024Review
- Understanding, facilitating and predicting aphasia recovery after rehabilitation.International journal of speech-language pathology · 2022Review
- How the Stroop Effect Arises from Optimal Response Times in Laterally Connected Self-Organizing Maps.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). ConferenceArticle
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Authors and funding
5 authors.
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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.
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