Predicting language treatment response in bilingual aphasia using neural network-based patient models
Open accessAbstract
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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Other studies focusing solely on bilingual persons with aphasia have reported conflicting findings regarding the influence of aphasia severity in the treated and untreated languages on treated language improvement and cross-language generalization.
“However, other studies focusing solely on bPWA have reported conflicting findings regarding the influence of aphasia severity in the treated and untreated languages on TLI and CLG7,10,29 underscoring the need for further research to clarify the contribution of these factors to treatment outcomes.”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [29]
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Experimental — accuracy not yet measuredThe BiLex neural network model successfully predicted the development of language skills in the treated language in bilingual aphasia patients.
“The study revealed that the BiLex model was able to successfully predict the development of language skills in the treated language”
From Artificial intelligence in assessment and intervention of speech and language disorders: A literature review (2025) · cites this paper as [33]
The BiLex model had lower prediction accuracy in the untreated language and tended to underestimate cross-linguistic generalization.
“but had lower prediction accuracy in the untreated language and tended to underestimate cross-linguistic generalization [33].”
From Artificial intelligence in assessment and intervention of speech and language disorders: A literature review (2025) · cites this paper as [33]
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“The present study reports on the next step of this approach: the development and cross-validation of BiLex as a model that captures treatment outcomes in the treated and the untreated language in BWA while accounting for factors known to determine post-stroke language impairment and recovery 33 .”
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“…simulated L1 and L2 impairment in BWA by lesioning each patients ’ personalized BiLex model to simulate stroke effects on their bilingual processing system using two alternative methods: removing either a circular portion of map neurons or removing neurons in a two-dimensional Gaussian pattern 19 .”
InfluentialSelf-citationChen, S., Cao, R.-L., & Cheng, S.-J. (2026) · Behavioral Science · doi:10.3390/bs16091486
“…conditions, has been independently reproduced in both the BiLex and active inference frameworks, albeit through different explanatory mechanisms (Grasemann et al., 2021; Sajid et al., 2020); and (c) the mapping between lesion operation and behavioural output is highly sensitive to prior…”
Influential
- Authors
- Grasemann, U., Peñaloza, C., Dekhtyar, M., Miikkulainen, R., & Kiran, S.
- Affiliations
- The University of Texas at Austin (US)
- Boston University (US)
- Corresponding author
- Grasemann, Uli
- Peñaloza, Claudia
- Venue
- Scientific Reports
- Published
- 2021-05-18
- Volume
- 11
- Issue
- 1
- Pages
- 10497-10497
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8131385
- OpenAlex
- W3162572179
- Cited by
- 24
- References
- 56
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 3 · 2021: 1
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Deafness and Other Communication Disorders of the National Institutes of Health (U01DC014922)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- bilingual aphasia, cross-lingual generalization, naming impairment, Spanish-English bilinguals, stroke, language rehabilitation
- MeSH terms
- Adult, Aged, Aphasia, Female, Humans, Male, Middle Aged, Nerve Net, Retrospective Studies, Speech Therapy, Multilingualism, Stroke, Datasets as Topic
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