Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial

Bilingual aphasia rehabilitation faces the challenge of determining which language to target in therapy to maximize recovery across both languages. This double-blind randomized controlled trial (48 Spanish-English bilinguals with chronic aphasia; NCT02916524) evaluated whether the BiLex computational model could predict the optimal language for aphasia therapy. Participants received 40 h of semantic feature-based treatment in either the BiLex-recommended language or the opposite language. Both groups showed similar gains in treated-language naming, with no significant difference in proportion of maximal improvement (Difference (SE) = -0.03 (0.07); t = -0.46; p = 0.65). However, the model-opposite group showed significantly greater cross-language generalization (Difference (SE) = -0.16 (0.07); t = -2.38; p = 0.02), though with higher response variability. Further, when the participants were divided into subgroups according to performance, the model-assigned group had a significant advantage in all but the lowest performing subgroups. All these differences were consistent with BiLex model predictions.

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Publication Details

Journal
npj Digital Medicine
Published
2026-04-10
DOI
https://doi.org/10.1038/s41746-026-02583-9
Primary Topic
Neurobiology of Language and Bilingualism
Type
article
Field-Weighted Citation Impact
0.00

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article

Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial

Manuel Jose Marte, Erin Carpenter, Michael Scimeca, Uli Grasemann et al.
npj Digital Medicine
Neurobiology of Language and Bilingualism
article

Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial

Manuel Jose Marte, Erin Carpenter, Michael Scimeca, Uli Grasemann, Claudia Peñaloza, Swathi Kiran, Yorghos Tripodis, Marissa Russell-Meill, Risto Miikkulainen
article en

Abstract

Bilingual aphasia rehabilitation faces the challenge of determining which language to target in therapy to maximize recovery across both languages. This double-blind randomized controlled trial (48 Spanish-English bilinguals with chronic aphasia; NCT02916524) evaluated whether the BiLex computational model could predict the optimal language for aphasia therapy. Participants received 40 h of semantic feature-based treatment in either the BiLex-recommended language or the opposite language. Both groups showed similar gains in treated-language naming, with no significant difference in proportion of maximal improvement (Difference (SE) = -0.03 (0.07); t = -0.46; p = 0.65). However, the model-opposite group showed significantly greater cross-language generalization (Difference (SE) = -0.16 (0.07); t = -2.38; p = 0.02), though with higher response variability. Further, when the participants were divided into subgroups according to performance, the model-assigned group had a significant advantage in all but the lowest performing subgroups. All these differences were consistent with BiLex model predictions.

npj Digital Medicine
Boston University (US), Institut d'Investigació Biomédica de Bellvitge (ES), Universitat de Barcelona (ES), The University of Texas at Austin (US)
National Institutes of Health
Quality Education
Openalex Percentile: Top 26%
Neurobiology of Language and Bilingualism
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