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<p>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); <i>t</i> = –0.46; <i>p</i> = 0.65). However, the model-opposite group showed significantly greater cross-language generalization (Difference (SE) = –0.16 (0.07); <i>t</i> = –2.38; <i>p</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Swathi Kiran,
  • Erin Carpenter,
  • Uli Grasemann,
  • Michael Scimeca,
  • Manuel J. Marte,
  • Marissa Russell-Meill,
  • Claudia Peñaloza,
  • Yorghos Tripodis,
  • Risto Miikkulainen

摘要

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.