Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.

Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.

Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.

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

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.

BiLex: A computational approach to the effects of age of acquisition and language exposure on bilingual lexical access.

Lexical access in bilinguals can be modulated by multiple factors in their individual language learning history. We developed the BiLex computational model to examine the effects of L2 age of acquisition, language use and exposure on lexical retrieval in bilingual speakers. Twenty-eight Spanish-English bilinguals and five monolinguals recruited to test and validate the model were evaluated in their picture naming skills in each language and filled out a language use questionnaire.

A Computational Account of Bilingual Aphasia Rehabilitation.

Current research on bilingual aphasia highlights the paucity in recommendations for optimal rehabilitation for bilingual aphasic patients (Roberts & Kiran, 2007; Edmonds & Kiran, 2006). In this paper, we have developed a computational model to simulate an English-Spanish bilingual language system in which language representations can vary by age of acquisition (AoA) and relative proficiency in the two languages to model individual participants. This model is subsequently lesioned by varying connection strengths between the semantic and phonological networks and retrained based on individual patient demographic information to evaluate whether or not the model's prediction of rehabilitation matched the actual treatment outcome.

Using computational patients to evaluate illness mechanisms in schizophrenia.

Background: Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental findings.

Methods: Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding and recall.