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

Grasemann, Uli; Peñaloza, Claudia; Dekhtyar, Maria; Miikkulainen, Risto; Kıran, Swathi

doi:10.1038/s41598-021-89443-6

Cited by 13 publications

(4 citation statements)

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“…Accurate diagnosis and prognosis are vital for personalized intervention in speech, language, and communication disorders, enhancing the quality of life (50,51). Prognosis involves predicting a patient's trajectory and outcomes (52).…”

Section: Offline Open Brain Ai Applicationsmentioning

confidence: 99%

Open Brain AI: An AI Research Platform

Themistocleous

2024

Linköping Electronic Conference Proceedings

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Language assessment is pivotal in identifying therapeutic interventions for speech, language, and communication disorders stemming from neurogenic origins, developmental or acquired, and student performance in the classroom. Traditional assessment techniques, however, are predominantly manual, necessitating extensive time and effort for administration and scoring. Such procedures can exacerbate the stress experienced by patients. In response to these inherent challenges, we introduced Open Brain AI (https://openbrainai.com). This state-of-the-art computational platform leverages advanced AI methodologies, encompassing machine learning, natural language processing, large language models, and automated speech-to-text transcription. These capabilities enable Open Brain AI to autonomously analyze multilingual spoken and written language productions. This work aims to present the development and evolution of Open Brain AI, elucidating its AI-driven language processing components and the intricate linguistic metrics it employs to evaluate the overarching and granular discourse structures. Open Brain AI significantly reduces the workload on researchers, clinicians, and teachers by facilitating rapid and automated language analysis. It allows healthcare and education professionals to optimize their operational processes, reallocating precious time and resources to more personalized user interactions. Moreover, Open Brain AI provides clinicians, researchers, and educators the autonomy to undertake essential data analytics, freeing up more bandwidth to focus on other vital facets of therapeutic intervention and care.

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Section: Offline Open Brain Ai Applicationsmentioning

confidence: 99%

Open Brain AI: An AI Research Platform

Themistocleous

2024

Linköping Electronic Conference Proceedings

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“…In this way, increased activation can be specific, accounting for the improvements restricted to just the treated language, or it can spread to the untreated language accounting for cross-language generalization effects (Kiran et al, 2013). Considering theoretical models of the bilingual mental lexicon (Kroll & Stewart, 1994) and as recently shown via computational modeling (Grasemann et al, 2021) cross-language generalization could be achieved via associative connections between the semantic and the untreated language lexical systems or via associative connections between the two lexical systems.…”

Section: Treatment Typementioning

confidence: 99%

Multilevel factors predict treatment response following semantic feature-based intervention in bilingual aphasia

Scimeca,

Peñaloza,

Kiran

2023

Bilingualism

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Semantic feature-based treatments (SFTs) are effective rehabilitation strategies for word retrieval deficits in bilinguals with aphasia (BWA). However, few studies have prospectively evaluated the effects of key parameters of these interventions on treatment outcomes. This study examined the influence of intervention-level (i.e., treatment language and treatment sessions), individual-level (baseline naming severity and age), and stimulus-level (i.e., lexical frequency, phonological length, and phonological neighborhood density) factors on naming improvement in a treated and untreated language for 34 Spanish–English BWA who completed 40 hours of SFT. Results revealed significant improvement over time in both languages. In the treated language, individuals who received therapy in their L1 improved more. Additionally, higher pre-treatment naming scores predicted greater response to treatment. Finally, a frequency effect on baseline naming accuracy and phonological effects on accuracy over time were associated with differential treatment gains. These findings indicate that multilevel factors are influential predictors of bilingual treatment outcomes.

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“…All except one study (Grasemann et al, 2021) reported premorbid language background (Table 2). A majority (17/28) of studies used the Language Use Questionnaire (LUQ) but cited two different sources of this questionnaire (Kastenbaum et al, 2018;Kiran et al, 2010).…”

Section: Research Question 1a Assessment Of Premorbid Language Backgr...mentioning

confidence: 99%

A Systematic Review of diagnostic instruments for Spanish-English bilingual speakers with aphasia

Faroqi-Shah

2023

Preprint

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PurposeSpeech-language pathologists report bilingual aphasia as the most challenging condition to assess and treat in neurorehabilitation settings, particularly because of limited access to standardized language assessments. This systematic review seeks to identify standardized assessments used by researchers to document premorbid language history and postmorbid language abilities in Spanish-English speaking bilingual persons with aphasia (SE BPWA), quality of psychometric properties of these language assessments, and the extent to which researchers use psychometric data to interpret scores from these assessments.MethodsA systematic search of PubMed, PsycINFO, Web of Science, and CINAHL databases was conducted in August 2023 for studies that met inclusion criteria (primary data of bilingual Spanish-English speaking adult participants with acute onset aphasia). Data pertaining to language assessments was synthesized in proportions and the identified language assessments were evaluated for psychometric robustness by examining published norms and/or test manuals. Risk of bias was assessed for each of the included studies and the overall certainty of the systematic review was assessed.ResultsTwenty-eight studies describing 262 (non-unique) SE BPWA were included in the systematic review. Information on how aphasia was diagnosed (8/28) and its characterization (17/28) was inconsistently reported. Across studies, premorbid language background was most frequently documented using the Language Use Questionnaire (17/28). Postmorbid language abilities were most commonly assessed using the Bilingual Aphasia Test (BAT, English N =19, Spanish N = 20), although using different versions (for Spanish) and not in its entirety. None of the assessments met all nine COSMIN categories for psychometric quality assessment. Three assessments have (limited) bilingual SE normative data: BAT-short version, Boston Naming Test, and Object and Action Naming Battery. However, no study referenced these norms or other SE norms. Critical appraisal ratings were Low for all the studies, impacting the certainty assessment of the current systematic review.ConclusionsThe limited psychometric quality of currently used language assessments poses increased risk of misdiagnoses for SE BPWA, furthering health disparities. This, coupled with the high risk of bias in individual studies, substantially reduces the generalizability and transparency of existing empirical research for advancing scientific understanding of bilingual aphasia. Researchers and clinicians are recommended to utilize available norms and better document participants’ aphasia characteristics.

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

Cited by 13 publications

References 54 publications

Open Brain AI: An AI Research Platform

Open Brain AI: An AI Research Platform

Multilevel factors predict treatment response following semantic feature-based intervention in bilingual aphasia

A Systematic Review of diagnostic instruments for Spanish-English bilingual speakers with aphasia

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