ArticleNeurorehabilitation and neural repair2024
Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach.
Article in Neurorehabilitation and neural repair, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Review
- Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating structural connectivity disruption after stroke: individual tractography or the use of a model-based approach?NeuroImage. Clinical · 2026Article
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- Machine learning models in post-stroke aphasia: a scoping review.Frontiers in neurologyReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients.
objectiveTo establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function.
methodsLanguage function was assessed in 76 stroke patients from the
resultsRandom Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes.
conclusionConnectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.
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