Benchmarking Machine Learning Models in Lesion-symptom Mapping for Predicting Language Outcomes in Stroke Survivors

Authors

Deepa Tilwani, University of South Carolina
Christian O'Reilly, University of South Carolina
Nicholas Riccardi, University of South Carolina
Valerie L. Shalin, University of South Carolina
Dirk B. den Ouden, Chapman UniversityFollow
Julius Fridriksson, University of South Carolina
Svetlana V. Shinkareva, University of South Carolina
Amit P. Sheth, University of South Carolina
Rutvik H. Desai, University of South Carolina

Document Type

Article

Publication Date

5-29-2025

Abstract

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors.

Comments

This article was originally published in Frontiers in Neuroimaging, volume 4, in 2025. https://doi.org/10.3389/fnimg.2025.1573816

Recommended Citation

Tilwani D, O’Reilly C, Riccardi N, Shalin VL, den Ouden D-B, Fridriksson J, Shinkareva SV, Sheth AP and Desai RH (2025) Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors. Front. Neuroimaging 4:1573816. https://doi.org/10.3389/fnimg.2025.1573816

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.