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

Deepa Tilwani^1,2,3,4*Christian O'Reilly^1,2,3,4Nicholas Riccardi⁵Valerie L Shalin^1,6Dirk B. Den Ouden⁵Julius Fridriksson⁵Svetlana V Shinkareva^3,7Amit Sheth^1,2,4Rutvik H Desai^3,7

• ¹Artificial Intelligence Institute, College of Engineering and Computing, University of South Carolina, Columbia, South Carolina, United States
• ²Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, South Carolina, United States
• ³Institute for Mind and Brain, University of South Carolina, USA, Columbia, SC, United States
• ⁴Carolina Autism and Neurodevelopment Research Center, University of South Carolina, USA, Columbia, SC, United States
• ⁵Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States
• ⁶Department of Psychology, Wright State University, USA, Dayton, OH, United States
• ⁷Department of Psychology, University of South Carolina, USA, Columbia, SC, United States

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.