Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors.

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 atlas

SUBMITTER: Tilwani D 

PROVIDER: S-EPMC12163030 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature