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

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

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 relati...

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Bibliographic Details
Main Authors: Deepa Tilwani, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Amit P. Sheth, Rutvik H. Desai
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Neuroimaging
Subjects:
aphasia
lesion-symptom mapping
neuroimaging
multivariate analysis
stroke
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816/full
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https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816/full

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