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

Front Neuroimaging · 2025 · PMC12163030 · PMID 40519564

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closest p · 0.0× alpha

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boldest claim

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highly significantp < 0.001actually significant

For AQ scores, modality effects were highly significant ( H (4) = 43.48, p < 0.001; Figure 3b ), with post-hoc tests revealing significant differences between FA vs. rsFC ( p < 0.001), FA vs.

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