ArticleBrain communications2025
The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value.
Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.NPJ digital medicine · 2026Article
- Cortical hierarchy and aphasia severity: the sensorimotor-association gradient modulates lesion effects within language-specific networks.Communications biology · 2026Article
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Authors and funding
4 authors.
Funding
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Abstract
Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation. Second, we mapped the topographic and connectomic lesion impact on both clinical measures. We retrospectively included 685 patients [age 67.4 ± 15.1, National Institutes of Health Stroke Scale 24 h median(IQR) = 3(1; 6), modified Rankin Scale 3 months = 1(0; 2), National Institutes of Health Stroke Scale 3 months = 0(0; 2)].
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