ArticleiScience2026
Multidimensional cortical morphological alterations in COPD using explainable machine learning.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
11 authors.
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Abstract
Cognitive dysfunction is a common extrapulmonary manifestation of chronic obstructive pulmonary disease (COPD). Here, we applied an XGBoost-SHAP machine learning framework to identify cortical morphological features related to cognitive performance in COPD. Pulmonary, cognitive, and structural MRI data from 72 patients with COPD and 68 healthy controls were analyzed. Multidimensional cortical features distinguished COPD from controls, with cortical thickness in bilateral parahippocampal and precentral gyri, and the local gyrification index in the right insula, left middle frontal sulcus, and subcallosal area identified as the most influential features. Notably, right precentral cortical thickness was associated with both pulmonary function (FEV
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