Multidimensional cortical morphological alterations in COPD using explainable machine learning

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 (FEV₁/FVC) and cognition, mediating 29.19% of their relationship. These findings indicate that multidimensional cortical morphology is related to cognitive function in COPD and suggest a structural link within the lung-brain-cognition axis, highlighting the precentral gyrus as a region of interest.