Machine learning analysis reveals abnormal functional network hubs in the primary angle-closure glaucoma patients

Abstract

BackgroundPrimary angle-closure glaucoma (PACG) is a serious and irreversible blinding eye disease. Growing studies demonstrated that PACG patients were accompanied by vision and vision-related brain region changes. However, whether the whole-brain functional network hub changes occur in PACG patients remains unknown.PurposeThe purpose of the study was to investigate the brain function network hub changes in PACG patients using the voxel-wise degree centrality (DC) method.Materials and methodsThirty-one PACG patients (21 male and 10 female) and 31 healthy controls (HCs) (21 male and 10 female) closely matched in age, sex, and education were enrolled in the study. The DC method was applied to investigate the brain function network hub changes in PACG patients. Moreover, the support vector machine (SVM) method was applied to distinguish PACG patients from HC patients.ResultsCompared with HC, PACG patients had significantly higher DC values in the right fusiform, left middle temporal gyrus, and left cerebelum_4_5. Meanwhile, PACG patients had significantly lower DC values in the right calcarine, right postcentral gyrus, left precuneus gyrus, and left postcentral gyrus. Furthermore, the SVM classification reaches a total accuracy of 72.58%, and the ROC curve of the SVM classifier has an AUC value of 0.85 (r = 0.25).ConclusionOur results showed that PACG patients showed widespread brain functional network hub dysfunction relative to the visual network, auditory network, default mode network, and cerebellum network, which might shed new light on the neural mechanism of optic atrophy in PACG patients.