Purpose: To explore the alterations in both local and remote brain connectivity in patients with thyroid-associated ophthalmopathy (TAO) and to investigate whether the alterations of local neural function could be used to distinguish patients with TAO from healthy controls (HCs) using support vector machine (SVM) classifier.
Materials and Methods: In total, 21 patients with TAO and 21 well-matched HCs were enrolled in our study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We employed regional homogeneity (ReHo) algorithm to evaluate local neural function and selected significantly altered brain regions as seed areas for subsequent study of the remote functional connectivity (FC). Moreover, we chose the observed alterations in the ReHo analysis as classification features to differentiate patients with TAO from HCs through SVM classification method.
Results: Compared with the HCs, TAO patients showed significantly lower ReHo values in the right middle occipital gyrus (MOG) and right angular (ANG). In contrast, TAO patients displayed higher ReHo values in the left hippocampus (Hipp). We further found TAO patients exhibited decreased FC between the left and right Hipp, right MOG and left cerebellum (CER), right ANG and left rectus, right superior temporal pole gyrus (PSTG) (voxel-level p < 0.01, Gaussian random field correction, cluster-level p < 0.05). The alterations in local neural function exhibited an accuracy of 78.57% and area under curve of 0.81 for distinguishing the patients from HCs.
Conclusion: We mainly found the results that patients with TAO showed significantly dysfunctional local and remote brain functional connectivity in several brain regions associated with visual and cognitive functions. The ReHo variability has potential value in differentiating patients with TAO from HCs. These findings may provide novel insights into the neurological mechanisms underlying visual and cognitive disorders in patients with TAO.
Keywords: thyroid-associated ophthalmopathy, functional magnetic resonance imaging, regional homogeneity, functional connectivity, machine learning