Purpose:Thyroid-associated ophthalmopathy (TAO) is an autoimmune disease that affects the orbit and is the most prevalent extra-thyroidal complication of Graves’ disease. Previous neuroimaging studies have focused on abnormal static regional activity and functional connectivity in patients with TAO. However, the characteristics of local brain activity over time are poorly understood. This study aimed to investigate alterations in the dynamic amplitude of low-frequency fluctuation (dALFF) in patients with active TAO and to distinguish patients with TAO from healthy controls (HCs) using a support vector machine (SVM) classifier.Methods:A total of 21 patients with TAO and 21 HCs underwent resting-state functional magnetic resonance imaging scans. dALFFs were calculated in conjunction with sliding window approaches to assess dynamic regional brain activity and to compare the groups. Then, we used SVM, a machine learning algorithm, to determine whether dALFF maps may be used as diagnostic indicators for TAO.Results:Compared with HCs, patients with active TAO showed decreased dALFF in the right calcarine, lingual gyrus, superior parietal lobule, and precuneus. The SVM model showed an accuracy of 45.24%–47.62% and area under the curve of 0.35–0.44 in distinguishing TAO from HCs. No correlation was found between clinical variables and regional dALFF.Conclusion:Patients with active TAO showed altered dALFF in the visual cortex and the ventral and dorsal visual pathways, providing further details on the pathogenesis of TAO.