ArticleFrontiers in neuroscience2025
Aberrant static and dynamic brain functional topological organization in the differentiation of myelin oligodendrocyte glycoprotein antibody-seropositive optic neuritis from seronegative optic neuritis.
Article in Frontiers in neuroscience, 2025. 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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Abstract
Objective: An early and accurate diagnosis of myelin oligodendrocyte glycoprotein antibody seropositive optic neuritis (MOG-ON) versus seronegative-ON is critical for optimal management. We aimed to explore alterations in static and dynamic functional networks for differentiation by resting-state functional magnetic resonance imaging (RS-fMRI) with the graph theory method. Methods: RS-fMRI was performed on 53 patients (23 with MOG-ON and 30 with seronegative-ON) and 26 healthy controls (HCs). Graph theory analysis was used to investigate the topological properties of the functional networks. Receiver operating characteristic (ROC) curve analysis was also performed to determine their effectiveness in differential diagnosis. Results: With respect to static properties, the MOG-ON and seronegative-ON groups presented a spectrum of abnormalities in global and nodal properties compared with the HC group. Furthermore, compared with the seronegative-ON group, the MOG-ON group also presented with abnormal properties mostly located in the visual network (VN). With respect to dynamic properties, the MOG-ON and seronegative-ON groups presented with greater variances of global and nodal properties compared with the HC group. Importantly, the variances in several global and nodal properties were greater in the MOG-ON group. Compared with that in HCs, the subnetwork (24 nodes and 28 edges) in the MOG-ON patients was enhanced. For ROC analysis, the optimal diagnostic performance was obtained by combining static and dynamic approaches. Conclusion: In conclusion, abnormal topological organization of static and dynamic brain functional networks may help explore the neural mechanisms of ON in different phenotypes and serve as biomarkers for differentiation.
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