Neuroradiology · 2025
Disrupted static and dynamic Large-scale brain functional network connectivity in the differentiation of myelin oligodendrocyte glycoprotein Antibody-Seropositive from seronegative optic neuritis.
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Wang Wentao, Liu Xilan, Sha Yan, Wang Ximing, Lu Ping
Аннотация
PURPOSE: The ability to distinguish myelin oligodendrocyte glycoprotein antibody-seropositive optic neuritis (MOG-ON) from seronegative-ON is critical in clinical practice. We investigate potential neural mechanisms and differentiation biomarkers via large-scale functional network connectivity (FNC) using resting-state functional magnetic resonance imaging (RS-fMRI).
METHODS: RS-fMRI-based independent component analysis (ICA) was performed in 79 subjects, including 23 with MOG-ON, 30 with seronegative-ON and 26 healthy controls (HCs). The resting-state networks (RSNs) extracted from the ICA were used to investigate static FNC (sFNC) changes within and between groups. In addition, 5 dynamic FNC (dFNC) states were identified using k-means cluster analysis, and several state-related properties were calculated. Receiver operating characteristic (ROC) curve analysis was also performed to determine its value in differential diagnosis.
RESULTS: In the sFNC analysis, the patient groups showed decreased intranetwork functional connectivity (FC) within several RSNs compared to the HC group. The MOG-ON group presented significantly altered intranetwork FC in the medial visual network (mVN) and posterior default mode network (pDMN) compared with the seronegative-ON group. Compared with the HCs, the patient groups also presented abnormal internetwork FC between RSNs. In the dFNC analysis, the patient groups presented altered fractional occupancy and dwell times in states 1 and 5 compared with HCs, and the changes in state-related metrics were also distinct between the MOG-ON and seronegative-ON groups. In terms of ROC curve analysis, optimal diagnostic performance was achieved by combining static and dynamic approaches.
CONCLUSIONS: Abnormal large-scale static and dynamic brain functional networks may help to better understand the neural mechanisms of MOG-ON and seronegative-ON and their differentiation.
METHODS: RS-fMRI-based independent component analysis (ICA) was performed in 79 subjects, including 23 with MOG-ON, 30 with seronegative-ON and 26 healthy controls (HCs). The resting-state networks (RSNs) extracted from the ICA were used to investigate static FNC (sFNC) changes within and between groups. In addition, 5 dynamic FNC (dFNC) states were identified using k-means cluster analysis, and several state-related properties were calculated. Receiver operating characteristic (ROC) curve analysis was also performed to determine its value in differential diagnosis.
RESULTS: In the sFNC analysis, the patient groups showed decreased intranetwork functional connectivity (FC) within several RSNs compared to the HC group. The MOG-ON group presented significantly altered intranetwork FC in the medial visual network (mVN) and posterior default mode network (pDMN) compared with the seronegative-ON group. Compared with the HCs, the patient groups also presented abnormal internetwork FC between RSNs. In the dFNC analysis, the patient groups presented altered fractional occupancy and dwell times in states 1 and 5 compared with HCs, and the changes in state-related metrics were also distinct between the MOG-ON and seronegative-ON groups. In terms of ROC curve analysis, optimal diagnostic performance was achieved by combining static and dynamic approaches.
CONCLUSIONS: Abnormal large-scale static and dynamic brain functional networks may help to better understand the neural mechanisms of MOG-ON and seronegative-ON and their differentiation.