Wentao Wang, Xilan Liu, Yan Sha, Ximing Wang, Ping Lu
{"title":"髓鞘少突胶质细胞糖蛋白抗体血清阳性与血清阴性视神经炎分化过程中静态和动态大范围脑功能网络连接中断。","authors":"Wentao Wang, Xilan Liu, Yan Sha, Ximing Wang, Ping Lu","doi":"10.1007/s00234-025-03643-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":"2107-2119"},"PeriodicalIF":2.6000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disrupted static and dynamic Large-scale brain functional network connectivity in the differentiation of myelin oligodendrocyte glycoprotein Antibody-Seropositive from seronegative optic neuritis.\",\"authors\":\"Wentao Wang, Xilan Liu, Yan Sha, Ximing Wang, Ping Lu\",\"doi\":\"10.1007/s00234-025-03643-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":19422,\"journal\":{\"name\":\"Neuroradiology\",\"volume\":\" \",\"pages\":\"2107-2119\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroradiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00234-025-03643-9\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroradiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00234-025-03643-9","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/20 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Disrupted static and dynamic Large-scale brain functional network connectivity in the differentiation of myelin oligodendrocyte glycoprotein Antibody-Seropositive from seronegative optic neuritis.
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.
期刊介绍：
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.
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