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已发表论文
抗 LGI1 抗体脑炎患者脑电图微状态及功能网络的异常改变
Authors Wang L, Jiang P, Wang Y, Sun D , Miao A, Wang X
Received 30 May 2025
Accepted for publication 27 October 2025
Published 12 November 2025 Volume 2025:18 Pages 15875—15886
DOI https://doi.org/10.2147/JIR.S540316
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Adam Bachstetter
Lei Wang,1,* Ping Jiang,1,* Yingfan Wang,1,* Dingfeng Sun,1 Ailiang Miao,1,2 Xiaoshan Wang1
1Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Department of Video-Electroencephalogram, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoshan Wang, Email lidou2005@126.com Ailiang Miao, Email ailiangmiao1986@163.com
Objective: This study aimed to use electroencephalogram (EEG) microstate analysis to characterize transient topographic patterns and rapid brain network reorganization in anti-leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis (anti-LGI1-AE).
Methods: EEG data were collected from fifteen patients with anti-LGI1-AE and eighteen age- and sex-matched controls. K-means clustering was used to extract microstate sequences, and temporal parameters were compared between groups. For microstates showing significant differences, weighted phase lag index matrices were computed across frequency bands, and network-based statistics were applied to identify functional connectivity differences.
Results: The topographic pattern of Microstate A differed significantly between the anti-LGI1-AE group and the control group (p = 0.002). Patients exhibited a significantly higher occurrence of Microstates B and C (p = 0.015 and p = 0.001, respectively). Additionally, the mean global field power of Microstate C was reduced in the patient group (p = 0.007). The transition probability from Microstate A to B was increased in patients (p = 0.013), though this difference did not remain significant after false discovery rate (FDR) correction (pFDR = 0.161). EEG functional network analysis based on microstates with significant differences revealed that, during Microstate B, patients showed a widespread increase in whole-brain functional connectivity in the beta frequency band (all p < 0.001). During Microstate C, enhanced delta-band connectivity was observed with the left occipital region serving as a core hub (p = 0.002). Beta-band connectivity was also increased between the left posterior temporal region, midline structures, and left parietal regions (p = 0.037).
Conclusion: Widespread alterations in functional brain networks are present in anti-LGI1-AE. Changes in microstate temporal parameters and enhanced functional connectivity may reflect compensatory regulatory mechanisms or pathological hyperactivation, revealing functional brain changes that go beyond overt structural damage.
Keywords: anti-LGI1 antibody encephalitis, anti-LGI1-AE, electroencephalography, EEG, microstate, functional brain connectivity, weighted phase lag index, WPLI
1Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Department of Video-Electroencephalogram, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoshan Wang, Email lidou2005@126.com Ailiang Miao, Email ailiangmiao1986@163.com
Objective: This study aimed to use electroencephalogram (EEG) microstate analysis to characterize transient topographic patterns and rapid brain network reorganization in anti-leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis (anti-LGI1-AE).
Methods: EEG data were collected from fifteen patients with anti-LGI1-AE and eighteen age- and sex-matched controls. K-means clustering was used to extract microstate sequences, and temporal parameters were compared between groups. For microstates showing significant differences, weighted phase lag index matrices were computed across frequency bands, and network-based statistics were applied to identify functional connectivity differences.
Results: The topographic pattern of Microstate A differed significantly between the anti-LGI1-AE group and the control group (p = 0.002). Patients exhibited a significantly higher occurrence of Microstates B and C (p = 0.015 and p = 0.001, respectively). Additionally, the mean global field power of Microstate C was reduced in the patient group (p = 0.007). The transition probability from Microstate A to B was increased in patients (p = 0.013), though this difference did not remain significant after false discovery rate (FDR) correction (pFDR = 0.161). EEG functional network analysis based on microstates with significant differences revealed that, during Microstate B, patients showed a widespread increase in whole-brain functional connectivity in the beta frequency band (all p < 0.001). During Microstate C, enhanced delta-band connectivity was observed with the left occipital region serving as a core hub (p = 0.002). Beta-band connectivity was also increased between the left posterior temporal region, midline structures, and left parietal regions (p = 0.037).
Conclusion: Widespread alterations in functional brain networks are present in anti-LGI1-AE. Changes in microstate temporal parameters and enhanced functional connectivity may reflect compensatory regulatory mechanisms or pathological hyperactivation, revealing functional brain changes that go beyond overt structural damage.
Keywords: anti-LGI1 antibody encephalitis, anti-LGI1-AE, electroencephalography, EEG, microstate, functional brain connectivity, weighted phase lag index, WPLI