Chinese Journal of Contemporary Neurology and Neurosurgery ›› 2025, Vol. 25 ›› Issue (12): 1113-1120. doi: 10.3969/j.issn.1672-6731.2025.12.004

• Neuroimaging • Previous Articles     Next Articles

Analysis of 18F-FDG PET metabolic characteristics in anti-glutamate decarboxylase 65 antibody-associated encephalitis based on cluster analysis

SUN Yue-qian1, WANG Ning1, GE Shi-hao1, WANG Qun1,2,3,4   

  1. 1 Center of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China;
    2 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450046, He'nan, China;
    3 China National Clinical Research Center for Neurological Diseases, Beijing 100070, China;
    4 Beijing Institute of Brain Disorders, Beijing 100069, China
  • Received:2025-10-10 Published:2026-01-08
  • Supported by:
    This study was supported by Capital Health Research and Development of Special Project (No. 2024- 1-2041), National Key Research and Development Program of China "Common Disease Prevention and Control Research" Key Project (No. 2022YFC2503800), China Postdoctoral Science Foundation (No. 2024M762180), and Beijing Postdoctoral Research Foundation.

基于聚类分析的抗谷氨酸脱羧酶65抗体相关脑炎18F-FDG PET代谢特征分析

孙越乾1, 王宁1, 葛世豪1, 王群1,2,3,4   

  1. 1 100070 首都医科大学附属北京天坛医院神经病学中心;
    2 450046 郑州大学第一附属医院神经内科;
    3 100070 北京, 国家神经系统疾病临床医学研究中心;
    4 100069 北京脑重大疾病研究院
  • 通讯作者: 王群,Email:wangq@ccmu.edu.cn
  • 基金资助:
    首都卫生发展科研专项(项目编号:首发2024-1-2041);国家重点研发计划“常见多发病防治研究”重点专项(项目编号:2022YFC2503800);中国博士后科学基金资助项目(项目编号:2024M762180);北京市博士后科学基金资助项目

Abstract: Objective To investigate the metabolic patterns of anti-glutamate decarboxylase 65 (GAD65) antibody-associated encephalitis using 18F-FDG PET, and to analyze the correlations between these patterns and clinical or neuroimaging features. Methods A total of 25 patients with anti-GAD65 antibody-associated encephalitis admitted to Beijing Tiantan Hospital, Capital Medical University, from January 2018 to July 2024 were enrolled. All patients underwent 18F-FDG PET imaging. The whole brain of each patient was segmented into 116 brain regions, and cerebellum-normalized standard uptake value ratio (SUVRc) was calculated for each region. Principal component analysis (PCA) was used to reduce the dimensionality of SUVRc, and k-means clustering algorithm was applied to divide the patients into 2 clusters. Pearson and partial correlation analyses were performed to explore the correlations between metabolic activity of key brain regions and clinical indicators or MRI volumes. Results PCA showed that the top 5 brain regions contributing most to Principal Component 1 were all located in the occipital lobe, in order of left cuneus, right cuneus, right lingual gyrus, left inferior occipital gyrus and right inferior occipital gyrus. The k-means clustering algorithm divided the patients into 2 clusters (14 cases in Cluster 1, 11 cases in Cluster 2). There were statistically significant differences in SUVRc of the left cuneus (F = 7.946, P = 0.000), right cuneus (F = 8.406, P = 0.000), right lingual gyrus (F = 9.447, P = 0.000), left inferior occipital gyrus (F = 17.036, P = 0.000), and right inferior occipital gyrus (F = 18.312, P = 0.000) between the 2 clusters and healthy controls. Further pairwise comparisons revealed that compared with the health controls, Cluster 2 had increased SUVRc in the left cuneus (t = 3.809, P = 0.000), right cuneus (t = 2.959, P = 0.000), right lingual gyrus (t = 3.726, P = 0.000), left inferior occipital gyrus (t = 3.948, P = 0.000), and right inferior occipital gyrus (t = 3.846, P = 0.000), so it was defined as the occipital lobe hypermetabolism cluster; Cluster 1 showed a decreasing trend in metabolic activity of these regions, thus defined as the non-occipital lobe hypermetabolism cluster. MRI volume analysis revealed that the brain regions with significant volume differences between the non-occipital lobe hypermetabolism cluster and occipital lobe hypermetabolism cluster were the cuneus (t = -2.214, P = 0.038), superior occipital gyrus (t = -2.213, P = 0.038), orbital gyrus (t = -2.829, P = 0.010), lateral part of the superior temporal gyrus (t = -2.246, P = 0.035), occipito-temporal medial sulcus (collateral sulcus), and lingual gyrus sulcus (t = -2.160, P = 0.042). Conclusions Different metabolic patterns of anti-GAD65 antibody-associated encephalitis were identified by 18F-FDG PET combined with cluster analysis, and their associations with clinical and neuroimaging features were revealed, which deepens the understanding of the pathophysiological mechanisms of this disease.

Key words: Encephalitis, Autoimmune diseases, Glutamate decarboxylase, Positron-emission tomography, Cluster analysis

摘要: 目的 基于18F-FDG PET探讨抗谷氨酸脱羧酶65(GAD65)抗体相关脑炎的代谢模式,并分析其与临床和神经影像学特征的相关性。方法 纳入2018年1月至2024年7月首都医科大学附属北京天坛医院收治的25例抗GAD65抗体相关脑炎患者,均行18F-FDG PET显像,将每例患者全脑分割为116个脑区,计算每个脑区的小脑标准化摄取值比值(SUVRc);采用主成分分析对SUVRc降维,k均值聚类算法将抗GAD65抗体相关脑炎患者分为2个集群,Pearson相关分析和偏相关分析探讨关键脑区代谢活性与临床和MRI体积的相关性。结果 主成分分析显示,主成分1贡献最大的5个脑区均位于枕叶,依次为左侧楔叶、右侧楔叶、右侧舌回、左侧枕下回和右侧枕下回。k均值聚类算法将抗GAD65抗体相关脑炎患者分为2个集群(集群1为14例、集群2为11例)。抗GAD65抗体相关脑炎患者2个集群与健康对照者左侧楔叶(F=7.946,P=0.000)、右侧楔叶(F=8.406,P=0.000)、右侧舌回(F=9.447,P=0.000)、左侧枕下回(F=17.036,P=0.000)和右侧枕下回(F=18.312,P=0.000)SUVRc均有统计学意义,进一步两两比较,与对照组相比,集群2左侧楔叶(t=3.809,P=0.000)、右侧楔叶(t=2.959,P=0.000)、右侧舌回(t=3.726,P=0.000)、左侧枕下回(t=3.948,P=0.000)和右侧枕下回(t=3.846,P=0.000)SUVRc增加,称为枕叶高代谢集群;集群1左侧楔叶、右侧楔叶、右侧舌回、左侧枕下回和右侧枕下回代谢活性有降低趋势,称为非枕叶高代谢集群。MRI体积分析显示,非枕叶高代谢集群与枕叶高代谢集群差异脑区为楔叶(t=- 2.214,P=0.038)、枕上回(t=- 2.213,P=0.038)、眶回(t=- 2.829,P=0.010)、颞上回外侧部(t=- 2.246,P=0.035)、枕颞内侧沟(侧副沟)和舌回沟(t=- 2.160,P=0.042)。结论 通过18F-FDG PET结合聚类分析识别出抗GAD65抗体相关脑炎的不同代谢模式,并揭示其与临床和神经影像学特征的关联性,加深对其病理生理学机制的理解。

关键词: 脑炎, 自身免疫疾病, 谷氨酸脱羧酶, 正电子发射断层显像术, 聚类分析