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

Yue-qian SUN1, Ning WANG1, Shi-hao GE1, Qun WANG1,2,3,4,*()   

  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 Online:2025-12-25 Published:2026-01-08
  • Contact: Qun WANG
  • Supported by:
    Capital Health Research and Development of Special Project(首发2024-1-2041); National Key Research and Development Program of China "Common Disease Prevention and Control Research" Key Project(2022YFC2503800); China Postdoctoral Science Foundation(2024M762180); 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
  • 基金资助:
    首都卫生发展科研专项(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(2022YFC2503800); 中国博士后科学基金资助项目(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