特发性全身性癫痫的结构和功能连接与新皮层代谢的分离:同时PET/MRI多模态研究。

Chenyang Yao, Jie Hu, Bixiao Cui, Jingjuan Wang, Zhen-Ming Wang, Yaqin Hou, Kharel Sudeep, Hongwei Yang, Yihe Wang, Yongzhi Shan, Jie Lu
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引用次数: 0

摘要

背景和目的:特发性全身性癫痫(IGE)约占癫痫病例的20%。IGE以普遍化的尖峰波放电为特征,越来越被认为是一种具有潜在代谢基础的网络疾病。本研究利用PET/MRI同时采集MRI和PET数据的优势,将结构连通性(SC)、功能连通性(FC)和葡萄糖代谢整合到一个统一的框架中。本研究旨在阐明IGE中新皮层的多模态异常,分析这些异常与临床表现的相关性,并探讨不同成像方式之间的相互作用。材料与方法:招募21例IGE患者和34例健康对照(hc)。同时进行PET/MRI扫描,包括DTI、静息状态fMRI和[18F]FDG-PET。DTI生成了新皮层连接蓝图,而静息状态fMRI提供了全脑连接矩阵。[18F]对FDG-PET数据进行处理,得到标准化摄取值比(SUVRs)。多变量距离矩阵回归用于鉴别SC和FC异常的新皮质区域。通过最小绝对收缩和选择算子回归来确定suv的差异。统计分析包括t检验、线性模型、中介分析和Pearson相关性,以比较各组之间每种技术的值,并探讨其与临床特征的关系。结果:SC异常主要发生在边缘(占所有异常新皮质区域的40%)和视觉网络(31%),而FC异常主要发生在默认模式网络(DMN, 45%)。代谢异常主要发生在额顶叶网络(26%)和躯体运动网络(22%)。边缘SC与发病年龄呈正相关,发作频率与DMN FC呈负相关,与额顶代谢呈正相关。中介分析表明,DMN FC介导了边缘SC与额顶叶和躯体运动代谢的关系。结论:多模态方法揭示了IGE中不同且相互关联的异常,不同的模态反映了疾病的各个方面,从而增强了我们对其复杂机制的理解。这种综合分析可以为更有效的治疗提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dissociation of Structural and Functional Connectivity and Metabolism in the Neocortex of Idiopathic Generalized Epilepsy: A Simultaneous PET/MRI Multimodal Study.

Background and purpose: Idiopathic generalized epilepsy (IGE) accounts for approximately 20% of epilepsy cases. Characterized by generalized spike-wave discharge, IGE is increasingly recognized as a network disorder with potential metabolic underpinnings. This study leverages the advantages of simultaneous PET/MRI, which enables the concurrent acquisition of MRI and PET data, to integrate structural connectivity (SC), functional connectivity (FC), and glucose metabolism into a unified framework. This study aims to elucidate the multimodal abnormalities of the neocortex in IGE, to analyze the correlations between these abnormalities and clinical presentations, and to investigate the interactions among different imaging modalities.

Materials and methods: Twenty-one patients with IGE and 34 healthy controls (HCs) were recruited. Simultaneous PET/MRI scans were performed, incorporating DTI, resting-state fMRI, and [18F]FDG-PET. DTI generated a neocortical connectivity blueprint, while resting-state fMRI provided a whole-brain connectivity matrix. [18F]FDG-PET data were processed to obtain standardized uptake value ratios (SUVRs). Multivariate distance matrix regression was used to identify abnormal neocortical regions in SC and FC. Differences in SUVRs were identified by using least absolute shrinkage and selection operator regression. Statistical analyses, including t tests, linear models, mediation analysis, and Pearson correlations, were conducted to compare values of each technique between groups and explore relationships with clinical features.

Results: SC abnormalities were primarily found in the limbic (40% of all abnormal neocortical regions) and visual networks (31%), while FC abnormalities were mostly in the default mode network (DMN, 45%). Metabolic abnormalities were predominantly in the frontoparietal (26%) and somatomotor (22%) networks. SC in the limbic was positively correlated with onset age, while seizure frequency was negative correlated with DMN FC and positively correlated with frontoparietal metabolism. Mediation analysis showed that DMN FC mediated the relationship between limbic SC and frontoparietal and somatomotor metabolism.

Conclusions: A multimodal approach reveals distinct and interrelated abnormalities in IGE, with different modalities reflecting various aspects of the disease, thus enhancing our understanding of its complex mechanisms. This integrative analysis could inform more effective treatments.

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