Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

被引:3
|
作者
Ran, Haifeng [1 ]
Chen, Guiqin [1 ]
Ran, Chunyan [1 ]
He, Yulun [1 ]
Xie, Yuxin [1 ]
Yu, Qiane [1 ]
Liu, Junwei [1 ]
Hu, Jie [1 ,2 ]
Zhang, Tijiang [1 ]
机构
[1] Zunyi Med Univ, Affiliated Hosp, Med Imaging Ctr Guizhou Prov, Dept Radiol, Zunyi 563003, Peoples R China
[2] Capital Med Univ, Xuanwu Hosp, Dept Radiol & Nucl Med, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Idiopathic generalized epilepsy; Children; White matter; Functional network; CONNECTIVITY; ABNORMALITIES; IMPAIRMENT; PATHWAYS; SYSTEM; LEVEL;
D O I
10.1016/j.acra.2023.12.043
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Rationale and Objectives: The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T 1-weighted images. Methods: A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation. Results: In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity. Conclusion: Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers. (c) 2024 The Association of University Radiologists. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:2930 / 2941
页数:12
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