Life Science Research Center, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, China.
Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, 149 Dalian Road, Huichuan District, Zunyi, Guizhou 563003, China.
Epilepsy Res. 2022 Sep;185:106989. doi: 10.1016/j.eplepsyres.2022.106989. Epub 2022 Jul 20.
Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes. The neural basis of BECTS is still poorly understood. This study aimed to further investigate the possible neural mechanisms of BECTS by comparing percent amplitude of fluctuation (PerAF) of resting-state functional magnetic resonance imaging (RS-fMRI) signal of each brain voxel and connectivity within and between related networks in children with BECTS and healthy controls (HCs).
Firstly, we used PerAF method to investigate brain functional alteration and defined the regions of interest (ROIs) where children with BECTS exhibited significant PerAF alterations compared to HCs. We then divided these ROIs into different networks based on previous findings and investigated alterations of functional connectivity within and between networks in children with BECTS. Receiver operating characteristic (ROC) curve was employed to assess the reliable biomarker for distinguishing children with BECTS from HCs based on the intergroup PerAF differences.
Children with BECTS showed decreased PerAF in the left middle frontal cortex (MFC), right precentral gyrus, left precuneus (PCUN), bilateral posterior cingulate cortex (PCC), left angular gyrus, left inferior parietal lobule (IPL), right supplementary motor area (SMA) and left primary somatosensory cortex (S1) compared to HCs. The IPL and PCC exhibited higher classification power by ROC analysis. Moreover, our findings exhibited increased Intra-network connectivity in the default mode network (DMN), and increased inter-network connectivity of the sensorimotor network (SMN) with Broca's area and DMN.
Our study investigated the abnormal PerAF and functional brain networks in children with BECTS, which might provide new insights into the pathological mechanisms of BECTS.
中央颞区棘波良性癫痫(BECTS)是最常见的儿童癫痫综合征之一。BECTS 的神经基础仍知之甚少。本研究旨在通过比较 BECTS 患儿和健康对照(HC)之间各脑区静息态功能磁共振成像(RS-fMRI)信号的波动幅度百分比(PerAF)和相关网络内和网络间的连接,进一步探讨 BECTS 的可能神经机制。
首先,我们使用 PerAF 方法来研究脑功能改变,并定义了与 BECTS 患儿相比,HC 患儿表现出显著 PerAF 改变的感兴趣区(ROI)。然后,我们根据先前的发现将这些 ROI 分为不同的网络,并研究了 BECTS 患儿网络内和网络间的功能连接变化。采用受试者工作特征(ROC)曲线来评估基于组间 PerAF 差异区分 BECTS 患儿和 HC 的可靠生物标志物。
与 HC 相比,BECTS 患儿的左侧中央额回(MFC)、右侧中央前回、左侧楔前叶(PCUN)、双侧后扣带回(PCC)、左侧角回、左侧下顶叶(IPL)、右侧辅助运动区(SMA)和左侧初级体感皮层(S1)的 PerAF 降低。通过 ROC 分析,IPL 和 PCC 表现出更高的分类能力。此外,我们的研究结果还显示,默认模式网络(DMN)内的网络内连接增加,感觉运动网络(SMN)与布罗卡区和 DMN 的网络间连接增加。
本研究探讨了 BECTS 患儿异常的 PerAF 和功能脑网络,这可能为 BECTS 的病理机制提供新的见解。