Combination of magnetoencephalographic and clinical features to identify atypical self-limited epilepsy with centrotemporal spikes

IF 2.3 3区医学 Q2 BEHAVIORAL SCIENCES

Epilepsy & Behavior Pub Date : 2024-10-30 DOI:10.1016/j.yebeh.2024.110095

Yihan Li , Yingfan Wang , Fengyuan Xu , Teng Jiang , Xiaoshan Wang

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引用次数: 0

Abstract

Introduction

Our aim was to use magnetoencephalography (MEG) and clinical features to early identify self-limited epilepsy with centrotemporal spikes (SeLECTS) patients who evolve into atypical SeLECTS (AS).

Methods

The baseline clinical and MEG data of 28 AS and 33 typical SeLECTS (TS) patients were collected. Based on the triple-network model, MEG analysis included power spectral density representing spectral power and corrected amplitude envelope correlation representing functional connectivity (FC). Based on the clinical and MEG features of AS patients, the linear support vector machine (SVM) classifier was used to construct the prediction model.

Results

The spectral power transferred from the alpha band to the delta band in the bilateral posterior cingulate cortex, and the inactivation of the beta band in both the right anterior cingulate cortex and left middle frontal gyrus were distinctive features of the AS group. The FC network in the AS group was characterized by attenuated intrinsic FC within the salience network in the alpha band, as well as attenuated FC interactions between the salience network and both the default mode network and central executive network in the beta band. The prediction model that integrated MEG and clinical features had a high prediction efficiency, with an accuracy of 0.80 and an AUC of 0.84.

Conclusion

The triple-network model of early AS patients has band-dependent MEG alterations. These MEG features combined with clinical features can efficiently predict AS at an early stage.

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结合脑磁图和临床特征识别伴有中心颞区棘波的非典型自限性癫痫。

简介：我们的目的是利用脑磁图（MEG）和临床特征来早期识别演变为非典型SeLECTS（AS）的自限性癫痫伴心颞区棘波（SeLECTS）患者：方法：收集了28名AS和33名典型SeLECTS（TS）患者的临床和MEG基线数据。基于三重网络模型，MEG分析包括代表频谱功率的功率谱密度和代表功能连接性（FC）的校正振幅包络相关性。根据 AS 患者的临床和 MEG 特征，使用线性支持向量机（SVM）分类器构建预测模型：结果：双侧扣带回后皮层的频谱功率从α波段转移到δ波段，右侧扣带回前皮层和左侧额中回的β波段失活是AS组的显著特征。强直性脊柱炎组的FC网络特点是：在α波段，显著性网络内部的内在FC减弱；在β波段，显著性网络与默认模式网络和中央执行网络之间的FC相互作用减弱。整合了 MEG 和临床特征的预测模型具有很高的预测效率，准确率为 0.80，AUC 为 0.84：早期强直性脊柱炎患者的三重网络模型具有波段依赖性 MEG 改变。结论：早期强直性脊柱炎患者的三重网络模型具有频带依赖性脑电图改变，这些脑电图特征与临床特征相结合可有效预测早期强直性脊柱炎。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Epilepsy & Behavior 医学-行为科学

CiteScore

5.40

自引率

15.40%

发文量

385

审稿时长

43 days

期刊介绍： Epilepsy & Behavior is the fastest-growing international journal uniquely devoted to the rapid dissemination of the most current information available on the behavioral aspects of seizures and epilepsy. Epilepsy & Behavior presents original peer-reviewed articles based on laboratory and clinical research. Topics are drawn from a variety of fields, including clinical neurology, neurosurgery, neuropsychiatry, neuropsychology, neurophysiology, neuropharmacology, and neuroimaging. From September 2012 Epilepsy & Behavior stopped accepting Case Reports for publication in the journal. From this date authors who submit to Epilepsy & Behavior will be offered a transfer or asked to resubmit their Case Reports to its new sister journal, Epilepsy & Behavior Case Reports.