A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.
A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.
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DOI：
10.1016/j.clinph.2020.12.021
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Lopes MA;Krzemiński D;Hamandi K;Singh KD;Masuda N;Terry JR;Zhang J
计算建模与MEG相结合，以区分青少年肌阵挛性癫痫患者与健康对照组。与健康对照组相比，青少年肌阵挛性癫痫患者的脑网络图像发生率（BNI）更高。BNI在我们队列中的分类准确率为73%。对于患有特发性全身性癫痫的人，当使用脑网络诱发性（BNI）的概念进行评估时，来自其静息状态头皮电生理记录的功能网络显示出比健康对照组更高的内在癫痫发作倾向。在此，我们测试了BNI框架是否适用于青少年肌阵挛性癫痫（JME）患者的静息态脑磁图（MEG）。BNI框架包括从明显正常的大脑活动中推导出一个功能网络，将癫痫发作的数学模型放入网络中，然后通过计算机模拟计算该网络产生癫痫发作的频率。我们考虑了26名JME患者和26名健康对照者的数据。我们发现JME患者的静息状态MEG功能网络的特征在于更高的癫痫发作倾向（即，BNI）高于健康对照。我们发现分类准确率为73%。BNI框架适用于MEG，能够区分癫痫患者和健康对照。BNI框架可应用于静息态MEG以辅助癫痫诊断。
Computational modelling is combined with MEG to differentiate people with juvenile myoclonic epilepsy from healthy controls. Brain network ictogenicity (BNI) was found higher in people with juvenile myoclonic epilepsy relative to healthy controls. BNI’s classification accuracy in our cohort was 73%. For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.
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