Objective Functional networks derived from resting-state scalp EEG from people with idiopathic (genetic) generalized epilepsy (IGE) have been shown to have an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we test whether the BNI framework is applicable to resting-state MEG and whether it may achieve higher classification accuracy relative to previous studies using EEG. Methods 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 consider data from 26 people with juvenile myoclonic epilepsy (JME) and 26 healthy controls. Results We find that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e. BNI) than those from healthy controls. We found a classification accuracy of 73%. Conclusions The BNI framework is applicable to MEG and capable of differentiating people with epilepsy from healthy controls. The observed classification accuracy is similar to previously achieved in scalp EEG. Significance The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.

中文翻译：

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静止状态MEG对青少年肌阵挛性癫痫的计算生物标志物

客观功能网络从静息性头皮脑电图来自特发性（遗传性）全身性癫痫（IGE）患者的证据表明，使用健康网络（BNI）的概念评估其功能性癫痫病的固有倾向要高于健康对照组。 ）。在本文中，我们测试BNI框架是否适用于静止状态MEG，以及相对于以前使用EEG的研究，它是否可以实现更高的分类准确性。方法BNI框架包括从看似正常的大脑活动中得出功能网络，将致烟性的数学模型放入网络中，然后计算该网络在计算机上产生癫痫发作的频率。我们考虑来自26名青少年肌阵挛性癫痫（JME）和26名健康对照者的数据。结果我们发现，与健康对照组相比，JME患者的静止状态MEG功能网络具有更高的癫痫发作倾向（即BNI）。我们发现分类准确性为73％。结论BNI框架适用于MEG，能够将癫痫患者与健康对照区分开。观察到的分类准确性类似于先前在头皮脑电图中获得的分类准确性。意义BNI框架可应用于静止状态的MEG，以帮助癫痫诊断。观察到的分类准确性类似于先前在头皮脑电图中获得的分类准确性。重要性BNI框架可应用于静息状态的MEG，以帮助癫痫诊断。观察到的分类准确性类似于先前在头皮脑电图中获得的分类准确性。重要性BNI框架可应用于静息状态的MEG，以帮助癫痫诊断。