Revealing epilepsy type using a computational analysis of interictal EEG
Revealing epilepsy type using a computational analysis of interictal EEG
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DOI：
10.1038/s41598-019-46633-7
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发表时间：
2019-07-15
影响因子：
4.6
通讯作者：
Terry, John R.
中科院分区：
文献类型：
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作者：
Lopes, Marinho A.;Perani, Suejen;Terry, John R.
根据癫痫发作的临床现象学、EEG记录和MRI，癫痫发作通常可分为局灶性或全身性。当癫痫发作和发作间期癫痫样放电不常见或不一致，且MRI未显示任何明显异常时，这种分类可能具有挑战性。为了应对这一挑战，我们引入了致痫性扩散（IS）的概念，作为与癫痫发作相关的病理性电活动如何在整个大脑网络中传播的预测。这个措施是定义使用一个人特定的计算机表示的功能网络的大脑，构造从发作间期脑电图，结合计算机模型的过渡从背景到神经元样活动的节点内的分布式网络。将这种方法应用于38名癫痫患者（17名患有遗传性全身性癫痫（GGE），21名患有内侧颞叶癫痫（mTLE））的头皮EEG数据集，我们发现患有GGE的人显示出更高的IS与患有mTLE的人相比。我们建议IS作为一个候选的计算生物标志物分类局灶性和全身性癫痫发作间期脑电图。
Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.