Dynamics of Electrical Activity in Epileptic Brain and Induced Changes Due to Interictal Epileptiform Discharges
Dynamics of Electrical Activity in Epileptic Brain and Induced Changes Due to Interictal Epileptiform Discharges
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DOI：
10.1109/access.2021.3138385
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Ahmed Hossam Mohammed;Ulyana Morar;M. Cabrerizo;H. Rajaei;A. Pinzon;I. Yaylali;M. Adjouadi
中科院分区：
文献类型：
--
作者：
Ahmed Hossam Mohammed;Ulyana Morar;M. Cabrerizo;H. Rajaei;A. Pinzon;I. Yaylali;M. Adjouadi
背景：了解癫痫脑网络的功能连接（FC）模式，因为它们与间歇癫痫样放电（ied）的存在或不存在有关，可以增强识别它们的机器学习（ML）算法。方法：利用头皮脑电图（EEG）数据构建FC图，证明ied引起的脑动力学变化。目的是展示FC地图中存在的独特IED特征如何在训练ML算法中有用，以产生有效的IED检测过程。结果：a)神经学家在IED节段期间预先确定的活跃额颞叶（FT）区域的特征是，与其他FT区域相比，在使用同一患者的非IED （NIED）节段时，平均局部FC比其他FT区域和平均局部FC最高的FT区域显著增加。这种统计显著性是发现的theta， alpha和beta子带。b)将一个区域耦合到另一个区域的远距离连接在IED和NIED段之间也显示出统计学上显著的差异。根据IED形态学的不同，与这些发现相匹配的显著子带因患者而异。因此，虽然theta子带在接收机工作特性曲线（ROC-AUC）下的面积最高，但包括其他子带的特征仍然很重要，因为它们一起会产生更高的ROC-AUC。结论：FC图本质上反映了癫痫大脑动态发生的重大变化。获得的结果为利用FC图作为检测ied的生物标志物提供了更大的信心。
Background: Understanding functional connectivity (FC) patterns of epileptic brain networks as they relate to the presence or absence of interictal epileptiform discharges (IEDs) can enhance machine learning (ML) algorithms identifying them. Methods: Changes in brain dynamics induced by the presence of IEDs are demonstrated by constructing FC maps from scalp electroencephalography (EEG) data. The intent is to demonstrate how unique IED characteristics present in the FC maps could be useful in training ML algorithms to yield an effective IED detection process. Results: a) The active frontal-temporal (FT) region as predetermined by the neurologists during an IED segment is found to be characterized by a statistically significant increase in the average local FC over the other FT region and over FT regions with the highest average local FC when using non-IED (NIED) segments of the same patient. This statistical significance is found for the theta, alpha, and beta sub-bands. b) Distant connections coupling one region to another also show a statistically significant difference between IED and NIED segments. Depending on the IED morphology, the significant sub-band matching those findings differs from patient to patient. Hence, while the theta sub-band results in the highest area under receiver operating characteristic curve (ROC-AUC) among the rest, it is still important to include features of other sub-bands since together they yield even higher ROC-AUC. Conclusions: FC maps intrinsically reflect the significant changes occurring in the dynamics of the epileptic brain. The obtained results provide added confidence in utilizing FC maps as biomarkers for detecting IEDs.