刺激诱导的光谱反应的网络刺激性有助于定位发作的发作区域
Mark A Hays1, Amir H Daraie1, Rachel J Smith2
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
概括
对皮质皮质谱反应 (CCSR) 的网络分析有效预测患者的发作区域 (SOZ). 这种方法可以识别关键的手术点,并改善位,从而减少对iEEG监测的需要.
科学领域:
- 神经科学是一个神经科学.
- 发病学 (Epileptology) 是一个专业的学科.
- 计算神经科学是一种神经科学.
背景情况:
- 内EEG (iEEG) 监测对于定位发作发作区 (SOZ) 至关重要.
- 单脉冲电刺激 (SPES) 和诱导的高频活动显示了SOZ识别的潜力.
- 皮层-皮层光谱反应 (CCSRs) 在发性网络中提供了一种新的激发性指数.
研究的目的:
- 通过诱导皮质皮质谱反应 (CCSRs) 预测发作区域 (SOZ) 位置.
- 利用CCSR作为发性网络中的兴奋度指数.
- 评估CCSR网络分析对手术目标识别的有用性.
主要方法:
- 在接受iEEG监测的27名患者中进行了SPES.
- 通过测量功率的早期增加 (10-200毫秒,10-250赫兹) 来量化CCSR.
- 应用图表中心性测量到CCSR网络连接强度来预测SOZ站点.
主要成果:
- 较大的CCSR中心性显著预测了SOZ地点 (AUC=0.85) 和手术目标 (AUC=0.91).
- 预测和目标SOZ位置之间的对齐预测了手术结果 (AUC=0.79).
- 在CCSR网络分析中,成功地发现了SOZ网站中兴奋度的增加.
结论:
- 对CCSR的网络分析可以确定SOZ站点的兴奋度增加.
- CCSR可以在SOZ中区分重要的外科位.
- CCSR可以补充传统的iEEG监测,以定位发作,从而减少记录多次发作的需要.
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