Department of Neurology, Yale University School of Medicine, New Haven, CT 06520-8018, USA.
Neuroscientist. 2013 Jun;19(3):238-47. doi: 10.1177/1073858412447876. Epub 2012 May 31.
Intracranial electro-encephalography (icEEG) provides a unique opportunity to record directly from the human brain and is clinically important for planning epilepsy surgery. However, traditional visual analysis of icEEG is often challenging. The typical simultaneous display of multiple electrode channels can prevent an in-depth understanding of the spatial-time course of brain activity. In recent decades, advances in the field of neuroimaging have provided powerful new tools for the analysis and display of signals in the brain. These methods can now be applied to icEEG to map electrical signal information onto a three-dimensional rendering of a patient's cortex and graphically observe the changes in voltage over time. This approach provides rapid visualization of seizures and normal activity propagating over the brain surface and can also illustrate subtle changes that might be missed by traditional icEEG analysis. In addition, the direct mapping of signal information onto accurate anatomical structures can assist in the precise targeting of sites for epilepsy surgery and help correlate electrical activity with behavior. Bringing icEEG data into a standardized anatomical space will also enable neuroimaging methods of statistical analysis to be applied. As new technologies lead to a dramatic increase in the rate of data acquisition, these novel visualization and analysis techniques will play an important role in processing the valuable information obtained through icEEG.
颅内脑电图(icEEG)提供了直接记录人类大脑的独特机会,对规划癫痫手术具有重要的临床意义。然而,icEEG 的传统视觉分析通常具有挑战性。多个电极通道的典型同步显示可能会妨碍对大脑活动的时空过程的深入理解。近几十年来,神经影像学领域的进步为大脑信号的分析和显示提供了强大的新工具。这些方法现在可以应用于 icEEG,将电信号信息映射到患者皮质的三维渲染图上,并直观地观察电压随时间的变化。这种方法可以快速可视化脑表面传播的癫痫发作和正常活动,还可以说明传统 icEEG 分析可能会错过的细微变化。此外,将信号信息直接映射到准确的解剖结构上,可以帮助精确地确定癫痫手术的靶点,并有助于将电活动与行为相关联。将 icEEG 数据纳入标准化的解剖空间还将使神经影像学的统计分析方法得以应用。随着新技术导致数据采集速度的急剧增加,这些新颖的可视化和分析技术将在处理通过 icEEG 获得的有价值信息方面发挥重要作用。