van Mierlo Pieter, Papadopoulou Margarita, Carrette Evelien, Boon Paul, Vandenberghe Stefaan, Vonck Kristl, Marinazzo Daniele
Medical Imaging and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - iMinds Medical IT Department, Ghent, Belgium.
Department of Data Analysis, Faculty of Psychology and Pedagogical Sciences, Ghent University, Ghent, Belgium.
Prog Neurobiol. 2014 Oct;121:19-35. doi: 10.1016/j.pneurobio.2014.06.004. Epub 2014 Jul 8.
Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity analysis is stressed. Despite the fact that many studies have showed promising results, we must conclude that functional connectivity analysis has not made its way into clinical practice yet.
如今，神经成像技术经常被用于研究网络中功能特化的脑区的整合。功能连接量化了同时记录的信号动态之间的统计依赖性，能够推断分离脑区的动态相互作用。在这篇综述中，我们讨论了从颅内和头皮脑电图（EEG）记录中获得的功能连接模式如何揭示癫痫性脑动态的信息，以及如何用于预测即将发生的癫痫发作和定位癫痫发作起始区。强调了使用功能连接分析提取脑电图数据中不可见的可识别信息的附加价值。尽管许多研究已显示出有前景的结果，但我们必须得出结论，功能连接分析尚未进入临床实践。
