Ossadtchi A, Baillet S, Mosher J C, Thyerlei D, Sutherling W, Leahy R M
Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089, USA.
Clin Neurophysiol. 2004 Mar;115(3):508-22. doi: 10.1016/j.clinph.2003.10.036.
Magnetoencephalography (MEG) dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most automated techniques are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. This leads to poor sensitivity versus specificity characteristics. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal neuronal current generators within the brain that produce interictal spike activity.
We first use an ICA (independent components analysis) method to decompose the multichannel MEG data and identify those components that exhibit spike-like characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources, and determine the foci of equivalent current dipoles and their associated time courses. We then perform a clustering of the localized dipoles based on distance metrics that takes into consideration both their locations and time courses. The final step of refinement consists of retaining only those clusters that are statistically significant. The average locations and time series from significant clusters comprise the final output of our method.
Data were processed from 4 patients with partial focal epilepsy. In all three subjects for whom surgical resection was performed, clusters were found in the vicinity of the resectioned area.
The presented procedure is promising and likely to be useful to the physician as a more sensitive, automated and objective method to help in the localization of the interictal spike zone of intractable partial seizures. The final output can be visually verified by neurologists in terms of both the location and distribution of the dipole clusters and their associated time series. Due to the clinical relevance and demonstrated promise of this method, further investigation of this approach is warranted.
癫痫棘波的脑磁图（MEG）偶极子定位在癫痫手术中有助于描绘异常皮层范围并确定颅内电极的放置位置。目视分析大量数据会导致疲劳和误差。大多数自动化技术基于发作间期棘波模板匹配或数据的预测滤波，并未明确将源定位作为分析的一部分。这导致敏感性与特异性特征较差。我们描述了一种将时间序列分析与源定位相结合的全自动方法，以检测大脑中产生发作间期棘波活动的局灶性神经元电流发生器簇。
我们首先使用独立成分分析（ICA）方法分解多通道MEG数据，并识别那些呈现棘波样特征的成分。然后从这些检测到的棘波中找到那些在整个阵列上的空间拓扑与局灶性神经源一致的棘波，并确定等效电流偶极子的焦点及其相关的时间进程。然后，我们基于距离度量对定位的偶极子进行聚类，该距离度量同时考虑了它们的位置和时间进程。细化的最后一步包括仅保留那些具有统计学意义的簇。显著簇的平均位置和时间序列构成了我们方法的最终输出。
对4例部分局灶性癫痫患者的数据进行了处理。在所有接受手术切除的3名受试者中，在切除区域附近发现了簇。
所提出的程序很有前景，可能对医生有用，作为一种更敏感、自动化和客观的方法，有助于定位难治性部分性癫痫的发作间期棘波区域。神经科医生可以从偶极子簇的位置和分布及其相关时间序列方面对视检查最终输出。由于该方法具有临床相关性且已显示出前景，因此有必要对该方法进行进一步研究。
