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癫痫儿童在视觉工作记忆任务中进行脑电图源定位分析。

Electroencephalography source localization analysis in epileptic children during a visual working-memory task.

机构信息

Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy.

School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece.

出版信息

Int J Numer Method Biomed Eng. 2020 Dec;36(12):e3404. doi: 10.1002/cnm.3404. Epub 2020 Oct 23.

Abstract

We localize the sources of brain activity of children with epilepsy based on electroencephalograph (EEG) recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standardized low resolution brain electromagnetic tomography (sLORETA), the weighted minimum norm estimation (wMNE) and the dynamic statistical parametric mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between the groups of epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the parietal cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment.

摘要

我们基于脑电图(EEG)记录,对癫痫儿童的大脑活动来源进行本地化。对于逆问题的数值解,借助于从公共数据库中处理的特定于年龄的 MRI 扫描,我们使用并比较了三种正则化数值方法,即标准化低分辨率脑电磁层析成像(sLORETA)、加权最小范数估计(wMNE)和动态统计参数映射(dSPM)。我们表明,所有三种方法都提供了癫痫儿童和对照组之间的相同的时空差异模式。特别是,我们的分析揭示了两组之间在顶叶皮层区域的统计学显著差异,这表明这些区域可能作为诊断目的和最终局部治疗的“生物标志物”。

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