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Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

机译:使用Fieldtrip工具箱的小鼠脑电图的偶极子源定位

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摘要

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.
机译:小鼠模型是神经科学中重要的研究工具,用于检查大脑功能和遗传差异在不同大脑区域的疾病。但是,与人类功能性大脑映射相比,在特定实验操作下映射激活的大脑区域的有限技术一直是小鼠模型的一个缺点。在这里,我们提出了一种功能强大的大脑映射方法,可以对小鼠大脑进行快速而强大的体内大脑映射。该方法基于具有微阵列的高密度脑电图(EEG)采集和EEG源估计以定位电生理起源。我们利用Fieldtrip工具箱进行源估计,并利用其软件开放性和灵活性来对EEG体积传导进行建模。比较了三种源估计技术:具有最小范数估计(MNE)的分布源建模,具有多个信号分类的扫描(MUSIC)和单偶极子拟合。使用光遗传学工具提供了评估定位方法性能的已知资源。基于接收器工作特性(ROC)分析对准确性进行了量化。 MNE和MUSIC算法绘制的平均检测准确率很高,在90%的灵敏度下误报率分别小于1.3%和7%。在单偶极子拟合算法中,平均中心距小于1.2 mm。使用微阵列进行小鼠微阵列脑电图源定位,为在清醒的小鼠中进行功能性脑图绘制提供了可靠的方法,从而为人脑跨物种研究提供了可能。

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