Timothy P H Sit, Rachael C Feord, Alexander W E Dunn, Jeremi Chabros, David Oluigbo, Hugo H Smith, Lance Burn, Elise Chang, Alessio Boschi, Yin Yuan, George M Gibbons, Mahsa Khayat-Khoei, Francesco De Angelis, Erik Hemberg, Martin Hemberg, Madeline A Lancaster, Andras Lakatos, Stephen J Eglen, Ole Paulsen, Susanna B Mierau
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引用次数: 0
Abstract
Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.
微电极阵列(MEA)记录通常用于比较神经元培养物的点燃率和爆发率。微电极阵列记录还能揭示微尺度的功能连接、拓扑结构和网络动力学--在跨空间尺度的大脑网络中看到的模式。在神经成像中,网络拓扑经常使用图论指标来描述。然而,很少有计算工具可用于分析来自 MEA 记录的微尺度大脑功能网络。在此,我们介绍一种 MATLAB MEA 网络分析管道(MEA-NAP),用于分析从单孔或多孔 MEA 采集的原始电压时间序列。三维人脑器官组织或二维人源或鼠类培养物的应用揭示了网络发展的差异,包括拓扑结构、节点制图和维度。MEA-NAP 结合了基于多单元模板的尖峰检测、用于确定重要功能连接的概率阈值以及用于比较网络的归一化技术。MEA-NAP 可识别药物扰动和/或致病突变的网络级效应,从而为揭示机理和筛选新的治疗方法提供一个转化平台。视频摘要。