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MEA-NAP:用于神经元 2D 和 3D 类器官多电极记录的灵活网络分析管道。

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings.

机构信息

Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3DY Cambridge, UK; Queen Square Institute of Neurology, University College London, WC1N 3BG London, UK.

Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3DY Cambridge, UK.

出版信息

Cell Rep Methods. 2024 Nov 18;4(11):100901. doi: 10.1016/j.crmeth.2024.100901. Epub 2024 Nov 8.

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.

摘要

微电极阵列 (MEA) 记录常用于比较神经元培养物中的放电和爆发率。MEA 记录还可以揭示微尺度功能连接、拓扑和网络动力学——这些模式在跨越空间尺度的大脑网络中可见。网络拓扑结构通常使用图论度量在神经影像学中进行特征描述。然而,用于分析从 MEA 记录中获得的微尺度功能大脑网络的计算工具很少。在这里,我们提出了一种用于从单孔或多孔 MEA 获得的原始电压时间序列的 MATLAB MEA 网络分析管道 (MEA-NAP)。对 3D 人类类器官或 2D 人源性或鼠源性培养物的应用揭示了网络发育的差异,包括拓扑、节点映射和维度。MEA-NAP 结合了基于多单元模板的尖峰检测、用于确定显著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 可以识别药物干扰和/或致病突变的网络级效应,因此可以为揭示机制见解和筛选新的治疗方法提供转化平台。视频摘要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bf6/11706071/dcb8b7899781/fx1.jpg

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