Mahadevan Arjun, Codadu Neela K, Parrish R Ryley
Department of Cellular and Molecular Biology, Xenon Pharmaceuticals Inc., Burnaby, BC, Canada.
Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom.
Front Neurosci. 2022 Jul 1;16:904931. doi: 10.3389/fnins.2022.904931. eCollection 2022.
High-density multi-electrode array (HD-MEA) has enabled neuronal measurements at high spatial resolution to record local field potentials (LFP), extracellular action potentials, and network-wide extracellular recording on an extended spatial scale. While we have advanced recording systems with over 4,000 electrodes capable of recording data at over 20 kHz, it still presents computational challenges to handle, process, extract, and view information from these large recordings. We have created a computational method, and an open-source toolkit built in Python, rendered on a web browser using Plotly's Dash for extracting and viewing the data and creating interactive visualization. In addition to extracting and viewing entire or small chunks of data sampled at lower or higher frequencies, respectively, it provides a framework to collect user inputs, analyze channel groups, generate raster plots, view quick summary measures for LFP activity, detect and isolate noise channels, and generate plots and visualization in both time and frequency domain. Incorporated into our Graphical User Interface (GUI), we also created a novel seizure detection method, which can be used to detect the onset of seizures in all or a selected group of channels and provide the following measures of seizures: distance, duration, and propagation across the region of interest. We demonstrate the utility of this toolkit, using datasets collected from an HD-MEA device comprising of 4,096 recording electrodes. For the current analysis, we demonstrate the toolkit and methods with a low sampling frequency dataset (300 Hz) and a group of approximately 400 channels. Using this toolkit, we present novel data demonstrating increased seizure propagation speed from brain slices of Scn1aHet mice compared to littermate controls. While there have been advances in HD-MEA recording systems with high spatial and temporal resolution, limited tools are available for researchers to view and process these big datasets. We now provide a user-friendly toolkit to analyze LFP activity obtained from large-scale MEA recordings with translatable applications to EEG recordings and demonstrate the utility of this new graphic user interface with novel biological findings.
高密度多电极阵列（HD-MEA）能够以高空间分辨率进行神经元测量，以记录局部场电位（LFP）、细胞外动作电位，并在扩展的空间尺度上进行全网络细胞外记录。虽然我们拥有具有4000多个电极的先进记录系统，能够以超过20 kHz的频率记录数据，但处理、加工、提取和查看这些大量记录中的信息仍然存在计算挑战。我们创建了一种计算方法以及一个用Python构建的开源工具包，该工具包使用Plotly的Dash在网页浏览器上呈现，用于提取和查看数据以及创建交互式可视化。除了分别提取和查看以较低或较高频率采样的全部或小部分数据外，它还提供了一个框架来收集用户输入、分析通道组、生成光栅图、查看LFP活动的快速汇总指标、检测和隔离噪声通道，并在时域和频域生成图表和可视化。我们还将一种新颖的癫痫检测方法纳入我们的图形用户界面（GUI），该方法可用于检测所有或选定通道组中的癫痫发作起始，并提供以下癫痫发作指标：距离、持续时间和在感兴趣区域的传播。我们使用从包含4096个记录电极的HD-MEA设备收集的数据集来证明该工具包的实用性。对于当前分析，我们用一个低采样频率数据集（300 Hz）和一组大约400个通道来展示该工具包和方法。使用这个工具包，我们展示了新的数据，表明与同窝对照相比，Scn1aHet小鼠脑片的癫痫传播速度增加。虽然具有高空间和时间分辨率的HD-MEA记录系统已经取得了进展，但可供研究人员查看和处理这些大数据集的工具有限。我们现在提供了一个用户友好的工具包来分析从大规模MEA记录中获得的LFP活动，该工具包可翻译应用于脑电图记录，并通过新的生物学发现证明了这个新图形用户界面的实用性。
