Kavli Affiliate: Edward Chang
| Authors: Jinghui Geng, Kateryna Voitiuk, David F. Parks, Ash Robbins, Alex Spaeth, Jessica L. Sevetson, Sebastian Hernandez, Hunter E. Schweiger, John P. Andrews, Spencer T. Seiler, Matthew A. T. Elliott, Edward F. Chang, Tomasz J. Nowakowski, Rob Currie, Mohammed A. Mostajo-Radji, David Haussler, Tal Sharf, Sofie R. Salama and Mircea Teodorescu
| Summary:
Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.