Kavli Affiliate: Brian Caffo
| Authors: Patrick Sadil, Konstantinos Arfanakis, Enamul Hoque Bhuiyan, Brian Caffo, Vince D Calhoun, Daniel J Clauw, Mark C DeLano, James C Ford, Ramtalik Gattu, Xiaodong Guo, Richard E Harris, Eric Ichesco, Micah A Johnson, Heejung Jung, Ari B Kahn, Chelsea M Kaplan, Nondas Leloudas, Martin A Lindquist, Qingfei Luo, Todd A Mulderink, Scott J Peltier, Pottumarthi V Prasad, Christopher Sica, Joshua Urrutia, Carol GT Vance, Tor D Wager, Yang Xuan, Xiaohong J Zhou, Yong Zhou, David C Shu and The Acute to Chronic Pain Signatures Consortium
| 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.