QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays

Mahmud, M. ORCID: 0000-0002-2037-8348, Pulizzi, R., Vasilaki, E. and Giugliano, M., 2014. QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays. Frontiers in Neuroinformatics, 8: 26. ISSN 1662-5196

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Abstract

Micro-Electrode Arrays (MEAs) have emerged as a mature technique to investigate brain (dys)functions in vivo and in in vitro animal models. Often referred to as “smart” Petri dishes, MEAs have demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA,h uncompressed). Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc.), are decomposed and batch-queued to a multi-core architecture or to a computers cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and inspire the creation of community-supported cloud-computing facilities for MEAs users.

Item Type:	Journal article
Publication Title:	Frontiers in Neuroinformatics
Creators:	Mahmud, M., Pulizzi, R., Vasilaki, E. and Giugliano, M.
Publisher:	Frontiers Research Foundation
Date:	2014
Volume:	8
ISSN:	1662-5196
Identifiers:	Number Type 10.3389/fninf.2014.00026 DOI
Rights:	Copyright © 2014 Mahmud, Pulizzi, Vasilaki and Giugliano. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	24 Jul 2018 07:54
Last Modified:	24 Jul 2018 07:55
URI:	https://irep.ntu.ac.uk/id/eprint/34140

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