MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

Michel Hu, Monica Frega, Else A. Tolner, A. M.J.M. van den Maagdenberg, J. P. Frimat*, Joost le Feber

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
90 Downloads (Pure)

Abstract

Functional assessment of in vitro neuronal networks—of relevance for disease modelling and drug testing—can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ‘‘MEA-ToolBox’’, a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient’s hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.

Original languageEnglish
Pages (from-to)1077-1092
Number of pages16
JournalNeuroinformatics
Volume20
Issue number4
Early online date9 Jun 2022
DOIs
Publication statusPublished - Oct 2022

Keywords

  • Burst analysis
  • Data visualization
  • Electrophysiology
  • Multi-electrode array
  • Neuron
  • Spike analysis

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