MEAnalyzer: A GUI-Driven Platform for Comprehensive Spike Train Analysis, Bursting Patterns, and Connectivity Metrics

Introduction: : Multi-electrode array (MEA) technology offers a powerful window into measuring neuronal activity, enabling researchers to record extracellular signals from multiple electrodes and monitor the maturation or pathological changes of neuronal networks. However, turning these raw data streams into biologically meaningful insights can be technically challenging, often requiring advanced coding skills to run specialized analysis pipelines. Traditional spike train analysis programs often require scripting or command-line interfacing, creating barriers for widespread adoption of advanced analytical techniques. This barrier can slow the pace of discovery and limit the full potential of MEA-based studies. Script-base analytical pipelines, even when run by experienced coders, can also introduce systematic reproducibility challenges.
In response to these challenges, we developed MEAnalyzer, a software platform designed to bridge this gap by offering a suite of integrated spike train analysis methods, including periodicity evaluation, functional connectivity, statistical metrics, and advanced data visualizations. By integrating burst identification, cross-correlation analysis, and graph theory approaches into a cohesive platform, MEAnalyzer empowers investigators to reveal subtle patterns in network activity without scripting. The software features an intuitive graphical user interface (GUI) that eliminates the requirement for programming skills, democratizing MEA data analysis. We anticipate that MEAnalyzer will broaden participation in MEA-based experiments, facilitate reproducible research, and also advance understanding of how neurons interact, adapt, and form complex connectivity during development or disease.

Materials and

Methods: : MEAnalyzer is built in MATLAB (2024b) and designed to parse standard MEA file formats. Users load spike time stamps into a graphical user interface(GUI), where they can define parameters such as bin sizes and thresholds for burst detection. The GUI is constructed with a focus on flexibility, usability, and reproducibility. Users can upload data files through a file browser, select desired analysis modules, and specify relevant parameters through interactive menus. Data visualizations such as raster plots, inter-spike interval histograms, and connectivity graphs, are generated and can be exported in high resolution for publication.
Functional connectivity is quantified via cross-correlation of spike or burst trains and visualized with graph-theory metrics. Each electrode is treated as a node, and edges represent significant co-activation based on user-defined correlation thresholds. MEAnalyzer computes and displays network-level properties enabling researchers to identify trends in overall network complexity or the role of individual electrodes.
We validated MEAnalyzer by examining rat hippocampal neuron–astrocyte co-cultures over different maturation periods, performing paired t-tests to determine whether connectivity metrics significantly changed over time. We recorded 60 electrodes (8×8 plate) for 60s at two maturation stages (22 vs. 27 DIV) and set parameters such that there was a 0.2s bin interval and bursts were defined as 4 or more spikes in 0.1 seconds. Connectivity was defined as a cross-correlation of >0.7. A functional connectivity graph was created (Fig 1) and customized such that node size represented burst rate, node color represented mean firing rate and edge color represents level of cross-correlation.

Results, Conclusions, and Discussions:: Using MEAnalyzer to analyze spike trains from rat hippocampal neuron–astrocyte cocultures demonstrated its capacity to capture temporal changes in network connectivity. Recordings at 22 and 27 days in vitro (DIV) revealed that spike-based connectivity, measured via cross-correlation, significantly increased from a node degree of roughly 84% to 95% (p = 0.0001), indicating more synchronized firing as the cultures matured. Likewise, burst-based connectivity rose from approximately 48% to 65% (p < 0.0001), highlighting greater co-activation among electrodes as neuronal networks became more complex. Graph visualizations of these results showed a denser network at DIV 27(Fig1) where node color represented mean firing rate and edge color represents level of cross-correlation, with thicker and more abundant edges reflecting stronger connectivity.

These results suggest that MEAnalyzer effectively addresses a critical need for accessible MEA analysis tools. By consolidating numerous analytical capabilities into a single, user-friendly interface, this software can streamline research workflows and reduce the steep learning curve associated with traditional command-line approaches. The ease of parameter selection and real-time visualization encourages iterative exploration of data, potentially uncovering complex network behaviors that might be overlooked with more rigid workflows. The implemented examples also underscores MEAnalyzer’s utility for identifying critical transitions in network organization over time. By integrating spike and burst detection with an intuitive graph theory framework, the software reveals evolving patterns of connectivity that might otherwise remain buried in large datasets. Moreover, the GUI-based approach circumvents the need for coding, enabling a broader range of neuroscientists to analyze and interpret MEA data.

MEAnalyzer represents a significant advance in the field of neural engineering, providing a comprehensive and accessible analysis pipeline for both novice and experienced scientists. The platform’s robust performance, transparent architecture, and compatibility with diverse MEA systems underscore its potential to become a community-standard tool. By democratizing advanced spike train analyses, MEAnalyzer has the potential to foster broader adoption of MEA technologies, ultimately accelerating discoveries in neural circuit function, disease modeling, and therapeutic interventions.

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