{
  "abstract": "Multi-electrode arrays (MEAs) generate mid- to high-throughput electrophysiological data widely used to characterise neural network activity in vitro. Downstream analysis of recordings from Axion Biosystems instruments, however, has long depended on the manufacturers closed-source pipeline (AxIS Navigator, NeuralMetric Tool, AxIS Metric Plotting Tool), limiting customisation and integration into flexible Python workflows. We present py-mea-span, an open-source Python package providing end-to-end analysis of Axion Biosystems spk binary files. Our package enables spike extraction, inter-spike interval burst detection, network burst detection and spike synchrony. We also provide tools for preparation of publication-ready visualisations including raster plots with spike-detection-rate histograms, plate-layout heatmaps, time-course trajectories, single timepoint violin plots and statistical comparisons. Outputs reproduce the manufacturers NeuralMetric Tool reference with near perfect accuracy in all benchmarked metrics. A mea-span command-line interface enables batch processing without writing Python code. As a proof of concept, we applied py-mea-span to human iPSC-derived cortical neurons with CRISPRi-mediated LGI2 knockdown. This revealed a reproducible network hyperexcitability phenotype in which burst and network burst frequencies were elevated while spike synchrony remained unchanged. Availability and implementation The package is distributed on PyPI (pip install py-mea-span), and version-controlled at https://github.com/Molecularbiologyworld/py-mea-span. User feedback can be provided via the GitHub issues tab.",
  "abstract_shown": true,
  "affiliations": [
    "University of Cambridge"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Z. Z. Han"
    },
    {
      "affs": [
        0
      ],
      "name": "J. E. Deane"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f2c6ckgm3w2n2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.14.759118",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759118v1.full",
  "id": "f2c6ckgm3w2n2",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759118v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7223058138",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759118v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f2c6ckgm3w2n2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:44Z",
      "native_id": "10.64898/2026.10.14.759118",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759118v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons",
  "title_text": "Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:37Z",
  "url": "https://research.draconitic.com/paper/f2c6ckgm3w2n2",
  "venue": "bioRxiv",
  "versions": []
}