SpikeSpector software for MultiElectrode Arrays: Field potential analysis and spatial integration of...

The analysis of extracellular recordings from MultiElectrode Arrays (MEAs) is central to understanding the spatio-temporal dynamics of neuronal network activity in both physiological and pathological conditions. The detection of local field potential (LFP) events (of variable amplitude and shape, embedded in noisy, overlapping signals) remains particularly challenging when relying solely on visual detection or on automated tools.

We developed SpikeSpector, a standalone, Python-based graphical user interface (GUI) that enables comprehensive LFP detection, precise manual curation, spatial mapping, and multimodal integration with immunohistochemical markers.

SpikeSpector tools are described using MEA data recorded from a mouse brain slice harboring a ganglioglioma tumor. SpikeSpector enables users to refine automatically detected events through interfaces, improving the reliability of field potential identification in complex datasets. The platform offers visualization of voltage traces aligned to MEA layouts, customizable spike sorting and deletion tools, and quantification of waveform metrics such as amplitude, slope, and half-width. In addition, SpikeSpector introduces novel modules for correlating spike metrics with histological staining intensities, offering a powerful framework to spatially relate electrophysiological patterns to tissue architecture.

Most existing tools for MEA analysis are limited by licensing costs, dependence on proprietary environments, and lack of integration with histological data, crucial for studies on brain diseases.

Overall, SpikeSpector represents a flexible and accessible solution for bridging electrophysiological data with histological insights, enabling a more accurate and context-rich analysis of neural dynamics.