NOVA: a novel R-package enabling multi-parameter analysis and visualization of neural activity in MEA recordings

Abstract

Multielectrode array (MEA) technology enables simultaneous recording of electrical signals from neuronal networks, producing complex datasets. Current analytical approaches typically examine a limited number of metrics such as mean firing rate and synchronicity, leaving much of the data underutilized. To address this gap, we created NOVA (Neural Output Visualization and Analysis), an accessible R-based computational tool for comprehensive MEA data interpretation and visualization. NOVA integrates dimensionality reduction through principal component analysis, hierarchical clustering with heatmap generation, and temporal trajectory mapping of network activity patterns. Our code offers both a userfriendly pipeline requiring minimal coding background as well as customizable advanced plotting modules for experienced users. Validation experiments using primary cortical neurons during development and pharmacological manipulation demonstrated NOVAs capacity to detect subtle activity shifts overlooked by conventional methods. Notably, our unbiased approach identified network burst duration as a stronger contributor to activity variance than commonly reported firing rate metrics, exemplifying NOVAs utility for discovering meaningful patterns and generating data-driven hypotheses.