Microelectrode arrays (MEAs) are valuable tools for electrophysiological analysis at a cellular population level, providing assessment of neural network health and development. Analysis can be complex, however, requiring intensive processing of large high-dimensional data sets consisting of many activity parameters. As a result, valuable information is lost, as studies subjectively report relatively few metrics in the interest of simplicity and clarity. From a screening perspective, many groups report simple overall activity; we are more interested in culture health and changes in network connectivity that may not be evident from basic activity parameters. For example, general changes in overall firing rate – the most commonly reported parameter – provide no information on network development or burst character, which could change independently. Our goal was to develop a fast objective process to capture most, if not all, the valuable information gained when using MEAs in neural development and toxicity studies. We implemented principal component analysis (PCA) to reduce the high dimensionality of MEA data. Upon analysis, we found that the first principal component was strongly correlated to time, representing neural culture development; therefore, factor loadings were used to create a single index score – named neural activity score (NAS) – reflective of neural maturation. To validate this score, we applied it to studies analyzing various treatments. In all cases, NAS accurately recapitulated expected results, suggesting this method is viable. This approach may be improved with larger training data sets and can be shared with other researchers using MEAs to analyze complicated treatment effects and multicellular interactions.
bioRxiv Subject Collection: Neuroscience