Machine Learning and Complex Network Analysis of Drug Effects on Neuronal Microelectrode Biosensor Data

Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA A receptor antagonist inducing hypersynchrony, demonstrated the workflow’s ability to detect pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. Comparing machine learning-based results with linear mixed model statistical tests validated the biological relevance of the rankings obtained while emphasizing caution when interpreting inconsistencies. This robust framework enables analysis of subtle or complex drug effects on in vitro neuronal networks, advancing biosensor applications in neuropharmacology and drug discovery.