Acute effects of anti-seizure medications on network level dynamics: A systematic comparison using multielectrode...

A multi-electrode array (MEA) is a powerful extracellular recording technique for long-term monitoring of neuronal network activity. In recent years, MEA-based assays have been applied increasingly to evaluate drug efficacy and neurotoxicity, including the assessment of anti-seizure medications (ASMs). However, systematic comparisons of ASMs with different mechanisms of action under identical experimental conditions remain limited.

In this study, we evaluated the effects of 18 ASMs on neuronal network activity using MEA recordings under standardized conditions. Network activity was characterized using four components-spike, burst, network burst, and synchrony-and a total of 44 MEA-derived parameters were analyzed. Dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) followed by hierarchical clustering classified the 18 ASMs into four distinct clusters.

Cluster 1, including S-licarbazepine, oxcarbazepine, and lamotrigine, exhibited pronounced effects across all four network components. Cluster 2, comprising carbamazepine, eslicarbazepine acetate, phenytoin, phenobarbital, and stiripentol, significantly affected spike and burst duration but showed only limited effects on burst frequency, network burst frequency, or synchrony. Cluster 3 was solely diazepam, for which minimal effects were detected. The remaining ASMs were assigned to Cluster 4 and produced only minimal measurable effects in network activity.

Several limitations should be considered, including the use of primary neuron cultures, incomplete maturation of certain drug targets, evaluation of only acute drug effects, and the limited spatial resolution of low-density MEA systems. Nevertheless, our results demonstrate that this MEA-based platform enables systematic evaluation of ASM effects on neuronal networks under identical experimental conditions. Furthermore, multivariate and unsupervised clustering analysis of 44 MEA parameters facilitated classification independent of drug mechanism of action. These findings suggest that MEA, combined with multidimensional data analysis, may provide a useful platform for the direct comparison of ASMs and could contribute to future pharmacological screening and drug discovery efforts.