High-density multielectrode array (MEA) recordings in a rodent glioma model.

Abstract

BACKGROUND: High-grade gliomas are aggressive primary brain tumors that affect the surrounding brain tissue, which can lead to neurological deficits, epilepsy, cognitive deficits, and tumor progression. Research has focused on glutamatergic alterations, but the understanding of the underlying network mechanisms is still scarce. NEW METHOD: To investigate how gliomas might affect neural network integrity and capacity for spiking activity, we induced network bursting events in neocortical slices from F98 glioma-bearing rats. Network activity was recorded using a 64 × 64 high-density multielectrode array to assess the network's capacity for extended periods of high activity. We describe glioma cell implantation, brain slice preparation, and detailed data analysis of electrophysiological recordings, focusing on computational methods for characterization of the network-wide bursting events. RESULTS: Spike sorting was performed using Kilosort4 and employed to identify network bursts. Burst origins were estimated from multi-unit activity, and network burst propagation speed was assessed. Network-level functional connectivity was analyzed using metrics derived from graphs based on the spike time tiling coefficient. Our results indicate reduced network integrity in glioma-bearing slices. COMPARISON WITH EXISTING METHODS: The combination of a high-density multielectrode array with Kilosort4 is advantageous over conventional recording techniques in terms of detecting network bursting and plotting center-of-activity trajectories. CONCLUSIONS: This protocol provides a robust platform for dissecting network activity and establishes a basis for future mechanistic studies. We also discuss the limitations and experimental challenges of our preclinical approach to investigating glioma-associated effects on surrounding neuronal networks.