Weighted Graph for Longitudinal Tracking of Neurons in Cortical Organoids on a High-Density Microelectrode Array

Tracking cortical neurons over several days can provide extensive data for their electrophysiology features, development trajectories, and connectivity with other neurons within neuronal circuits. However, reliably identifying the same neurons over time remains a challenge for high-density microelectrode array (HD-MEA) recordings. In this study, we developed a weighted graph-based tracking algorithm to classify trackable units and monitor their features over time. We applied this algorithm to 160 recordings of neural activity collected from a mouse cortical organoid every hour for seven days on an HD-MEA. Recordings were pre-processed to extract spiking waveforms, spike trains, and spatial locations. Waveforms were clustered for putative cell types. Our approach identified 46 trackable units, representing 53.5% of all recorded neural activity samples. Functional connectivity analysis revealed that trackable units had a higher tendency to connect with untrackable ones. This study demonstrates the effectiveness of a weighted graph-based method for tracking individual neurons over extended periods and provides insights into neuronal development and network dynamics in cortical organoids.