Data-driven burst shape analysis for functional phenotyping of neuronal cultures

Abstract

Cultures of neurons in vitro are instrumental for studying network dynamics in normal and pathological conditions. Mature networks typically exhibit network bursting activity, which has traditionally been quantified by simplified features such as inter-burst intervals and burst durations. While these features advanced the understanding of development, disease phenotypes, and drug effects, they overlook the temporal structure of activity within bursts. Here, we developed a comprehensive framework to quantify burst shapes, the time course of network firing during bursts. Applying this approach to four datasets, including rodent- and human pluripotent stem cell-derived cultures, we show that burst shapes contain rich information about the underlying network dynamics. We quantify this information by using traditional and shape features to classify the recording conditions (types of genetic disorder, presence of pharmacological agents) and demonstrate that shapes significantly increase classification accuracy. We provide a pipeline for burst shape characterization, including simplified features that capture most of the shape information, establishing burst shape as a robust and biologically meaningful marker for functional phenotyping in disease modeling and drug screening.