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

Cultures of neurons in vitro are instrumental for studying network dynamics under normal and pathological conditions. Mature networks typically exhibit network bursting activity, traditionally quantified by simplified features such as inter-burst intervals and burst durations. These features have advanced our understanding of development, disease phenotypes, and drug effects. However, they overlook the temporal structure within bursts, which is highly sensitive to network changes and can thus reveal additional physiological or pathological effects. Here, we developed a comprehensive framework to quantify burst shapes, the time-course of network firing during bursts. On 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 classifying conditions (genetic disorders, pharmacological agents) using traditional and burst-shape features, and find that including shape features significantly improves accuracy. We provide an open pipeline for burst shape characterization, introducing burst shape as a complementary functional phenotyping feature that expands the analytical toolkit for disease modeling and drug screening.