Deciphering Complexity in Human Brain Organoids via a Novel Hurst Exponent Estimation

Complexity is a hallmark of the human brain, emerging from the interaction of billions of neurons over time. Among various measures, the Hurst exponent (H) has been used to characterize temporal correlations in neuronal activity. Here, we introduce a method to estimate H directly from binary spike train series and propose that the coefficient of variation (CV) of H, rather than H itself, more accurately reflects neural complexity during organoid maturation. After validating the method on simulated data with embedded short- and long-range correlations, we applied it to cortical organoids derived from human-induced pluripotent stem cells, recorded via multielectrode arrays over 250 day. The analysis revealed a peak in complexity, as indicated by the CV of H, around the 100th day. At this stage, the organoids exhibited a wide repertoire of dynamic patterns, including both isolated spiking and highly synchronized network events. These results suggest that organoid activity undergoes a structured evolution in complexity and that CV of H provides a robust metric to capture this transition. Our approach offers a scalable tool for quantifying neural complexity in the developing brain.