Tracking the Same Neurons across Multiple Days in Ca²⁺ Imaging Data

Ca²⁺ imaging techniques permit time-lapse recordings of neuronal activity from large populations over weeks. However, without identifying the same neurons across imaging sessions (cell registration), longitudinal analysis of the neural code is restricted to population-level statistics. Accurate cell registration becomes challenging with increased numbers of cells, sessions, and inter-session intervals. Current cell registration practices, whether manual or automatic, do not quantitatively evaluate registration accuracy, possibly leading to data misinterpretation. We developed a probabilistic method that automatically registers cells across multiple sessions and estimates the registration confidence for each registered cell. Using large-scale Ca²⁺ imaging data recorded over weeks from the hippocampus and cortex of freely behaving mice, we show that our method performs more accurate registration than previously used routines, yielding estimated error rates <5%, and that the registration is scalable for many sessions. Thus, our method allows reliable longitudinal analysis of the same neurons over long time periods. Sheintuch et al. present a probabilistic method for tracking the same neurons across multiple days (cell registration) in large-scale Ca²⁺ imaging data recorded from behaving mice. The probabilities for pairs of neighboring cells from different sessions to be the same neuron are estimated and utilized to perform reliable cell registration.