Tracking calcium dynamics from individual neurons in behaving animals

Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.Author summary: Calcium imaging of neuron populations has enabled mapping the neuronal circuits that control animal behavior. However, animal movement, together with the intermittent detectability of calcium sensors, hinders the automatic tracking of individual neuron activity. Here we introduce a novel algorithm and open-access software to track the position of individual neurons in a calcium imaging movie in behaving animals. To handle the motion and the deformation of the animal our method combines state-of-the art algorithms to track neurons, with algorithms to estimate the deformation and predict the positions of neurons when they are silent and undetectable. Our method and software are robust and versatile in various animal models, from two-photon imaging of mouse visual cortex over days, to the highly deforming Hydra. Efficient image analysis and software for monitoring the activity of neuron populations in a wide range of animal models are needed to fully reconstruct the activity of neural circuits and study the emergent functional properties of neuronal ensembles that control animal state and behavior.