Abstract
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 long-term monitoring of the activity of individual neurons and the subsequent statistical characterization of neuronal functional organization. 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 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 large range of particle motions and detectability parameters. Combining automatic monitoring of single neuron activity over long time-lapse sequences in behaving animals with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra. We document the existence three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results prove that the EMC2 algorithm can be used as a robust platform for neuronal tracking in behaving animals.
Competing Interest Statement
The authors have declared no competing interest.
