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

By Liron Sheintuch, Alon Rubin, Noa Brande-Eilat, Nitzan Geva, Noa Sadeh, Or Pinchasof, Yaniv Ziv. Cell Reports, 2017

327 citations recorded by the source corpus.

Summary 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.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/527a8469d5972274aca4dcd435bcd41fd09b8965

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Citations in the corpus

CaImAn an open source tool for scalable calcium imaging data analysis (2019, methodology): “Similarly to Sheintuch et al. (2017) the same experiment performed over the course of different days produced very different populations of active neurons.”