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Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data

Open access
Mukamel, Eran A., Nimmerjahn, Axel, Schnitzer, Mark J.·2009·Neuron

Abstract

SUMMARY Recent advances in fluorescence imaging permit studies of Ca2+ dynamics in large numbers of cells, in anesthetized and awake behaving animals. However, unlike for electrophysiological signals, standardized algorithms for assigning optically recorded signals to individual cells have not yet emerged. Here we describe an automated sorting procedure that combines independent component analysis and image segmentation for extracting cells’ locations and their dynamics with minimal human supervision. In validation studies using simulated data, automated sorting significantly improved estimation of cellular signals compared to conventional analysis based on image regions of interest. We used automated procedures to analyze data recorded by two-photon Ca2+ imaging in the cerebellar vermis of awake behaving mice. Our analysis yielded simultaneous Ca2+ activity traces for up to >100 Purkinje cells and Bergmann glia from single recordings. Using this approach, we found microzones of Purkinje cells that were stable across behavioral states and in which synchronous Ca2+-spiking rose significantly during locomotion.

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Crossref · 10.1016/j.neuron.2009.08.009

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Metadata

Authors
Mukamel, E. A., Nimmerjahn, A., & Schnitzer, M. J.
Venue
Neuron
Publisher
Elsevier BV
Published
2009-09
Volume
63
Issue
6
Pages
747-760
ISSN
0896-6273 (ISSN-L)
OpenAlex
W1995360097
Cited by
707
References
5
FWCI
10.87
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data. https://app.cochat.ai/papers/10.1016/j.neuron.2009.08.009
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Mukamel, E. A., Nimmerjahn, A., & Schnitzer, M. J. (2009). Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data. Neuron. https://doi.org/10.1016/j.neuron.2009.08.009

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.