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Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data
Open accessAbstract
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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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Sulforhodamine 101 as a specific marker of astroglia in the neocortex in vivo
Nimmerjahn, A., Kirchhoff, F., Kerr, J. N. D., & Helmchen, F. (2004) · Nature Methods · doi:10.1038/nmeth706 · Cited by 784
Self-citationCa2+ buffering and action potential-evoked Ca2+ signaling in dendrites of pyramidal neurons
Helmchen, F., Imoto, K., & Sakmann, B. (1996) · Biophysical Journal · doi:10.1016/s0006-3495(96)79653-4 · Cited by 559
Control of visually guided behavior by distinct populations of spinal projection neurons
Orger, M. B., Kampff, A. R., Severi, K. E., Bollmann, J. H., & Engert, F. (2008) · Nature Neuroscience · doi:10.1038/nn2048 · Cited by 274
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Self-citationMiniaturized integration of a fluorescence microscope
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Self-citationFunctional imaging of hippocampal place cells at cellular resolution during virtual navigation
Dombeck, D. A., Harvey, C. D., Tian, L., Looger, L. L., & Tank, D. W. (2010) · Nature Neuroscience · doi:10.1038/nn.2648 · Cited by 873
Daigle, T. L., Madisen, L., Hage, T. A., Valley, M. T., Knoblich, U., Larsen, R. S., Takeno, M. M., Huang, L., Gu, H., Larsen, R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.035 · Cited by 817
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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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