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Computational analysis of GCaMP fluorescence data in neuronal activity
- 1 Changchun university of science and technology
* Author to whom correspondence should be addressed.
Abstract
The background and motivation of our research is to explore how to use computational methods to analyze GCaMP fluorescence data. GCaMP is a genetically encoded calcium indicator that can be used to observe the activity of thousands of neurons simultaneously using modern fluorescence imaging techniques. Our research includes the application of principles such as basic signal processing, statistical inference, hypothesis testing, and graph theory to help understand raw GCaMP fluorescence data recorded from awake, behavioral mice. The data set includes GCaMP fluorescence trace and correlation coefficient matrix among neurons. Our methods include high-pass filtering, Gaussian mixture model fitting and online active set method peak inference (oasis). By analyzing the correlation between neurons, we can understand the connection and centrality between neurons.
Keywords
Oasis Computational Analysis, GCaMP Fluorescence, Neuronal Activity, Data Deconvolution, Neural Networks
[1]. Zhang, Y., Rózsa, M., Liang, Y. et al. Fast and sensitive GCaMP calcium indicators for imaging neural populations. Nature 615, 884–891 (2023). https://doi.org/10.1038/s41586-023-05828-9
[2]. Shemesh, O., Linghu, C., Piatkevich, K. D., Goodwin, D. R., Gritton, H., Romano, M. F., ... & Boyden, E. (2020). Precision Calcium Imaging of Dense Neural Populations via a Cell-Body-Targeted Calcium Indicator. Neuron. Available at: https://www.cell.com/neuron/pdf/S0896-6273(20)30398-6.pdf
[3]. Dorostkar, M., Dreosti, E., Odermatt, B., & Lagnado, L. (2010). Computational processing of optical measurements of neuronal and synaptic activity in networks. Journal of Neuroscience Methods. Available at: https://www.sciencedirect.com/science/article/pii/ S0165027010000725
[4]. Tian, L., et al. (2009). Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nature Methods, 6(12), 875–881.
[5]. Chen, T. W., et al. (2013). Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature, 499(7458), 295–300.
[6]. Dana, H. et al. (2019). High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nature Methods, 16(7), 649-657.
[7]. Cantu, D. A., Wang, B., Gongwer, M. W., He, C. X., Goel, A., Suresh, A., Kourdougli, N., Arroyo, E. D., Zeiger, W., & Portera-Cailliau, C. (2020). EZcalcium: Open-Source Toolbox for Analysis of Calcium Imaging Data. Technology and Code, doi: 10.3389/fncir.2020.00025.
[8]. M. T. Valley,* M. G. Moore,* J. Zhuang, N. Mesa, D. Castelli, D. Sullivan, M. Reimers, and J. Waters 17 JAN 2020https://doi.org/10.1152/jn.00304.2019
[9]. Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M., & Harris, K. D. (2016). Fast and accurate spike sorting of high-channel count probes with KiloSort. Advances in Neural Information Processing Systems, 29.
[10]. Front. Mol. Neurosci., 05 December 2014 Sec. Methods and Model Organisms Volume 7 - 2014 | https://doi.org/10.3389/fnmol.2014.00097
[11]. Stopper, G., Caudal, L.C., Rieder, P. et al. Novel algorithms for improved detection and analysis of fluorescent signal fluctuations. Pflugers Arch - Eur J Physiol 475, 1283–1300 (2023). https://doi.org/10.1007/s00424-023-02855-3
Cite this article
Qin,H. (2024).Computational analysis of GCaMP fluorescence data in neuronal activity.Theoretical and Natural Science,46,163-172.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Modern Medicine and Global Health
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