Methods for Analyzing GCaMP Calcium Imaging

Research Article
Open access

Methods for Analyzing GCaMP Calcium Imaging

Houde He 1* , Sheng Huang 2
  • 1 Department of communication engineering, Xi Dian University, Xian ,710126, China    
  • 2 Living Word Shanghai, Shanghai,201107, China    
  • *corresponding author 1745148575@qq.com
TNS Vol.108
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-089-4
ISBN (Online): 978-1-80590-090-0

Abstract

GCaMP is a genetically encoded calcium indicator that is most widely used to observe the activities of populations of as many as thousands of neurons simultaneously using modern fluorescence imaging techniques. GCaMP is widely used to monitor neural activity in living animals. For example, researchers can use GCaMP to observe calcium transients in neurons, which are proxies for action potentials and synaptic activity.(GCaMP - an Overview ScienceDirect Topics, 2015). However, calcium imaging itself has several limitations. First,the calcium imaging has a poor signal to noise ratio(SNR) that makes the imaging difficult to detect dynamic signal and subtle fluctuations. Second, the fluorescence of GCaMP is slower than the action potentials from neuron activities especially in quick succession. The third issue is that there are millions of neurons that researchers investigated, but each neuron presents different activity and relation. Therefore, accurately targeting a specific group of neurons that perform similar tasks is challenging in the experiment. Aimed at these challenges,four successive methods including High pass filter, Gaussian Mixed Model,，correlation matrix, and devolution were used to improve the analysis of neuron activity. By using these methods to analyze the data set from two GCaMP6 (a particular version of GCaMP) fluorescence recording data sets containing the time series traces of hundreds of neurons in the mouse primary visual cortex (Vl) residing within a three-dimensional volume approximately 800μm × 800μm × 100μm in size. The results from the data analysis showed that the filtering effect of FIR high pass filter is the most significant because it significantly enhance the SNR and reduce noise. Through Gaussian Mixed Model and correlation coefficient, it clearly presents the connectivity of each neuron in a 233 times 233 matrix R and indicates the distribution of neuron activities by fitting into the Gaussian curve. The deconvolution successfully infer potential spikes. These methods efficiently enhance the noise reduction, network connectivity and temporal resolution of the analysis of imaging.

Keywords:

GCaMP, filtering, High-Pass Filter, Gaussian Mixed Model, Correlation coefficient, Deconvolution

He,H.;Huang,S. (2025). Methods for Analyzing GCaMP Calcium Imaging. Theoretical and Natural Science,108,138-150.
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References

[1]. 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: OpenSource Toolbox for Analysis of Calcium Imaging Data. Frontiers in Neural Circuits, 14.https://doi.org/10.3389/fncir.2020.00025

[2]. “Chebyshev Filter - an Overview | ScienceDirect Topics.” Www.sciencedirect.com,www.sciencedirect.com/topics/engineering/chebyshev-filter. Robbins, M., Christensen, C. N.,Kaminski, C. F., & Zlatic, M. (2021). Calcium imaging analysis – how far have we come?F1000Research, 10, 258. https://doi.org/10.12688/f1000research.51755.2

[3]. Ellis, George . “Butterworth Filter - an Overview | ScienceDirect Topics.”Www.sciencedirect.com, 2012, www.sciencedirect.com/topics/computer-science/butterworthfilter

[4]. Friedrich, Johannes, et al. “Fast Online Deconvolution of Calcium Imaging Data.”PLOS Computational Biology, vol. 13, no. 3, 14 Mar. 2017, pp. e1005423–e1005423,https://doi.org/10.1371/journal.pcbi.1005423. Accessed 30 Apr. 2023.

[5]. Hanning Window - an overview | ScienceDirect Topics. (n.d.). Www.sciencedirect.com.https://www.sciencedirect.com/topics/engineering/hanning-window

[6]. Hann or Hanning or Raised Cosine. (n.d.). Ccrma.stanford.edu. https://ccrma.stanford.edu/~jos/sasp/Hann Hanning Raised Cosine.html

[7]. j-friedrich. “GitHub - J-Friedrich/OASIS: Deconvolution of Calcium Imaging Data.” GitHub, 28，Feb. 2023, github.com/j-friedrich/OASIS. https://doi.org/10.1371/journal.pcbi.1005423.g001.Accessed 5 Oct. 2024. \Project3 data\MACOSX\Project3 data\OASIS matlabmaster\packages\oasis’)

[8]. Qin, Hao. “Computational Analysis of GCaMP Fluorescence Data in Neuronal Activity.” Theoretical and Natural Science, vol. 46, no. 1, 31 July 2024, pp. 163–172,www.researchgate.net/publication/382759620 Computational analysis of GCaMP fluorescence data in neuronal activity, https://doi.org/10.54254/2753-8818/46/20240628. Accessed 7 Oct.2024.

[9]. Rochefort, N. L., Jia, H., & Konnerth, A. (2008). Calcium imaging in the living brain: prospects for molecular medicine. Trends in Molecular Medicine, 14(9), 389–399.https://doi.org/10.1016/j.molmed.2008.07.005

[10]. Shemesh, Or A., et al. “Precision Calcium Imaging of Dense Neural Populations via a CellBody-Targeted Calcium Indicator.” Neuron, vol. 107, no. 3, Aug. 2020, pp. 470-486.e11,https://doi.org/10.1016/j.neuron.2020.05.029.

Cite this article

He,H.;Huang,S. (2025). Methods for Analyzing GCaMP Calcium Imaging. Theoretical and Natural Science,108,138-150.

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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About volume

Volume title: Proceedings of the 4th International Conference on Computing Innovation and Applied Physics

ISBN：978-1-80590-089-4(Print) / 978-1-80590-090-0(Online)
Editor：Ömer Burak İSTANBULLU, Marwan Omar, Anil Fernando
Conference website: https://2025.confciap.org/
Conference date: 17 January 2025
Series: Theoretical and Natural Science
Volume number: Vol.108
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. 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: OpenSource Toolbox for Analysis of Calcium Imaging Data. Frontiers in Neural Circuits, 14.https://doi.org/10.3389/fncir.2020.00025

[2]. “Chebyshev Filter - an Overview | ScienceDirect Topics.” Www.sciencedirect.com,www.sciencedirect.com/topics/engineering/chebyshev-filter. Robbins, M., Christensen, C. N.,Kaminski, C. F., & Zlatic, M. (2021). Calcium imaging analysis – how far have we come?F1000Research, 10, 258. https://doi.org/10.12688/f1000research.51755.2

[3]. Ellis, George . “Butterworth Filter - an Overview | ScienceDirect Topics.”Www.sciencedirect.com, 2012, www.sciencedirect.com/topics/computer-science/butterworthfilter

[4]. Friedrich, Johannes, et al. “Fast Online Deconvolution of Calcium Imaging Data.”PLOS Computational Biology, vol. 13, no. 3, 14 Mar. 2017, pp. e1005423–e1005423,https://doi.org/10.1371/journal.pcbi.1005423. Accessed 30 Apr. 2023.

[5]. Hanning Window - an overview | ScienceDirect Topics. (n.d.). Www.sciencedirect.com.https://www.sciencedirect.com/topics/engineering/hanning-window

[6]. Hann or Hanning or Raised Cosine. (n.d.). Ccrma.stanford.edu. https://ccrma.stanford.edu/~jos/sasp/Hann Hanning Raised Cosine.html

[7]. j-friedrich. “GitHub - J-Friedrich/OASIS: Deconvolution of Calcium Imaging Data.” GitHub, 28，Feb. 2023, github.com/j-friedrich/OASIS. https://doi.org/10.1371/journal.pcbi.1005423.g001.Accessed 5 Oct. 2024. \Project3 data\MACOSX\Project3 data\OASIS matlabmaster\packages\oasis’)

[8]. Qin, Hao. “Computational Analysis of GCaMP Fluorescence Data in Neuronal Activity.” Theoretical and Natural Science, vol. 46, no. 1, 31 July 2024, pp. 163–172,www.researchgate.net/publication/382759620 Computational analysis of GCaMP fluorescence data in neuronal activity, https://doi.org/10.54254/2753-8818/46/20240628. Accessed 7 Oct.2024.

[9]. Rochefort, N. L., Jia, H., & Konnerth, A. (2008). Calcium imaging in the living brain: prospects for molecular medicine. Trends in Molecular Medicine, 14(9), 389–399.https://doi.org/10.1016/j.molmed.2008.07.005

[10]. Shemesh, Or A., et al. “Precision Calcium Imaging of Dense Neural Populations via a CellBody-Targeted Calcium Indicator.” Neuron, vol. 107, no. 3, Aug. 2020, pp. 470-486.e11,https://doi.org/10.1016/j.neuron.2020.05.029.

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