Functional Imaging Data Analysis in Neuroscience: From Calcium Signals to Population Dynamics
Current announcements
Nothing at the moment.
Course requirements
- Please, bring a laptop.
- Please, install conda/miniforge before the course starts.
- Please, install VSCodeꜛ and the Python extension for VS Codeꜛ, the Python Environments extensionꜛ, and the Jupyter extensionꜛ before the course starts. We will use VS Code as our main Python IDE during the course. If you want to use another IDE, please make sure that it supports Jupyter notebooks and Python 3.9 or higher.
- Please, download the course material from this GitHub repositoryꜛ and bring the image files you have recorded during your practical work.
- Please, also bring the DeepLabCut tracking results you have generated during the DeepLabCut course.
Syllabus
Tutorial 1: From calcium imaging to network dynamics: An overview
Tutorial 2: Ca-image analysis with CaImAn: Demo pipeline
Tutorial 3: Spike inference from Calcium imaging data with CASCADE
Tutorial 4: Identifying patterns and hidden structure in neural population activity
Tutorial 5: Discussion and roundup
Exercises
Unless otherwise stated, please perform the hands-on examples shown in each tutorial. Use the datasets provided or your own experimental data. You are encouraged to experiment with your own recordings and to combine the analysis techniques from different parts of the course. Each tutorial (including reading and exercises) takes around 1.5–3 hours.
Learning outcomes
Upon successful completion, students will be able to:
- Understand the principles and workflow of functional calcium imaging data analysis in neuroscience
- Explain the rationale for using calcium imaging and its advantages and limitations for studying neuronal population dynamics.
- Process and analyze calcium imaging data using CaImAn
- Perform motion correction, segmentation, and extraction of fluorescence signals from raw calcium imaging data.
- Apply quantitative quality control and data visualization methods to extracted traces.
- Infer neuronal spiking activity from calcium imaging signals using CASCADE
- Understand the challenges and approaches for inferring action potentials from slow calcium dynamics.
- Apply the CASCADE pipeline to convert calcium traces into estimated spike trains and evaluate inference accuracy.
- Analyze and interpret neural population activity
- Visualize and quantify population activity using heatmaps, binning, and summed activity traces.
- Apply correlation and dimensionality reduction techniques to uncover patterns and functional structure.
- Sort and cluster neurons based on their relationship to population-level signals.
- Identify functional assemblies and emergent collective dynamics within neural circuits.
- Critically evaluate the limitations, sources of error, and reproducibility of each analysis step
- Identify common pitfalls in calcium imaging analysis and spike inference.
- Document and reproduce analysis workflows using recommended open-source tools.
References and further readings
Calcium analysis tools:
- Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L Gauthier, Pengcheng Zhou, Baljit S Khakh, David W Tank, Dmitri B Chklovskii, and Eftychios A Pnevmatikakis, CaImAn an open source tool for scalable calcium imaging data analysis, 2019, eLife, Vol. 8, p. e38173, eLife Sciences Publications, Ltd, DOI: 10.7554/eLife.38173ꜛ.
- Marius Pachitariu, Carsen Stringer, Sylvia Schröder, Mario Dipoppa, L. Federico Rossi, Matteo Carandini, and Kenneth D Harris, Suite2p: Beyond 10,000 neurons with standard two-photon microscopy, 2016, bioRxiv, p. 1-9, doi: doi.org/10.1101/061507ꜛ.
- Eftychios A. Pnevmatikakis, Daniel Soudry, Yuanjun Gao, Timothy A. Machado, Josh Merel, David Pfau, Thomas Reardon, Yu Mu, Clay Lacefield, Weijian Yang, Misha Ahrens, Randy Bruno, Thomas M. Jessell, Darcy S. Peterka, Rafael Yuste, Liam Paninski, , 2016, Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data, Neuron, Volume 89, 2, p. 285–299, DOI: 10.1016/j.neuron.2015.11.037
- Pnevmatikakis, E. A. & Giovannucci, A., NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data, 2017, Journal of Neuroscience Methods, Volume 291, p. 83–94, DOI: 10.1016/j.jneumeth.2017.07.031
General Calcium imaging:
- W. Denk, J. H. Strickler, W. W. Webb, Two-photon laser scanning fluorescence microscopy, 1990, Scienceꜛ 06 Apr 1990 : 73-76, DOI: 10.1126/science.2321027ꜛ
- Grienberger, C., & Konnerth, A., Imaging calcium in neurons, Neuron, 2012, 73(5), 862–885, DOI: 10.1016/j.neuron.2012.02.011ꜛ
- Chen, T.-W., Wardill, T. J., Sun, Y., et al., Ultrasensitive fluorescent proteins for imaging neuronal activity, Nature, 2013, 499(7458), 295–300, DOI: 10.1038/nature12354ꜛ
Spike inference methods and ground truth datasets:
- P Rupprecht, S Carta, A Hoffmann, M Echizen, A Blot, AC Kwan, Y Dan, SB Hofer, K Kitamura, F Helmchen, & RW Friedrich, A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging., 2021, Nat Neurosci, Vol. 24, Issue 9, pages 1324-1337, url, doi: 10.1038/s41593-021-00895-5ꜛ
- Theis, L., Berens, P., Froudarakis, E., et al., Benchmarking spike rate inference in population calcium imaging, Neuron, 2016, 90(3), 471-482, DOI: 10.1016/j.neuron.2016.04.014ꜛ
- Berens, P., Freeman, J., Deneux, T., et al., Community-based benchmarking improves spike rate inference from two-photon calcium imaging data, PLOS Computational Biology, 2018, 14(5), e1006157, DOI: 10.1371/journal.pcbi.1006157
Advanced computational approaches for neural data:
- Wulfram Gerstner, Werner M. Kistler, Richard Naud, and Liam Paninski, Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, 2014, Cambridge University Press, ISBN: 978-1-107-06083-8, free online versionꜛ
- Gerasimos G. Rigatos (2015), Advanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological Neurons, , Springer-Verlag Berlin Heidelberg, doi: 10.1007/978-3-662-43764-3
- Christoph Börgers, An Introduction to Modeling Neuronal Dynamics, 2017, Vol. 66, Springer International Publishing, doi: 10.1007/978-3-319-51171-9
- P. Dayan, I. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, 2001, MIT Press
Open-source resources, repositories, and tutorials:
- HelmchenLabSoftware/Cascade: https://github.com/HelmchenLabSoftware/Cascadeꜛ
- CaImAn toolbox: https://github.com/flatironinstitute/CaImAnꜛ
- Suite2p: https://github.com/MouseLand/suite2pꜛ
- SpikeFinder benchmarking datasets: https://github.com/codeneuro/spikefinderꜛ
Acknowledgements
Sample data used in this course include:
- CaImAn sample calcium imaging data: Example datasets are provided by the CaImAn project (Giovannucci et al., eLife, 2019) and are available through the official CaImAn GitHub repositoryꜛ. Please refer to the repository for detailed licensing and citation information.
- CASCADE sample traces: Example datasets and ground truth traces are provided by the CASCADE project (Giovannucci et al., Nature Methods, 2020) and are available via the official CASCADE repositoryꜛ. Refer to the repository and associated publications for further details.
We gratefully acknowledge the original authors and maintainers of CaImAn and CASCADE for making their sample data publicly available for educational and research purposes.
Past courses
- Neuronal Circuit Dysfunction of CNS diseases (WPM31), July 9+10, 2025
This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).
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