Functional Imaging Data Analysis in Neuroscience: From Calcium Signals to Integrated Behavior Mapping

Description: In this course, students will learn essential computational approaches for analyzing functional imaging data in neuroscience. The focus is on the full workflow: quantitative processing of calcium imaging data, inference of neuronal spiking activity, and the integration of behavioral measurements with neuronal activity. All analyses are based on open-source Python tools (CaImAn, CASCADE), and the course emphasizes practical, reproducible methods that do not require advanced programming skills.

Next course time: Neuronal Circuit Dysfunction of C­NS diseases (WPM31), July 9, 2025, 1:00 pm to 4:00 pm, July 10, 2025, 9:30 am to 15:00 pm
Venue: DZNE Bonn, B.1.008

duration: 12.5 h for lectures including hands-on sessions
course held in: 2025
published: July 04, 2025
latest update: July 04, 2025 (07:09 am)

Jump to Syllabus

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 repository 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: Ca-image analysis with CaImAn: Demo pipeline

Tutorial 2: Spike inference from Calcium imaging data with CASCADE

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.
  • Integrate behavioral data with neuronal activity
    • Synchronize, merge, and visualize behavioral measurements (e.g., locomotion, stimulus presentation) with inferred neuronal activity.
    • Implement basic statistical and visualization methods to explore relationships between neuronal population activity and behavior.
  • 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.

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

  • n/a


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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