Calcium imaging records fluorescence changes related to neural activity at multiple spatial scales. In this hands-on course, participants will work with both two-photon recordings of individual cells and widefield recordings of whole-brain activity, developing an understanding of the pathway from raw movies to interpretable neural and anatomical information.

Format and Workload

The course meets weekly. Each course day consists of a 3.5-hour live online session and a homework assignment for a total workload of 30 hours.

Course Content

Participants will learn to manage TIFF and OME files, inspect imaging data and metadata, and extract fluorescence traces from individual pixels and multi-pixel regions of interest. They will account for neuropil contamination and use deconvolution with OASIS to infer spike events from fluorescence signals.

For two-photon data, the course covers both manual and automated approaches to finding and analyzing cells. Participants will annotate cellular regions of interest, apply image-segmentation methods with Scikit-Image and Cellpose, and correct motion artifacts through image registration. They will use Suite2p through both its graphical interface and Python scripts to preprocess recordings and detect and classify cells, then bring these steps together in reproducible pipelines and track cells across multi-day recordings with Track2p.

Participants will also preprocess widefield imaging data, visualize whole-brain fluorescence, and compute event-related activity maps with NumPy and Matplotlib. The widefield data will then provide the basis for anatomical registration with Scikit-Image and atlas parcellation with siibra-python, including assessment of registration quality and exploration of anatomical regions in the siibra web viewer.

Practical work uses scientific Python tools including NumPy, SciPy, Matplotlib, and Scikit-Image, together with neuroscience-specific tools including OASIS, Suite2p, Track2p, Cellpose, and siibra-python.

Learning and participation

Topics

  • Fluorescence Extraction: visualizing calcium traces and inferring spikes with Tifffile, NumPy, Matplotlib, and OASIS.
  • ROI Selection: manual cell detection and automated image segmentation with Napari, Scikit-Image, and Cellpose.
  • Calcium Imaging Pipelines: preprocessing, ROI detection, cell classification, and cell tracking with Suite2p and Track2p.
  • Widefield and Atlas Analysis: preprocessing and visualizing whole-brain fluorescence, computing event-related maps, performing anatomical registration, and parcellating the data with Scikit-Image and siibra-python.

Intended participants

  • Researchers and students from all universities are welcome.
  • Participants of all educational backgrounds are welcome.
  • Comfort with Python data analysis and basic familiarity with microscopy or calcium imaging concepts are helpful.

Useful resources for preparation

Certification

Participants who attend at least 75% of the course and complete the assignments will receive a certificate by email at the end of the course.