stiminterp provides an 1D-interpolation-based solution for removing photostimulation artefacts from multiphoton calcium imaging data.
The holographic stimulation saturates the PMTs and causes data loss. By identifying lines with the stimulation artefacts, this pipeline can replace the pixel rows containing the stimulation artefacts with the average values from corresponding rows in the preceding and following frames.
Create a fresh environment and install via pip:
conda create -n stiminterp-env python=3.12
conda activate stiminterp-env
pip install stiminterp
Understanding the causal role of brain dynamics is one of the fundamental questions in systemns neuroscience. Multiphoton holographic optogenetics, combined with multiphoton calcium imaging, enables causal testing of circuit models at single-cell resolution. However, photostimulation can saturate PMTs, producing line artefacts in the imaging data.
With stiminterp you can:
- Detect artefact-contaminated lines from HDF5 generated by ScanImage
- Perform spatiotemporal 1D-interpolation using
scipy.interpolate - Recover calcium imaging movies that can be fed into standard analysis pipelines such as
suite2p
Sample data used for examples and figures can be found on Gin.
All microscopy data has been acquired using a custom two-photon microscope by Sumiya Kuroda in the Mrsic-Flogel Lab and Dale Elgar from COSYS Ltd..
This work represents a joint collaboration between Stanford University and the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, supported by the Gatsby Charitable Foundation.
Previous work on artefact removal of all-optical imaging movies:
This package was inspired by previous calcium imaging analysis pipeline at Deisseroth lab.
This repo was made using neuroinformatics-unit/python-cookiecutter. See here for more info.
Contributions are welcome. Please open an issue or submit a pull request on GitHub.
BSD-3-Clause