The Seeded Iterative Demixing (SID) algorithm extracts neuronal activity signals (as reported by fluorescent indicators) from Light Field Microscopy (LFM) recordings in scattering brain tissue. SID was developed by the Vaziri Lab at Rockefeller University, and is described and demonstrated in the following publications:
Nöbauer*, T., Skocek*, O., Pernía-Andrade, A. J., Weilguny, L., Martínez Traub, F., Molodtsov, M. I. & Vaziri, A.
Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy.
Nature Methods 14, 811–818 (2017).
Skocek*, O., Nöbauer*, T., Weilguny, L., Martínez Traub, F., Xia, C. N., Molodtsov, M. I., Grama, A., Yamagata, M., Aharoni, D., Cox, D. D., Golshani, P. & Vaziri, A.
High-speed volumetric imaging of neuronal activity in freely moving rodents.
Nature Methods 15, 429–432 (2018).
- Input argument reference for the main function,
sid_main() - Discussion of input arguments and general usage (Wiki)
- Function documentation (generated from source code)
- A demo example will be added shortly.
- Matlab Base
- Matlab Parallel Computing Toolbox
- Matlab Image Processing Toolbox
- Matlab Statistics and Machine Learning Toolbox
- Matlab Curve Fitting Toolbox
Tested with Matlab R2017b on Red Hat Enterprise Linux 6.
A multi-core workstation is recommended, with enough RAM to hold the input data. One or more Tesla-grade GPUs with several GB of RAM are highly recommended for fast execution (see Matlab documentation for supported GPU models).
Clone/download and add the base folder and the src/ and external/ folders to Matlab path recursively.
Binaries for command-line usage will be provided in the bin/ directory (the current binaries are not yet functional; to be updated shortly).
See LICENSE.md