Features -------- CaImAn includes a variety of scalable methods for the analysis of calcium (and voltage) imaging data: | - **Motion correction** [NoRMCorre]_ - Fast parallelizable OpenCV and FFT-based motion correction of large movies - Can be run also in online mode (i.e. one frame at a time) - Corrects for non-rigid artifacts due to raster scanning or non-uniform brain motion - FFTs can be computed on GPUs (experimental). Requires pycuda and skcuda to be installed. | - **Source extraction** - Separates different sources based on constrained nonnegative matrix Factorization [CNMF]_, [SNMF]_, [CaImAn]_ - Deals with heavily overlapping and neuropil contaminated movies - Suitable for both 2-photon [CNMF]_ and 1-photon [CNMF_E]_ calcium imaging data - Selection of inferred sources using a pre-trained convolutional neural network classifier - Online processing available for both 2-photon [OnACID]_ and 1-photon data streams [OnACID-E]_ | - **Denoising, deconvolution and spike extraction** - Infers neural activity from fluorescence traces [CNMF]_ - Also works in online mode (i.e. one sample at a time) [OASIS]_ | - **Automatic ROI registration across multiple days** [CaImAn]_ | - **Handling of very large datasets** - Utilizes memory mapping for efficient loading of large datasets. - Parallel processing in patches. - Frame-by-frame online processing. - OpenCV-based efficient movie playing and resizing. | - **Pipeline for Voltage Imaging Analysis** [VolPY]_ - Uses a Mask R-CNN to identify neurons in the FOV - Extracts spiking activity using adaptive template matching. - Fully integrated with CaImAn, inherits all its capabilities. | - **Behavioral Analysis** [Behavior]_ - Unsupervised algorithms based on optical flow and NMF to automatically extract motor kinetics - Scales to large datasets by exploiting online dictionary learning - We also developed a tool for acquiring movies at high speed with low cost equipment `[Github repository] `__. | - **Variance Stabilization** [VST]_ - Noise parameters estimation under the Poisson-Gaussian noise model - Fast algorithm that scales to large datasets - A basic demo can be found at ``CaImAn/demos/notebooks/demo_VST.ipynb`` References ========== The following references provide the theoretical background and original code for the included methods. Software package detailed description and benchmarking ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you use this code please cite the corresponding papers where original methods appeared (see References below), as well as: .. [CaImAn] Giovannucci A., Friedrich J., Gunn P., Kalfon J., Koay S.A., Taxidis J., Najafi F., Gauthier J.L., Zhou P., Tank D.W., Chklovskii D.B., Pnevmatikakis E.A. (2018). CaImAn: An open source tool for scalable Calcium Imaging data Analysis. eLife 2019;8:e38173. `[paper] `__ Deconvolution and demixing of calcium imaging data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. [CNMF] Pnevmatikakis, E.A., Soudry, D., Gao, Y., Machado, T., Merel, J., … & Paninski, L. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89(2):285-299, `[paper] `__, `[Github repository] `__. .. [SNMF] Pnevmatikakis, E.A., Gao, Y., Soudry, D., Pfau, D., Lacefield, C., … & Paninski, L. (2014). A structured matrix factorization framework for large scale calcium imaging data analysis. arXiv preprint arXiv:1409.2903. `[paper] `__. .. [CNMF_E] Zhou, P., Resendez, S. L., Stuber, G. D., Kass, R. E., & Paninski, L. (2016). Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 2018;7:e28728. `[paper] `__, `[Github repository] `__. .. [OASIS] Friedrich J. and Paninski L. Fast active set methods for online spike inference from calcium imaging. NIPS, 29:1984-1992, 2016. `[paper] `__, `[Github repository] `__. Online Analysis ~~~~~~~~~~~~~~~ .. [OnACID] Giovannucci, A., Friedrich J., Kaufman M., Churchland A., Chklovskii D., Paninski L., & Pnevmatikakis E.A. (2017). OnACID: Online analysis of calcium imaging data in real data. NIPS 2017, pp. 2378-2388. `[paper] `__ .. [OnACID-E] Friedrich J., Giovannucci A. & Pnevmatikakis E.A. (2020). Online analysis of microendoscopic 1-photon calcium imaging data streams. PLoS Comput Biol 17(1):e1008565. `[paper] `__. Motion Correction ~~~~~~~~~~~~~~~~~ .. [NoRMCorre] Pnevmatikakis, E.A., and Giovannucci A. (2017). NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. Journal of Neuroscience Methods, 291:83-92 `[paper] `__, `[Github repository] `__. Behavioral Analysis ~~~~~~~~~~~~~~~~~~~ .. [Behavior] Giovannucci, A., Pnevmatikakis, E. A., Deverett, B., Pereira, T., Fondriest, J., Brady, M. J., … & Masip, D. (2017). Automated gesture tracking in head-fixed mice. Journal of Neuroscience Methods, 300:184-195. `[paper] `__. Variance Stabilization ~~~~~~~~~~~~~~~~~~~~~~ .. [VST] Tepper, M., Giovannucci, A., and Pnevmatikakis, E (2018). Anscombe meets Hough: Noise variance stabilization via parametric model estimation. In ICASSP, 2018. `[paper] `__. `[Github repository] `__ Voltage imaging ~~~~~~~~~~~~~~~~ .. [VolPY] Cai, C. , Friedrich, J. , Pnevmatikakis, E. A. , Podgorski, K. , Giovannucci, A.(2020). VolPy: automated and scalable analysis pipelines for voltage imaging datasets. bioRxiv 2020.01.02.892323 `[paper] `__.