Getting Started with CaImAn

Demos

  • Notebooks: The notebooks provide a simple and friendly way to get into CaImAn and understand its main characteristics. They are located in the demos/notebooks. To launch one of the jupyter notebooks:

    source activate CaImAn
    jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10
    

    and select the notebook from within Jupyter’s browser. The argument --NotebookApp.iopub_data_rate_limit=1.0e10 will prevent any memory issues while plotting on a notebook.

  • demo files are also found in the demos/general subfolder. We suggest trying demo_pipeline.py first as it contains most of the tasks required by calcium imaging. For behavior use demo_behavior.py

  • If you want to directly launch the python files, your python console still must be in the CaImAn directory.

Basic Structure

We recently refactored the code to simplify the parameter setting and usage of the various algorithms. The code now is based revolves around the following objects:

  • params: A single object containing a set of dictionaries with the parameters used in all the algorithms. It can be set and changed easily and is passed into all the algorithms.
  • MotionCorrect: An object for motion correction which can be used for both rigid and piece-wise rigid motion correction.
  • cnmf: An object for running the CaImAn batch algorithm either in patches or not, suitable for both two-photon (CNMF) and one-photon (CNMF-E) data.
  • online_cnmf: An object for running the CaImAn online (OnACID) algorithm on two-photon data with or without motion correction.
  • estimates: A single object that stores the results of the algorithms (CaImAn batch, CaImAn online) in a unified way that also contains plotting methods.

To see examples of how these methods are used, please consult the demos.

Parameters

CaImAn gives you access to a lot of parameters and lets you adapt the analysis to your data. Parameters are stored in the params object in a set of dictionaries, sorted by the part of the analysis they are used in:

  • data: General params describing the dataset like dimensions, decay time, filename and framerate
  • init: Parameters for component initialization like neuron size gSig, patch size etc.
  • motion: motion correction parameters (max shift size, patch size etc.)
  • online: Parameters specific for the online OnACID algorithm
  • quality: Parameters for component evaluation (spatial correlation, SNR and CNN)
  • spatial: Parameters used in detection of spatial components
  • temporal: Parameters used in extraction of temporal components and deconvolution

Of these parameters, most have a default value that usually does not have to be adjusted. However, some parameters are crucial to be adapted to the specific dataset for proper analysis performance:

  • fnames: List of paths to the file(s) to be analysed. Memmap and hdf5 result files will be saved in the same directory.

  • fr: Imaging frame rate in frames per second.

  • decay_time: Length of a typical transient in seconds. decay_time is an approximation of the time scale over which to expect a significant shift in the calcium signal during a transient. It defaults to 0.4, which is appropriate for fast indicators (GCaMP6f), slow indicators might use 1 or even more. However, decay_time does not have to precisely fit the data, approximations are enough.

  • p: Order of the autoregressive model. p = 0 turns deconvolution off. If transients in your data rise instantaneously, set p = 1 (occurs at low sample rate or slow indicator). If transients have visible rise time,

    set p = 2. If the wrong order is chosen, spikes are extracted unreliably.

  • nb: Number of global background components. This is a measure of the complexity of your background noise. Defaults to nb = 2, assuming a relatively homogeneous background. nb = 3 might fit for more complex noise, nb = 1 is usually too low. If nb is set too low, extracted traces appear too noisy, if nb is set too high, neuronal signal starts getting absorbed into the background reduction, resulting in reduced transients.

  • merge_thr: Merging threshold of components after initialization. If two components are correlated more than this value (e.g. when during initialization a neuron was split in two components), they are merged and treated as one.

  • rf: Half-size of the patches in pixels. Should be at least 3 to 4 times larger than the expected neuron size to capture the complete neuron and its local background. Larger patches lead to less parallelization.

  • stride: Overlap between patches in pixels. This should be roughly the neuron diameter. Larger overlap increases computational load, but yields better results during reconstruction/denoising of the data.

  • K : Number of (expected) components per patch. Adapt to rf and estimated component density.

  • gSig: Expected half-size of neurons in pixels [rows X columns]. CRUCIAL parameter for proper component detection.

  • method_init: Initialization method, depends mainly on the recording method. Use greedy_roi for 2p data, corr_pnr for 1p data, and sparse_nmf for dendritic/axonal data.

  • ssub/tsub: Spatial and temporal subsampling during initialization. Defaults to 1 (no compression). Can be set to 2 or even higher to save resources, but might impair detection/extraction quality.

Component evaluation

The quality of detected components is evaluated with three parameters:

  • Spatial footprint consistency (rval): The spatial footprint of the component is compared with the frames where this component is active. Other component’s signals are subtracted from these frames, and the resulting raw data is correlated against the spatial component. This ensures that the raw data at the spatial footprint aligns with the extracted trace.
  • Trace signal-noise-ratio (SNR): Peak SNR is calculated from strong calcium transients and the noise estimate.
  • CNN-based classifier (cnn): The shape of components is evaluated by a 4-layered convolutional neural network trained on a manually annotated dataset. The CNN assigns a value of 0-1 to each component depending on its resemblance to a neuronal soma.

Each parameter has a low threshold (rval_lowest (default -1), SNR_lowest (default 0.5), cnn_lowest (default 0.1)) and high threshold (rval_thr (default 0.8), min_SNR (default 2.5), min_cnn_thr (default 0.9)). A component has to exceed ALL low thresholds as well as ONE high threshold to be accepted.

Additionally, CNN evaluation can be turned off completely with the use_cnn boolean parameter. This might be useful when working with manually annotated spatial components (seeded CNMF (link to notebook?)), where it can be assumed that manually registered ROIs already have a neuron-like shape.

Result Interpretation

As mentioned above, the results of the analysis are stored within the estimates objects. The basic entries are the following:

Result variables for 2p batch analysis

The results of CaImAn are saved in an estimates object. This is stored inside the cnmf object, i.e. it can be accessed using cnmf.estimates. The variables of interest are:

  • estimates.A: Set of spatial components. Saved as a sparse column format matrix with dimensions (# of pixels X # of components). Each column corresponds to a spatial component.
  • estimates.C: Set of temporal components. Saved as a numpy array with dimensions (# of components X # of timesteps). Each row corresponds to a background component denoised and deconvolved.
  • estimates.b: Set of background spatial components (for 2p analysis): Saved as a numpy array with dimensions (# of pixels X # of components). Each column corresponds to a spatial background component.
  • estimates.f: Set of temporal background components (for 2p analysis). Saved as a numpy array with dimensions (# of background components X # of timesteps). Each row corresponds to a temporal background component.
  • estimates.S: Deconvolved neural activity (spikes) for each component. Saved as a numpy array with dimensions (# of background components X # of timesteps). Each row corresponds to the deconvolved neural activity for the corresponding component.
  • estimates.YrA: Set of residual components. Saved as a numpy array with dimensions (# of components X # of timesteps). Each row corresponds to the residual signal after denoising the corresponding component in estimates.C.
  • estimates.F_dff: Set of DF/F normalized temporal components. Saved as a numpy array with dimensions (# of components X # of timesteps). Each row corresponds to the DF/F fluorescence for the corresponding component.

To view the spatial components, their corresponding vectors need first to be reshaped into 2d images. For example if you want to view the i-th component you can type

import matplotlib.pyplot as plt
plt.figure(); plt.imshow(np.reshape(estimates.A[:,i-1].toarray(), dims, order='F'))

where dims is a list or tuple that has the dimensions of the FOV. To get binary masks from spatial components you can apply a threshold before reshaping:

M = estimates.A > 0
masks = [np.reshape(np.array(M[:,i]), dims, order=‘F') for i in range(M.shape[1])]

Similarly if you want to plot the trace for the i-th component you can simply type

plt.figure(); plt.plot(estimates.C[i-1])

The methods estimates.plot_contours and estimates.view_components can be used to visualize all the components.

Variables for component evaluation

If you use post-screening to evaluate the quality of the components and remove bad components the results are stored in the lists: - idx_components: List containing the indexes of accepted components. - idx_components_bad: List containing the indexes of rejected components.

These lists can be used to index the results. For example estimates.A[:,idx_components] or estimates.C[idx_components] will return the accepted spatial or temporal components, respectively. If you want to view the first accepted component you can type

plt.figure(); plt.imshow(np.reshape(estimates.A[:,idx_components[0]].toarray(), dims, order='F'))
plt.figure(); plt.plot(cnm.estimates.C[idx_components[0]])

Variables for 1p processing (CNMF-E)

The variables for one photon processing are the same, with an additional variable estimates.W for the matrix that is used to compute the background using the ring model, and estimates.b0 for the baseline value for each pixel.

Variables for online processing

The same estimates object is also used for the results of online processing, stored in onacid.estimates.