Deconvolution of calcium imaging data using marked point processes

Abstract

Calcium imaging has been widely used for measuring spiking activities of neurons. When using calcium imaging, we need to extract summarized information from the raw movie beforehand. Recent studies have used matrix deconvolution for this preprocessing. However, such an approach can neither directly estimate the generative mechanism of spike trains nor use stimulus information that has a strong influence on neural activities. Here, we propose a new deconvolution method for calcium imaging using marked point processes. We consider that the observed movie is generated from a probabilistic model with marked point processes as hidden variables, and we calculate the posterior of these variables using a variational inference approach. Our method can simultaneously estimate various kinds of information, such as cell shape, spike occurrence time, and tuning curve. We apply our method to simulated and experimental data to verify its performance.

Author summary

Calcium imaging is a promising technique that enables the observation of the activities of large neural populations as a movie. Since the measured movie is a large-scale dataset containing a considerable amount of information, we need to apply a preprocessing procedure to extract crucial information from the raw movie for the analysis that follows. Recent studies have adopted matrix decomposition to decompose the observed movie into the product of two matrices: one consisting of cell shapes and the other consisting of calcium florescent time series. This approach can estimate cell locations and activities simultaneously; however, it cannot express some aspects of neural population codes. For instance, this approach cannot incorporate other covariates that may affect the neural population activities. In this paper, we propose a new statistical model for calcium imaging movies and an estimation procedure for this model. To express the random occurrence of spikes occurring in the movie, our model adopts a marked point process, which is used to express sequences of events to which certain characteristic values are attached. Our model can estimate cell shapes, spikes, and tuning curves of cells directly without any additional preprocessing procedure, and it also improves the estimation accuracy compared to the conventional approach.