A zero-inflated gamma model for deconvolved calcium imaging traces
This work addresses a gap in statistical modeling for calcium imaging data, benefiting neuroscientists by enhancing the accuracy of neural activity analysis, though it is incremental as it builds on existing pre-processing methods.
The authors tackled the problem of statistically modeling deconvolved calcium imaging signals, which are crucial for interpreting neural activity, by proposing a zero-inflated gamma (ZIG) model. They found that the ZIG model outperformed simpler models like Poisson or Bernoulli in both simulated and real neural data, improving performance in neural encoding and decoding tasks.
Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing "pre-processing" tools for calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, statistical modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements, and for incorporating these observations into downstream probabilistic encoding and decoding models. Surprisingly, these issues have to date received significantly less attention. In this work we examine the statistical properties of the deconvolved activity estimates, and compare probabilistic models for these random signals. In particular, we propose a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass that serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model outperforms simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.