RT Journal Article
SR Electronic
T1 A zero-inflated gamma model for post-deconvolved calcium imaging traces
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 637652
DO 10.1101/637652
A1 Xue-Xin Wei
A1 Ding Zhou
A1 Andres Grosmark
A1 Zaki Ajabi
A1 Fraser Sparks
A1 Pengcheng Zhou
A1 Mark Brandon
A1 Attila Losonczy
A1 Liam Paninski
YR 2019
UL http://biorxiv.org/content/early/2019/05/14/637652.abstract
AB Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing “pre-processing” tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model out-performs 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.