To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data.

Background: With the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step.

Methods: In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding.

Results: We found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods.

Comparison With Existing Methods: Our simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods.

Conclusion: We conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.