A structured matrix factorization framework for large scale calcium imaging data analysis

TL;DR

This paper introduces a structured matrix factorization method for analyzing large-scale calcium imaging data, enabling simultaneous neuron localization, demixing, and spike deconvolution with minimal parameter tuning.

Contribution

The authors develop a novel, constrained matrix factorization framework that automatically estimates calcium dynamics and noise, improving analysis of large neuronal imaging datasets.

Findings

Effective in vivo application to large-scale neuronal data

Simultaneous localization and activity extraction of neurons

Enhanced performance with initialization and post-processing techniques

Abstract

We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity of each neuron from the slow dynamics of the calcium indicator. The matrix factorization approach relies on the observation that the spatiotemporal fluorescence activity can be expressed as a product of two matrices: a spatial matrix that encodes the location of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. We present a simple approach for estimating the dynamics of the calcium indicator as well as the observation noise statistics from the observed data. These parameters are then used to set up the matrix factorization problem in a constrained form that requires no further parameter tuning. We discuss initialization and post-processing techniques that enhance the performance of our method, along with efficient and largely parallelizable algorithms. We apply our method to {\it in vivo} large scale multi-neuronal imaging data and also demonstrate how similar methods can be used for the analysis of {\it in vivo} dendritic imaging data.