Abstract
In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.
Highlights
In vivo calcium imaging of activities from large neural populations at single cell resolution has become a widely used technique among experimental neuroscientists
Calcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations
W is an appropriate sparse weight matrix, where Wij is constrained to Wij = 0 if dist(xi, xj) 2= [l, l + 1[, we model the background at one pixel as a linear combination of the background fluorescence in pixels which are chosen to be on a ring with radius l [6]
Summary
In vivo calcium imaging of activities from large neural populations at single cell resolution has become a widely used technique among experimental neuroscientists. The reason is that the ring model of CNMF-E aims to capture aspects of the point spread function (PSF) which is largely invariant with respect to the location within the local environment of each neuron To test this hypothesis we used a very simple convolutional neural network (CNN) with ring shaped kernels to capture the background structure. The intuition behind the convolution is straightforward: if all the rows of the W matrix had the same non-zero entries (but centered around different points) the application of W would correspond to a simple spatial convolution with the common “ring” as the filter In our case this model is not expressive enough to adequately fit the background, in particular it fails to capture pixel dependent brightness differences, and by assuming shift invariance it fails to capture that the PSF can vary when compared across the full FOV. We investigated parametrizing the background model with a slightly more complex model, which we refer to as “Ring-CNN”
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