maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

Abstract

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.