Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

Neuron 2016 Jan 7;89(2):285-99. Epub 2016 Jan 7.

Department of Statistics, Center for Theoretical Neuroscience, and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Neuroscience and Kavli Institute of Brain Science, Columbia University, New York, NY 10032, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA; Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY 10027, USA. Electronic address:

We present a modular approach for 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 from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

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http://dx.doi.org/10.1016/j.neuron.2015.11.037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881387PMC
January 2016
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