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

Abstract：

: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.

journal_name

Neuron

journal_title

Neuron

authors

Pnevmatikakis EA,Soudry D,Gao Y,Machado TA,Merel J,Pfau D,Reardon T,Mu Y,Lacefield C,Yang W,Ahrens M,Bruno R,Jessell TM,Peterka DS,Yuste R,Paninski L

doi

10.1016/j.neuron.2015.11.037

subject

Has Abstract

pub_date

2016-01-20 00:00:00

pages

285-99

issue

2

eissn

0896-6273

issn

1097-4199

pii

S0896-6273(15)01084-3

journal_volume

89

pub_type

杂志文章

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