Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.
Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.
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DOI：
10.1016/j.neuron.2015.11.037
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发表时间：
2016-01-20
期刊：
影响因子：
16.2
通讯作者：
Paninski L
中科院分区：
文献类型：
--
作者：
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
我们提出了一个模块化的方法来分析钙成像记录的大型神经元合奏。我们的目标是同时识别神经元的位置，分离空间重叠的成分，并从钙指标的缓慢动态中去噪和去卷积尖峰活动。我们的方法依赖于一个受约束的非负矩阵分解，表达的时空荧光活性的产品的空间矩阵，编码的空间足迹的每个神经元在光场和时间矩阵，表征的钙浓度的每个神经元随时间的推移。该框架与一种新的约束反卷积方法相结合，该方法从荧光迹线中提取神经活动的估计，以创建需要最小参数调整的时空处理算法。我们通过将其应用于体外和体内多神经元成像数据、全脑光片成像数据和树突状成像数据来证明我们的方法的普遍适用性。
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 multineuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.