Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo
Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo
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DOI：
10.1038/ncomms12190
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发表时间：
2016-07-01
影响因子：
16.6
通讯作者：
Vanzetta, Ivo
中科院分区：
文献类型：
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作者：
Deneux, Thomas;Kaszas, Attila;Vanzetta, Ivo
从大规模双光子记录中提取神经元尖峰活动仍然具有挑战性，特别是在体内的哺乳动物中，大噪声经常污染信号。我们提出了一种方法MLspike，它返回所测量的钙荧光背后最有可能的尖峰序列。它依赖于一个生理模型，包括基线波动和合成和遗传编码指标的明显非线性。模型参数可以由用户提供或从数据本身估计。MLspike由于其原始的概率表示离散化而具有计算效率;此外，它还可以返回尖峰概率或样本。以广泛的模拟和来自七种不同制剂的真实的数据为基准，它优于最先进的算法。结合从系统的数据调查（噪声水平，尖峰率等），光子噪声不一定是主要的限制因素，我们的方法允许尖峰提取大规模的记录，如在声光三维记录超过1,000个神经元在体内。
Extracting neuronal spiking activity from large-scale two-photon recordings remains challenging, especially in mammals in vivo, where large noises often contaminate the signals. We propose a method, MLspike, which returns the most likely spike train underlying the measured calcium fluorescence. It relies on a physiological model including baseline fluctuations and distinct nonlinearities for synthetic and genetically encoded indicators. Model parameters can be either provided by the user or estimated from the data themselves. MLspike is computationally efficient thanks to its original discretization of probability representations; moreover, it can also return spike probabilities or samples. Benchmarked on extensive simulations and real data from seven different preparations, it outperformed state-of-the-art algorithms. Combined with the finding obtained from systematic data investigation (noise level, spiking rate and so on) that photonic noise is not necessarily the main limiting factor, our method allows spike extraction from large-scale recordings, as demonstrated on acousto-optical three-dimensional recordings of over 1,000 neurons in vivo.