Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
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DOI：
10.1016/j.jneumeth.2015.10.010
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发表时间：
2016-01-30
影响因子：
3
通讯作者：
Kutz, J. Nathan
中科院分区：
文献类型：
--
作者：
Brunton, Bingni W.;Johnson, Lise A.;Kutz, J. Nathan
背景：神经科学的需求广泛，以了解和可视化神经活动的大规模记录，数十次或数百个电极在几分钟到数小时内记录动态大脑活动的大数据。此类数据集的特征是在空间和时间上都具有连贯的模式，但是现有的计算方法通常仅限于空间或时间分析的分析。新方法：在这里，我们报告了动态模式分解的适应（DMD），这是一种最初开发的算法，用于研究液体物理学，以进行大规模的神经记录。 DMD是一种模态分解算法，它使用耦合的空间时间模式描述了高维动态数据。该算法对噪声和亚采样率的变化是可靠的。它很容易地缩放到大量同时获得的测量值。回报：我们首先验证了从人类受试者中执行已知运动任务的人类受试者的DMD方法。接下来，我们将DMD与无监督的聚类相结合，开发了一种新的方法来提取睡眠期间的纺锤体网络。我们通过刻板的皮质分布模式，频率和持续时间来识别几个不同的睡眠主轴网络。我们可能认为DMD是低维PCA空间的旋转，因此每个基矢量都具有连贯的动力学。结论：结果分析结合了在空间和功率频谱分析中执行PCA的关键特征，使其特别适合分析。大规模神经记录。 （c）2015 Elsevier B.V.保留所有权利。
Background: There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately.New method: Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements.Results: We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration.Comparison with existing methods: DMD is closely related to principal components analysis (PCA) and discrete Fourier transform (DFT). We may think of DMD as a rotation of the low-dimensional PCA space such that each basis vector has coherent dynamics.Conclusions: The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings. (C) 2015 Elsevier B.V. All rights reserved.