Brunton Bingni W, Johnson Lise A, Ojemann Jeffrey G, Kutz J Nathan
Department of Biology, University of Washington, Seattle, WA 98195, USA; Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA; Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA.
Center for Sensorimotor Neural Engineering, University of Washington, Seattle, WA 98195, USA; Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA.
J Neurosci Methods. 2016 Jan 30;258:1-15. doi: 10.1016/j.jneumeth.2015.10.010. Epub 2015 Oct 31.
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.
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.
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.
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.
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.
神经科学领域广泛需要理解和可视化神经活动的大规模记录，即由数十或数百个电极在数分钟到数小时内记录动态脑活动所获取的大数据。此类数据集具有跨空间和时间的连贯模式，但现有的计算方法通常仅限于分别在空间或时间上进行分析。
在此，我们报告了将动态模态分解（DMD）（一种最初为研究流体物理学而开发的算法）应用于大规模神经记录的情况。DMD是一种模态分解算法，它使用耦合的时空模式来描述高维动态数据。该算法对噪声和子采样率的变化具有鲁棒性；它可以轻松扩展到同时获取的大量测量数据。
我们首先在执行已知运动任务的人类受试者的硬膜下电极阵列记录上验证了DMD方法。接下来，我们将DMD与无监督聚类相结合，开发了一种在睡眠期间提取纺锤体网络的新方法。我们发现了几个不同的睡眠纺锤体网络，可通过其典型的皮质分布模式、频率和持续时间来识别。
DMD与主成分分析（PCA）和离散傅里叶变换（DFT）密切相关。我们可以将DMD视为低维PCA空间的一种旋转，使得每个基向量都具有连贯的动态。
由此产生的分析结合了在空间中执行PCA和在时间上进行功率谱分析的关键特征，使其特别适合于分析大规模神经记录。
