标题： 固有频率分布表征神经动力学 显示英文标题

标题： Intrinsic frequency distribution characterises neural dynamics

摘要： 分解具有特定基本动态的多变量时间序列对于理解、预测和控制非线性时空动态系统（如大脑）至关重要。动态模式分解（DMD）是一种将非线性时空动态分解为几个基本动态（动态模式；DMs）的方法，这些动态模式具有固有频率和衰减率。特别是，与基于傅里叶变换的方法不同，后者用于将单通道信号分解为在规则间隔处具有离散频率的正弦波振幅，DMD可以根据可用数据推导出多通道信号的固有频率；此外，它还可以捕捉非平稳成分，如具有不同固有频率的状态之间的交替。在这里，我们提出使用从DMD中得出的固有频率分布（DM频率）来表征神经活动。评估了健康受试者以及处于静息状态的痴呆症或帕金森病患者的脑电图中DM频率的分布。通过使用这些分布，这些患者比使用由离散傅里叶变换得出的振幅谱更能显著地区分于健康受试者。这一发现表明，DM频率的分布表现出与振幅谱不同的行为，因此，该分布可能作为新的生物标志物，通过表征电生理信号的非线性时空动态来发挥作用。 显示英文摘要

摘要： Decomposing multivariate time series with certain basic dynamics is crucial for understanding, predicting and controlling nonlinear spatiotemporally dynamic systems such as the brain. Dynamic mode decomposition (DMD) is a method for decomposing nonlinear spatiotemporal dynamics into several basic dynamics (dynamic modes; DMs) with intrinsic frequencies and decay rates. In particular, unlike Fourier transform-based methods, which are used to decompose a single-channel signal into the amplitudes of sinusoidal waves with discrete frequencies at a regular interval, DMD can derive the intrinsic frequencies of a multichannel signal on the basis of the available data; furthermore, it can capture nonstationary components such as alternations between states with different intrinsic frequencies. Here, we propose the use of the distribution of intrinsic frequencies derived from DMDs (DM frequencies) to characterise neural activities. The distributions of DM frequencies in the electroencephalograms of healthy subjects and patients with dementia or Parkinson's disease in a resting state were evaluated. By using the distributions, these patients were distinguished from healthy subjects with significantly greater accuracy than when using amplitude spectra derived by discrete Fourier transform. This finding suggests that the distribution of DM frequencies exhibits distinct behaviour from amplitude spectra, and therefore, the distribution may serve as a new biomarker by characterising the nonlinear spatiotemporal dynamics of electrophysiological signals.