Mathematics and Statistics, Boston University, Boston, United States.
Department of Psychiatry, University of Minnesota, Minneapolis, United States.
Elife. 2021 Sep 27;10:e68803. doi: 10.7554/eLife.68803.
Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.
脑节律被认为有助于大脑功能，低频节律的相位尤其具有重要作用。要理解相位在神经功能中的作用，需要在目标相位上进行干扰神经活动的干预，这就需要实时估计相位。当前实时相位估计的方法依赖于带通滤波，它假设窄带信号并在相位估计中耦合信号和噪声，从而增加相位噪声并损害相位与行为之间关系的检测。为了解决这个问题，我们提出了一种用于实时跟踪相位的状态空间相位估计器。通过将解析信号作为潜在状态进行跟踪，该框架避免了带通滤波的要求，分别对信号和噪声进行建模，考虑了节律性混杂因素，并为相位估计提供了可信区间。我们在模拟中证明，在常见混杂因素（如宽带节律、相位重置和同时发生的节律）的情况下，状态空间相位估计器优于当前最先进的实时方法。最后，我们展示了该方法在体内数据中的应用。该方法可作为 Open Ephys 采集系统的即用型插件使用，使其广泛可用于实验。
