Oscillons in Wakefulness and in Sleep: Discrete Structure of Hippocampal Brain Rhythms

摘要 大脑中的神经元被淹没在振荡的细胞外局部场电位(LFP)中，由 同步的突触电流。这些振荡的动力学是主要的原因之一 大脑各个层次的活动特征：来自单个神经元的同步放电 以及神经元集合到高级认知过程中。 对LFP数据的生理学解释依赖于数学和计算 用于其分析的方法。传统上，LFP的振荡性质使用傅里叶变换来激励 方法，这些方法在过去几十年中确实主导了LFP研究，目前 构成了理解大脑振荡的唯一系统框架。然而，这些方法并不是 非常适合处理生物信号的两个基本属性：噪声和非平稳性，以及 因此可能会模糊大脑节律的实际生理结构。为了解决这个问题， 我们开发了一种基于Padé逼近技术的方法--一种强大的新技术 这使得对LFP振荡的分析更加细致入微。 以前，我们的方法被成功地应用于各种物理信号的研究，例如检测 引力天线中的引力波。将这种方法应用于生物领域也将引导我们 立即进行新的观测。具体地说，我们发现海马区和大脑皮层的LFP 在大鼠身上记录的由一小组频率调制的波组成，我们称之为振荡子。我们 假设振子代表脑波的实际物理结构(例如，θ- WAVE或γ-WAVE)，以前被传统的、不太强大的技术所掩盖。另一把钥匙 我们方法的特点是它拥有一个公正的噪声分量标记，这使得我们能够 为了识别并去除信号中的“噪声外壳”，然后不仅要调查噪声本身， 还包括噪声和振荡动力学之间的相互作用。 拟议研究的目标是对这一新层次进行广泛的详细研究 通过这种新发现的计算透镜了解大脑节律的结构。我们预计我们的 这项工作将使我们从根本上更好地理解清醒和健康状态下的脑电波结构。 睡眠，并对潜在的神经生理和认知现象产生新的见解。

ABSTRACT Neurons in the brain are submerged into oscillating extracellular Local Field Potential (LFP) created by the synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of the brain activity at all levels: from the synchronized spiking of the individual neurons and neuronal ensembles to the high-level cognitive processes. A physiological interpretation of the LFP data depends on the mathematical and computational approaches used for its analysis. Traditionally, the oscillatory nature of LFP motivates using Fourier methods, which have indeed dominated LFP research for the last several decades and currently constitute the only systematic framework for understanding brain oscillations. Yet these methods are not well suited for handling two fundamental attributes of biological signals: noise and nonstationarity, and may therefore obscure the actual physiological structure of the brain rhythms. To address this problem, we developed an approach based on the Padé Approximation techniques—a powerful novel technique that allows a much more nuanced analysis of the LFP oscillations. Previously, our method was successfully applied to studying various physical signals, e.g., to detecting gravitational waves in gravitational antennas. Applying this method in biological realm also lead us immediately to new observations. Specifically, we discovered that the hippocampal and the cortical LFPs recorded in rats consist of a small set of frequency-modulated waves, which we call oscillons. We hypothesize that oscillons represent the actual, physical structure of the brain waves (such as, e.g., θ- wave or γ-waves) that was previously obscured by the traditional, less powerful techniques. Another key feature of our method is that it possesses an impartial marker of the noise component, which allows us to identify and remove the “noise shell” from the signal and then to investigate not only the noise itself, but also the interplay between the noise and the oscillatory dynamics. The goal of the proposed research is to carry and extensive scope of detailed studies of this new level of the brain rhythms’ structure through this newly discovered computational lens. We anticipate that our work will lead us a fundamentally better understanding of the brain wave structure in wakefulness and in sleep, and produce new insights into the underlying neurophysiological and cognitive phenomena.