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

清醒和睡眠中的振荡：海马脑节律的离散结构

• 批准号: 10226824
• 负责人: Yuri Alexander Dabaghian
• 金额: $ 34.43万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10226824
• 关键词: Abbreviations; Active Learning; Address; Affect; Alpha Rhythm; Animal Behavior; Architecture; Behavior; Behavioral; Biological; Biological Process; Brain; Cephalic; Characteristics; Cognitive; Complex; Complex Analysis; Data; Data Analyses; Electroencephalogram; Female; Fourier Analysis; Fourier Transform; Frequencies; Functional disorder; Goals; Gravitation; Hippocampus (Brain); Individual; Lead; Learning; Link; Mathematics; Methods; Modernization; Nature; Neurons; Noise; Perception; Physiological; Process; Property; Rattus; Reproducibility; Research; Resolution; Role; Shapes; Signal Transduction; Sleep; Source; Structure; Synapses; Techniques; Testing; Time; Wakefulness; Work; approximation theory; base; cognitive process; computerized data processing; data standards; extracellular; gravitational wave; insight; lens; male; neuronal circuitry; neurophysiology; novel; physical model

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.

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， 同步的突触电流这些振荡的动力学是 大脑活动在各个层面的特征：从单个神经元的同步尖峰 和神经元集合到高级认知过程。 LFP数据的生理学解释取决于数学和计算 用于分析的方法。传统上，LFP的振荡性质促使使用傅立叶 方法，在过去的几十年里，这些方法确实主导了LFP研究， 构成了理解脑振荡的唯一系统框架。然而，这些方法不是 非常适合处理生物信号的两个基本属性：噪声和非平稳性， 可能因此模糊了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近似技术的方法， 这使得对LFP振荡的分析更加细致入微。 以前，我们的方法已成功地应用于研究各种物理信号，例如，于检测 引力天线中的引力波将这种方法应用于生物学领域， 立即进行新的观察。具体来说，我们发现海马和皮层LFP 在大鼠中记录的波形由一小部分频率调制波组成，我们称之为双光子。我们 假设脑电波代表脑电波的实际物理结构（例如，θ- 波或γ波），这是以前被传统的，不太强大的技术掩盖。另一个关键 我们的方法的特点是，它拥有一个公正的标记的噪声分量，这使得我们 为了从信号中识别和去除“噪声壳”，然后不仅研究噪声本身， 而且噪声和振荡动力学之间的相互作用。 提出研究的目标就是要对这一新的层面进行范围广泛、细致的研究 通过这个新发现的计算透镜来观察大脑节律的结构。我们预计， 这项工作将使我们从根本上更好地了解清醒时的脑电波结构， 睡眠，并对潜在的神经生理学和认知现象产生新的见解。