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: 10395559
• 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/10395559
• 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.

抽象的 大脑中的神经元淹没在由以下因素产生的振荡细胞外局部场电位 (LFP) 中： 这些振荡的动态是主要的之一。 各级大脑活动的特征：来自各个神经元的同步尖峰 和神经元集成到高级认知过程。 LFP 数据的生理学解释取决于数学和计算 传统上，LFP 的振荡性质促使人们使用傅里叶分析。 方法，在过去几十年中确实主导了 LFP 研究，目前 然而，这些方法并不是理解大脑振荡的唯一系统框架。 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 因此可能会掩盖大脑节律的实际生理结构。 我们开发了一种基于帕德近似技术的方法——一种强大的新技术 这样可以对 LFP 振荡进行更细致的分析。 此前，我们的方法已成功应用于研究各种物理信号，例如检测 引力天线中的引力波也引导我们在生物领域应用这种方法。 具体来说，我们发现海马和皮质 LFP。 在老鼠身上记录的信号由一小组调频波组成，我们称之为振荡。 认为振荡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、不太强大的技术所掩盖的另一个关键。 我们方法的特点是它拥有噪声分量的公正标记，这使我们能够 识别并消除信号中的“噪声壳”，然后不仅研究噪声本身， 还有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水平进行广泛的详细研究 我们期望通过这个新发现的计算镜头来了解大脑节律的结构。 这项工作将使我们从根本上更好地理解清醒状态和睡眠状态下的脑电波结构。 睡眠，并对潜在的神经生理学和认知现象产生新的见解。