Waves and noise in hippocampo-cortical circuit: a study of Alzheimer's disease

海马皮质回路中的波和噪声：阿尔茨海默病的研究

• 批准号: 10468177
• 负责人: Yuri Alexander Dabaghian
• 金额: $ 39.46万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10468177
• 关键词: APP-PS1; Abbreviations; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease model; Animals; Architecture; Behavior; Biological; Brain; Brain Diseases; Cessation of life; Characteristics; Complex; Coupling; Data; Data Analyses; Disease Progression; Elements; Epilepsy; Frequencies; Genotype; Goals; Hippocampus (Brain); Individual; Learning; Link; Longevity; Magnetic Resonance; Memory; Methodology; Methods; Molecular; Mus; Nature; Nerve Degeneration; Neuronal Dysfunction; Neurons; Noise; Pathology; Patients; Pattern; Performance; Physiological; Population; Process; Property; Quality of life; Rattus; Research; Rodent; Rodent Diseases; Signal Transduction; Source; Structure; Synapses; Techniques; Testing; Thinness; Time; Transgenic Organisms; Work; age group; cognitive process; extracellular; gravitational wave; insight; learned behavior; lens; middle age; mouse model; non rapid eye movement; novel; sex

ABSTRACT Neurons in the brain are submerged into oscillating extracellular Local Field Potential (LFP) created by synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of the brain activity at all levels: from the individual neurons’ spiking to the activity of networks that underlie high-level cognitive processes. However, our interpretation of the LFP structure and functions depend on the techniques that we use for data analyses. The oscillatory nature of LFP motivates using Fourier methods, which have dominated LFP research for decades and currently constitute the only systematic framework for understanding the “brain rhythms.” Yet these methods poorly handle two fundamental attributes of biological signals: noise and non-stationarity, and may therefore obscure the structure of the LFP data and its physiological meaning. We have recently adapted a powerful technique that previously applied to studying complex physical signals (e.g., gravitational waves, magnetic resonances, etc.) for nuanced analysis of the LFP oscillations. By applying this method, we discovered that hippocampal and cortical LFPs recorded in rodents consist of a few frequency-modulated waves, which we call Oscillons. We hypothesize that these objects represent the actual, physical structure of the brain waves and hence may hold keys to better understanding of the circuit mechanisms of learning and memory. Another principal feature of our method is 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 interplays between the noise and the regular, oscillatory part of the signal, their interactions with neuronal spiking, etc. Since Alzheimer’s Disease (AD) is characterized by alterations in both the oscillatory and stochastic activity in the hippocampal network, the quest of better understanding of AD-induced pathologies fits ideally the strengths of our approach. Our goal is to use it for studying the circuit mechanisms of AD and to learn to manipulate the network activity through our methodology.

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， 同步突触电流这些振荡的动力学特性是 从单个神经元的尖峰信号到构成大脑活动基础的网络活动， 高级认知过程然而，我们对LFP结构和功能的解释取决于 我们用于数据分析的技术。LFP的振荡性质使用傅立叶激励 方法，这已经主导了LFP研究几十年来，目前构成了唯一的系统 理解“大脑节律”的框架。然而，这些方法不能很好地处理两个基本的 生物信号的属性：噪声和非平稳性，因此可能会模糊生物信号的结构。 LFP数据及其生理意义。我们最近采用了一种强大的技术， 应用于研究复杂的物理信号（例如，引力波、磁共振等）为 LFP振荡的细致分析。通过应用这种方法，我们发现海马和 在啮齿动物中记录的皮层LFP由几个频率调制波组成，我们称之为Oscillons。 我们假设这些物体代表了脑电波的实际物理结构， 可能是更好地理解学习和记忆回路机制的关键。另一 我们的方法的主要特征是噪声分量的公正标记，这使我们能够识别 从信号中去除“噪声外壳”，然后不仅研究噪声本身， 噪声和信号的规则振荡部分之间的相互作用，它们与神经元的相互作用， 尖峰等。 由于阿尔茨海默病（AD）的特征在于振荡和随机信号的改变， 活动在海马网络，寻求更好地了解AD诱导的病理符合 最好是我们方法的优势。我们的目标是用它来研究AD的电路机制， 通过我们的方法学来操纵网络活动。