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

基本信息

批准号：
10647725
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
项目类别：
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/10647725
关键词：
APP-PS1 Abbreviations Affect Age Alzheimer&apos s Disease Alzheimer&apos s disease model Animals Architecture Behavior Biological Brain Brain Diseases Cessation of life Characteristics Complex Coupling Data Data Analyses Dedications Disease Progression Elements Frequencies Genotype Goals Hippocampus 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 Rat Transgene Rattus Research Rodent Rodent Diseases Signal Transduction Sleep Source Structure Synapses Techniques Testing Thinness Time Transgenic Organisms Work age group cognitive process epileptiform extracellular gravitational wave insight learned behavior lens middle age mouse model neuron component non rapid eye movement novel rapid eye movement sex

项目摘要

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.

抽象的大脑中的神经元被浸入振荡的细胞外局部场电位（LFP）。同步合成电流。这些振荡的动力是主要特征之一各个级别的大脑活动：从单个神经元的尖峰到基础的网络活动高级认知过程。但是，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的振荡性是使用傅立叶的数十年来一直主导LFP研究的方法，目前构成了唯一的系统性理解“大脑节奏”的框架。然而，这些方法处理两个基本的方法很差生物信号的属性：噪声和非平稳性，因此可能掩盖了该结构 LFP数据及其物理含义。我们最近改编了一种强大的技术用于研究复杂的物理信号（例如，引力波，磁共振等） LFP振荡的细微分析。通过应用这种方法，我们发现海马和记录在啮齿动物中的皮质LFP由一些频率调节的波组成，我们称之为oscillons。我们假设这些对象代表了脑波的实际物理结构，因此可能会有钥匙更好地了解学习和记忆的电路机制。其他我们方法的主要特征是噪声组件的公正标记，它使我们能够识别并从信号中删除“噪声外壳”，然后不仅要调查噪声本身，还会调查噪声和信号的常规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氏病（AD）的特征是振荡和随机性改变在海马网络中的活动，寻求更好地了解AD诱导的病理理想情况下，我们方法的优势。我们的目标是将其用于研究AD的电路机制和学会通过我们的方法来操纵网络活动。