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

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特征之一 所有级别的大脑活动：从单个神经元的尖峰到基础网络的活动 高级认知过程。然而，我们对LFP结构和功能的解释取决于 我们用来进行数据分析的技术。用傅立叶变换研究LFP的振荡性 方法，这些方法主导了LFP研究数十年，目前构成了唯一系统的 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问题 生物信号的属性：噪声和非平稳性，因此可能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，以前 用于研究复杂的物理信号(如引力波、磁共振等)。为 LFP振荡的细微差别分析。通过应用这种方法，我们发现海马区和 在啮齿动物身上记录到的皮质LFP由几个频率调制的波组成，我们称之为振荡波。 我们假设这些物体代表了脑电波的实际物理结构，因此 可能掌握着更好地理解学习和记忆的电路机制的关键。另一个 我们方法的主要特征是噪声分量的公正标记，这使得我们能够识别 去掉信号中的“噪声壳”，不仅要调查噪声本身，还要调查 噪声与信号的规则振荡部分之间的相互作用，以及它们与神经元的相互作用 扣球等。 由于阿尔茨海默病(AD)的特点是振荡性和随机性的改变 海马网的活动，对更好地理解AD诱导的病理的追求符合 理想情况下，我们方法的优势所在。我们的目标是用它来研究AD和AD的电路机制 学习如何通过我们的方法来操纵网络活动。

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.