Lin Shih-Chieh, Gervasoni Damien
Electrical activity is essential for neuronal communication. Over the years, in vivo multielectrode recordings have revealed that the electrical activities of individual neurons are not independent of each other. Instead, neurons tend to fire in a coordinated way within a given neural network. When measured as the electroencephalogram (EEG) or local field potential (LFP) signals, this neural coordination results in complex oscillatory activity patterns, which reflect synchronous synaptic potentials in a local network (Lopes da Silva 1991). Thus, unveiling the physiological mechanisms generating such complex oscillatory neural activity patterns is key to achieving a better understanding of how the brain operates in behaving animals. The dynamics of the forebrain is not random. Ever since the initial discovery of cerebral electrical activity by Caton (Caton 1875) in rabbits and monkeys, and later in humans by Berger (Berger 1929), different patterns of forebrain activity have been tightly linked to various behavioral and wake-sleep states. Indeed, these distinct patterns of neural activity have become incorporated as part of the criteria of wake-sleep states (Green and Arduini 1954; Rechtschaffen and Kales 1968; Lopes da Silva and van Leeuwen 1969; Timo-Iaria et al. 1970; Moruzzi 1972; Winson 1972; Winson 1974; Gottesmann 1992; Steriade et al. 1993), suggesting that forebrain dynamics fall into several different regimes. This observation is intriguing because the same neural circuit can support several different dynamic regimes, which likely serve distinct roles in information processing and storage. Therefore, a quantitative description of its network dynamics can further reveal how the forebrain underlies so many fundamental functions in mammals. In this chapter, we first describe the forebrain oscillatory activity patterns associated with different wake-sleep states, and highlight limitations of existing state identification methods. Then, we introduce a novel state-space framework (Gervasoni et al. 2004) that we have employed to quantitatively describe global forebrain dynamics in rodents. Such an analysis revealed several distinct regimes in which the forebrain can operate. These regimes correspond to distinct global brain states and are correlated with the occurrence of major wake-sleep states observed in both rats and mice. In addition, the state-space framework proposed here has allowed us to characterize the gradient dynamics within global brain states, providing a quantitative description of state transition dynamics in rodents. We end this chapter by discussing the underlying driving forces and potential functional roles of global brain states.
电活动对于神经元通讯至关重要。多年来，体内多电极记录表明，单个神经元的电活动并非相互独立。相反，在给定神经网络中，神经元倾向于以协同方式放电。当作为脑电图（EEG）或局部场电位（LFP）信号进行测量时，这种神经协调会产生复杂的振荡活动模式，这反映了局部网络中的同步突触电位（洛佩斯·达席尔瓦，1991年）。因此，揭示产生这种复杂振荡神经活动模式的生理机制是更好地理解大脑在行为动物中如何运作的关键。前脑的动态并非随机。自从卡顿（卡顿，1875年）在兔子和猴子中首次发现大脑电活动，以及后来伯格（伯格，1929年）在人类中发现以来，前脑活动的不同模式一直与各种行为和睡眠 - 觉醒状态紧密相连。事实上，这些不同的神经活动模式已成为睡眠 - 觉醒状态标准的一部分（格林和阿尔杜伊尼，1954年；雷奇沙芬和卡莱斯，1968年；洛佩斯·达席尔瓦和范·李文，1969年；蒂莫 - 伊拉里亚等人，1970年；莫鲁齐，1972年；温森，1972年；温森，1974年；戈特斯曼，1992年；斯特里亚德等人，1993年），这表明前脑动态可分为几种不同的状态。这一观察结果很有趣，因为相同的神经回路可以支持几种不同的动态状态，这些状态可能在信息处理和存储中发挥不同的作用。因此，对其网络动态进行定量描述可以进一步揭示前脑如何构成哺乳动物中如此多基本功能的基础。在本章中，我们首先描述与不同睡眠 - 觉醒状态相关的前脑振荡活动模式，并强调现有状态识别方法的局限性。然后，我们介绍一种新颖的状态空间框架（热尔瓦索尼等人，2004年），我们已用它来定量描述啮齿动物的全局前脑动态。这样的分析揭示了前脑可以运作的几种不同状态。这些状态对应于不同的全局脑状态，并与在大鼠和小鼠中观察到的主要睡眠 - 觉醒状态的出现相关。此外，这里提出的状态空间框架使我们能够表征全局脑状态内的梯度动态，从而对啮齿动物的状态转换动态进行定量描述。我们通过讨论全局脑状态的潜在驱动力和潜在功能作用来结束本章。
