Defining Global Brain States Using Multielectrode Field Potential Recordings

Excerpt

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.