Global Forebrain Dynamics Predict Rat Behavioral States and Their Transitions

Abstract

The wake-sleep cycle, a spontaneous succession of global brain states that correspond to major overt behaviors, occurs in all higher vertebrates. The transitions between these states, at once rapid and drastic, remain poorly understood. Here, intracranial local field potentials (LFPs) recorded in the cortex, hippocampus, striatum, and thalamus were used to characterize the neurophysiological correlates of the rat wake-sleep cycle. By way of a new method for the objective classification and quantitative investigation of all major brain states, we demonstrate that global brain state transitions occur simultaneously across multiple forebrain areas as specific spectral trajectories with characteristic path, duration, and coherence bandwidth. During state transitions, striking changes in neural synchronization are effected by the prominent narrow-band LFP oscillations that mark state boundaries. Our results demonstrate that distant forebrain areas tightly coordinate the processing of neural information during and between global brain states, indicating a very high degree of functional integration across the entire wake-sleep cycle. We propose that transient oscillatory synchronization of synaptic inputs, which underlie the rapid switching of global brain states, may facilitate the exchange of information within and across brain areas at the boundaries of very distinct neural processing regimens.

Abnormal Sleep/Wake Dynamics in Orexin Knockout Mice

“…While this is a reasonable assumption for OXKO mice, it will be important to confirm these results in other rodent models of narcolepsy, such as ataxin-3 mice. 31 previous work has shown a high degree of coherent activity in cortex, hippocampus, striatum, and thalamus across states in normal animals, 14 suggesting that a single channel of eeg is sufficient for state space analysis of sleep/wake behavior. However, reduced eeg coherence has been reported in people with narcolepsy 32 and would support a hypothesized role for orexin across the cortex and in regions that coordinate cortical activity such as the basal forebrain and thalamus.…”

“…using an approach similar to that of gervasoni and colleagues, 14 we defined a 2-dimensional (2-D) state space using 2 spectral amplitude ratios calculated by dividing integrated spectral amplitudes at selected frequency bands. First, a sliding window fourier transform was applied to each raw (0.3-256 hz) eeg signal using a 2-sec window with a 1-sec step size.…”

“…to distinguish between Wake and nreM sleep, we initially considered choices of ratio 2 that focused on the delta band (2-4 hz), but the separation of clusters was optimal when we included all eeg activity between 0.3 and 20 hz as shown in previous state space work. 14,15 We defined ratio 1 as 6.5-9/0.3-9 Hz to emphasize high theta (6.5-9 Hz) frequencies because activity in this range dominates rodent reM sleep, and dysregulation of reM sleep is an important aspect of the narcolepsy phenotype. Note that the choice of frequency bands for ratio 1 (6.5-9 Hz) is slightly different from those proposed by gervasoni et al and empirically resulted in a better cluster separation for our data set.…”

“…consistent with this trend, state space analysis provides a novel, non-categorical method for analyzing sleep/wake behavior. [14][15][16] By enabling visualization of behavioral states as a continuum, state space analysis captures the richness of physiology better than conventional, categorical scoring of sleep/wake behavior. for example, the state space approach revealed clear differences in the depth of Wake and nreM sleep of OXKO mice that were not apparent with traditional spectral analysis, possibly because in these analyses transitional data is often excluded or diluted through averaging and because the state space approach employs the ratio of two frequency bands rather than a single frequency band.…”

“…[14][15][16] We adapted these techniques for analysis of EEG recordings in mice and developed metrics for inter-animal comparisons. State space techniques have high temporal resolution and analyze behavior as a continuum, rather than in discrete states, thus facilitating higher dimensional examination of state transitions.…”