Global Forebrain Dynamics Predict Rat Behavioral States and Their Transitions

LFP characteristics throughout the wake-sleep cycle

The extensive inspection of raw intracranial LFP traces (Fig. 1A) confirmed that large-amplitude oscillations were present during all behavioral states, being particularly conspicuous around state transitions (Gottesmann, 1996; Steriade et al., 2001). LFP power spectrograms showed similar state-dependent patterns across the four different forebrain areas (Fig. 1B). This redundancy suggests that these areas are synchronously modulated and all informative about the ongoing behavioral state. Different states shared common spectral features, therefore creating ambiguity in a spectrogram-only sorting of behavioral states [e.g., increased theta oscillations (5-9 Hz) during both AE and REM (Fig. 1B, white asterisk)]. State transitions occurred simultaneously across all areas.

Global brain states as dynamic spectral trajectories

The 2-D state space revealed a finite number of clusters (Figs. 1C, 2A) that corresponded to the different behavioral states exhibited by freely behaving rats (Fig. 2B). This plot allowed for the unequivocal identification of most behavioral states (QW, SWS, REM, and WT), as well as the more elusive transient state named IS (Gottesmann, 1973, 1996), or transition state between SWS and REM episodes (Benington et al., 1994; Mandile et al., 1996; Piscopo et al., 2001) (Fig. 2B). When LFP spectral amplitude within specific bandwidths was plotted on this state space, it was also possible to further characterize the internal dynamics of individual states. For instance, plotting delta (1-4 Hz) amplitude over the 2-D state space (Fig. 2C) broadly separated light SWS (during which 10-14 Hz spindles are predominant) from deep SWS (mostly composed of delta waves). Light and deep SWS constituted a continuous spectrum within the same state, in contrast to the well separated clusters reflecting categorically different global brain states, such as SWS and REM. Moreover, the 2-D state space also provided temporal dynamics information about state evolution, visualized as continuous spectral trajectories as animals coursed from one state to another (Fig. 2D). Brief state changes, such as micro-arousals during SWS episodes (Schieber et al., 1971; Halasz, 1998), could thus be captured (see supplemental Fig. S2, available at www.jneurosci.org as supplemental material). Trajectories connecting different clusters, or state transitions, followed stereotypical spatial paths with characteristic duration. Three of the most frequent trajectories, QW→ AE→ QW, QW→ WT→ QW, and QW→ SWS→ IS→ REM→ QW, are illustrated in Figure 2D. The QW→ SWS→ QW and QW→ SWS→ IS→ QW sequences were also observed (data not shown). These five cyclic trajectories accounted for the overwhelming majority of the behaviors exhibited by rats across the wake-sleep cycle.

• Download figure
• Open in new tab
• Download powerpoint

Figure 2.

Global brain states and two-dimensional state space. A, Scatter plot of the two chosen LFP spectral amplitude ratios, in which four distinct clusters are clearly visible. Each dot corresponds to a 1 sec window for which the amplitude ratios were calculated (48 hr recording, rat 1; for clarity, only one-third of data points, evenly sampled, were plotted). B, When color coded according to the behavioral states visually identified, each cluster in the plot corresponds to a distinct state. C, The amplitude of cortical LFPs in the delta frequency range (1-4 Hz) is color coded. A fine distinction can be made between light SWS (high spindle density) and deep SWS (mostly composed of delta waves). D, Transitions between states can be defined as specific trajectories connecting different clusters, with characteristic duration and speed. Typical trajectories are illustrated. Transitions from SWS to REM always course through the IS region. Trajectories also define the polarity of the different clusters. Entrance to and exit from the SWS cluster always occur on one end of the elongated SWS cluster.

Although a considerable degree of inter-animal variability is to be expected when recording from outbred laboratory animals, we found remarkable similarity across the state spaces obtained for different animals. In five rats, the relative positions of all major and minor states were highly conserved, as can be seen in scatter and density plots (Fig. 3A,B, respectively). For instance, the SWS cluster was always located on the upper right quadrant of the state space, whereas IS and REM occupied the left quadrants, and the waking cluster (including AE and QW) occupied the lower right quadrant. When the speed of the spectral trajectories (value obtained by dividing the distance between two consecutive dots in the 2-D space by the time that separates them, i.e., 1 sec) was plotted over the state space (Fig. 3C), regions of the state space in which spectral features changed slowly (dark blue) coincided with the three main clusters (Fig. 3, compare A, B with C), whereas regions of fast spectral change (green-yellow-red) corresponded to transitional zones between major clusters.

• Download figure
• Open in new tab
• Download powerpoint

Figure 3.

The topography of the two-dimensional state spaces is consistent across animals. A, Scatter plots of the 2-D state space (conventions as in Fig. 2 A). For all animals, 48 hr of recording are displayed; to avoid graphic saturation, only 20% of the data points were evenly sampled and plotted. B, Density plots, calculated from the scatter plots, show the conserved cluster topography and the relative abundance of various states (see also Fig. 2 B). C, Speed plots representing the average velocity of spontaneous trajectories within the 2-D state space. Stationarity (low speed) can be observed within the three main clusters, whereas a maximum speed is reached during transitions from one cluster to another (i.e., between brain states).

Together, these results indicate that the three main clusters, corresponding to waking, SWS, and REM states, represent stable states of forebrain neural activities. On the other hand, transitional zones between clusters with high spectral change speed, including IS, represent periods in which animals shift from one state to another. These results imply that the anatomical and physiological mechanisms governing spectral trajectories across states are conserved among different animals. The sole possible exception is WT, whose cluster occupies a variable location in different rats. Although the overall power spectrum of the WT state was very similar across different rats, with a dominant peak frequency at 7-12 Hz, individual variations were observed in the distribution of resonant (harmonic) frequency peaks (see supplemental Fig. S3, available at www.jneurosci.org as supplemental material), amounting to the inter-animal topographic variation of WT clusters.

Next, we investigated how the information about global brain states is distributed among various forebrain areas by generating state-space maps from the LFPs of each single area in a particular animal (Fig. 4) (see supplemental Fig. S4, available at www.jneurosci.org as supplemental material). The resulting area-specific state-space maps were plotted for each animal and color coded for behavioral states. As predicted from the high redundancy of LFP power spectrograms (Fig. 1B), single-area maps were qualitatively similar to the multiple-area pooled maps, except for the cortex-specific maps, in which REM was not well dissociable from other state clusters because of the low contrast (ambiguity) in cortical LFPs between REM and waking in the theta frequency range. This impression was confirmed using a linear discriminant analysis to assess how well each map separates various behavioral states based on the visually coded states (Fig. 4). Error rates were low in all comparisons except for cortex-specific maps for the waking (QW+AE) versus REM and SWS versus REM comparisons in rat 1, 2, and 3. For these comparisons only, classification errors rates of cortex-derived maps were >1.7-fold those calculated for the overall state-space maps. The all-area state maps, combined using PCA, always successfully captured most of the state information and segregated the clusters close to the best separation offered by individual-area maps. Thus, all-area maps were used as an overall measure for the subsequent state analyses.

• Download figure
• Open in new tab
• Download powerpoint

Figure 4.

Area-specific state maps. Individual area state maps generated for three rats and color coded for the behavioral state. PCA was used to combine amplitude ratios obtained from different LFPs (rightmost column). Forty-eight hours of data were plotted for each animal (subsampling 1/3 of data for clarity). Rat 1 was not implanted for recording in the CP. Compared with the state space generated by combining LFPs from all areas, all area-specific maps show qualitatively similar separation of major behavioral states, except for cortex-specific maps. This difference was quantified using linear discriminant analysis to assess how well each map separates the various behavioral states based on the visually coded states. The error rates in the tables represent the proportion of misclassification when an optimal linear decision boundary was used to discriminate the two selected states. Only the highlighted combinations (yellow) show >1.7-fold classification error compared with the error rate in the overall state-space map. These high error rate combinations came exclusively from cortex-specific maps in the (AE+QW) versus REM and SWS versus REM comparisons. The results indicate that single-area maps provide as much information as multiple-area pooled maps with regard to state classification. There is therefore a certain redundancy among the forebrain areas studied, with the possible exception of cortex.

The remarkable segregation of clusters in the 2-D state space and its consistency across animals argues that the various behavioral states represent distinct regimens of global forebrain dynamics that can be solely defined by neural signals, thus allowing the development of automatic algorithms capable of accurate classification of global states without reference to behavioral or electromyogram data (Robert et al., 1999; Kohlmorgen et al., 2000; Grube et al., 2002) (see supplemental information and supplemental Fig. S5, available at www.jneurosci.org as supplemental material).

Functional coupling within the forebrain ensemble across global brain states

To gain additional insight into the dynamics of global brain states, we calculated the coherence between pairs of brain areas and the pooled coherence (Amjad et al., 1997; Halliday and Rosenberg, 1999, 2000) across all recording sites (Figs. 5, 6), which assesses the simultaneous functional coupling among different forebrain areas. When plotted against time, the LFP coherence spectra showed very pronounced state-dependent fluctuations (Fig. 5A,B). These state-dependent coherence patterns, present in all pairwise coherence analyses (Fig. 5A) (see supplemental Fig. S6, available at www.jneurosci.org as supplemental material), were well captured by the pooled coherence measure (Fig. 5B). Thus, pooled coherence provided a consistent measure to look simultaneously at the coherence structure over multiple LFPs with a single measure. A one-way multivariate ANOVA revealed that the pooled coherence for delta (1-4 Hz), theta (5-9 Hz), spindle (10-14 Hz), beta (15-25 Hz), and gamma (30-55 Hz) ranges was significantly different between states (p < 0.001). SWS was the state of maximum coherence in the delta and spindle frequency bands (Fig. 5B) (0.33 ± 0.09 and 0.27 ± 0.10, respectively; mean ± SEM), characterizing the progressive entrainment of all of the forebrain areas by large-amplitude in-phase slow waves (Steriade et al., 1993; Amzica and Steriade, 1995; Achermann and Borbely, 1998a,b; Destexhe et al., 1999). Present during AE, QW, and REM (Maloney et al., 1997; Gross and Gotman, 1999; Uchida et al., 2001), fast gamma oscillations were associated with an overall low pooled coherence during AE, most likely reflecting the absence of gamma coherence over large distances during this state (Destexhe et al., 1999; Gross and Gotman, 1999). In agreement with EEG (Achermann and Borbely, 1998a) and magneto-encephalographic (Llinas and Ribary, 1993) evidence in humans, REM was the state of maximum pooled coherence in the gamma range (0.22 ± 0.08; mean ± SEM), indicating a stronger functional coupling between forebrain areas during this state (Fig. 5B, REM episodes from 5640 to 5785 sec and from 6116 to 6328 sec). As illustrated in Figure 5C, the state-dependent patterns of pooled coherence were confirmed by plotting the coherence measurements on the 2-D state maps. These plots further revealed distinctions within the major states, such as the graded difference between light and deep SWS, and a marked contrast between AE and QW states within the waking state. Indeed, the highest delta pooled coherence values were observed in the right part of the SWS cluster corresponding to deep SWS. High spindle coherence values were observed during IS and WT (Fig. 6) and to a lesser extent during light SWS. Interestingly, theta coherence was high throughout REM episodes in all brain area pairs [e.g., Cx-Hi (Fig. 5A,C)] but low when calculated across the ensemble (Fig. 5B), indicating a low degree of in-phase theta activity among the ensemble during this state.

• Download figure
• Open in new tab
• Download powerpoint

Figure 5.

LFP coherence analysis. A, Examples of pairwise coherence spectrograms illustrating the state-dependent pattern of coupling between Cx and Th (top) and between Cx and Hi (bottom). The spectrograms are aligned with the behavioral state identification (visual coding). Notice that, during REM sleep, both pairs of areas present a high coherence in the theta frequency range (white arrows). B, LFP pooled coherence spectrogram showing the variations of coherence across all areas during the wake-sleep cycle. The highest pooled coherence values are observed in the low frequency range (delta range) during SWS episodes. During WT, animals present typical 7-12 Hz LFP oscillations, and the pooled coherence is greatly increased in this frequency range and its harmonics. The IS state shows high pooled coherence in the 7-22 Hz range. REM and QW have the highest pooled coherence in the gamma range (>30 Hz) but also a low pooled coherence in the theta range, indicating that the four areas present theta oscillations that are not strictly in-phase. C, Coherence measurements over 48 hr of recording are overlaid on the 2-D state map. For delta, spindle, and gamma frequency ranges, pooled coherence is shown. In the theta range, pairwise coherence between Cx and Hi is displayed. High delta pooled coherence values are observed in the right portion of the SWS cluster corresponding to deep SWS, whereas high spindle coherence values are observed during IS and WT (see also Fig.6). High gamma pooled coherence is observed during both REM and QW. The pair Cx-Hi presents high theta coherence during both REM and AE.

• Download figure
• Open in new tab
• Download powerpoint

Figure 6.

Pooled coherence, a single measure to address the dynamic of global brain states. 3-D state space in four rats derived from the two amplitude ratios (X- and Y-axes) and the additional average pooled coherence between 7 and 55 Hz (Z-axis). The use of pooled coherence as a single measure of the coupling between forebrain areas captured state-dependent patterns and further improved the separation between states. Notice that the WT cluster can be easily identified.

As illustrated in Figure 6, the use of pooled coherence from 7-55 Hz as a third dimension of the state space greatly improves the separation between states and further separates WT from the other states because of its high coherence values. For example, in the case of rats 3 and 5, WT appeared undifferentiated at the 2-D plot (Figs. 3A, 5C) but could be clearly separated from the other states on the 3-D representation (Fig. 6). This reflects the fact that the state with the highest LFP pooled coherence in the forebrain was WT, spanning broad frequency bands (7-12, 14-18, and 20-28 Hz) (Figs. 5B, 7, third column) (see supplemental Fig. S3, available at www.jneurosci.org as supplemental material).

• Download figure
• Open in new tab
• Download powerpoint

Figure 7.

Coherence spectra at state transitions. Four common global brain state transitions of a representative animal are presented with an example of LFP recordings (first column), the corresponding spectral trajectories and their duration within the 2-D state space (second column), and an averaged pooled coherence spectrum for the pre- and post-states and the transition itself (third column). LFP, Recordings of the four areas revealed common oscillatory features and simultaneous changes during state transitions. Calibration bar, 5 sec. Trajectories, Paths connecting major clusters were identified, and histograms of the distribution of duration (insets) were calculated for these trajectories. The average and mode ± SEM of the duration are indicated for this animal (see supplemental Fig. S7, available at www.jneurosci.org as supplemental material). Coherence spectrum, Average coherence spectrum of transitions plotted against the average coherence spectrum of the pre- and post-states, whose points were taken from time points immediately adjacent to the selected trajectories with the same duration as the trajectory. Arrows indicate frequency bands at which significant changes in LFP coherence occur during the transition.

Forebrain dynamics during state transitions

To further scrutinize the neural dynamics underlying transitions between global brain states, we quantified the stereotypical trajectory patterns bridging major states in the 2-D state space. Common state-to-state transitions (Fig. 7, left panels) can be easily identified by a parametric analysis of trajectory paths and their duration (Fig. 7, middle panels) (see supplemental Fig. S7, available at www.jneurosci.org as supplemental material). Whereas most state transitions were direct and fast (QW→ SWS at 11.02 ± 0.36 sec; REM→ QW at 3.50 ± 0.11 sec; mode ± SEM), transitions involving IS [e.g., SWS→ REM transition (Fig. 7B,B′)] lasted considerably longer (26.67 ± 0.36 sec; mode ± SEM; F_(7,40) = 85.70; p < 0.001; one-way ANOVA, followed by LSD post hoc test).

Regardless of differences in duration, almost all state transitions involved striking changes in forebrain LFP synchronization manifested in the coherence spectra (Fig. 7, right panels) (see supplemental Fig. S8, available at www.jneurosci.org as supplemental material). Significant coherence changes during state transitions were identified by comparing the coherence spectra of state transitions with the spectrum of the flanking states. REM→ QW transitions showed the least changes of coherence spectrum among all successive states (Fig. 7C″). For the QW→ SWS transitions, we observed a significant change of coherence in the spindle frequency range (Fig. 7A″) relative to both QW and SWS (p = 0.025 and p = 0.043 respectively; paired Student's t test). We also found a significant increase in delta range coherence when comparing the QW→ SWS transitions with SWS (p = 0.007; paired Student's t test). Interestingly, delta coherence during the transition was very similar to that observed during QW (p = 0.046; paired Student's t test) and significantly lower than the average of QW and SWS values, (p = 0.012; paired Student's t test), whereas no significant difference was found at spindle range (p = 0.201; paired Student's t test). Altogether, these results indicate that delta coherence changes were slower and less prominent than changes in spindle coherence, supporting the notion that transitions into SWS mainly involve changes in the magnitude of spindle coherence (Achermann and Borbely, 1998b).

Dramatic changes in coherence spectra were even more pronounced in transitions involving IS: the pooled coherence amplitude during SWS→ IS→ REM transitions was significantly higher than the expected average of SWS and REM between 7 and 22 Hz (p = 0.01; paired Student's t test) and peaked at 8-12 Hz (Fig. 7B″) (see also Fig. 5A,B), which corresponds to the frequency band of the large-amplitude oscillations that characterize IS (Mandile et al., 1996). Similar increases in coherence could also be found in other state transitions involving IS, such as SWS→ IS→ QW and REM→ IS→ QW (data not shown). For QW→ WT transitions, the resonant peaks of coherence were consistently shifted toward higher frequencies relative to those of the WT state (Fig. 7D″), suggesting the presence of transient increased coherence at higher frequencies, at the beginning of WT epochs (Shaw, 2003).

Physiological significance of the 3-D state space

Our results are in line with numerous studies showing that behavioral state-related rhythms recorded by surface EEG are also present in subcortical areas. For instance, beta oscillations recorded in the motor cortex during voluntary movements (Sanes and Donoghue, 1993) have also been recorded in the hippocampus (Leung, 1992) and more recently in the striatum (Courtemanche et al., 1997, 2003). Fast gamma oscillations (30-80 Hz) associated with activation of cortical sensory areas (Buzsaki et al., 1992; Singer and Gray, 1995) have also been reported in the hippocampus of rodents and humans (Borbely et al., 1984; Buzsaki et al., 1992). Here we show that LFPs from the cortex, thalamus, hippocampus, and striatum evolve similarly and simultaneously across all states that comprise the wake-sleep cycle.

It is well known that LFPs mostly reflect synaptic currents, i.e., the summation of postsynaptic potentials and intrinsic currents (Nunez, 1981; Lopes da Silva, 1991). It is also known that the amplitude of LFP signals is correlated to the degree of coherent activity in a population of neurons (Lopes da Silva, 1991). In a freely behaving animal, synaptic currents generated by local networks and short-range connections are not easily dissociable from those caused by distant synaptic projections (Cauller and Connors, 1994; Castro-Alamancos and Connors, 1996). The changes in LFP signals observed concurrently within four forebrain structures captured by the 3-D state space reflect the global synaptic input landscape underlying the activity of forebrain neurons. These changes probably do not derive from a single common source but rather from the local integration of local, regional, and global inputs. This point is particularly well illustrated by the low pooled coherence in the theta band during AE and REM, states characterized by strong hippocampal theta rhythm (Green and Arduini, 1954; Timo-Iaria et al., 1970).

The 3-D dynamic landscape is primarily determined by ascending neuromodulatory systems, including the pontine and basal forebrain cholinergic nuclei, and the monoaminergic systems in the brainstem (Jouvet, 1962, 1972; Moruzzi, 1972; Jones, 1991, 1993; Steriade et al., 1993; Berridge and Waterhouse, 2003). These interconnected systems (Nauta et al., 1974; Nauta, 1979; Steriade and Deschenes, 1984; Jones, 1993; Zaborszky et al., 1999; Killackey and Sherman, 2003) lead to the state-dependent delivery of modulatory neurotransmitters throughout the entire forebrain, shaping the synaptic landscape that gates neuronal responses and behavior. As a result, the activity of neuronal populations in multiple forebrain areas evolves simultaneously across the wake-sleep cycle according to complex and dynamic state-specific patterns (Winson and Abzug, 1977; Pavlides et al., 1988; Steriade et al., 2001; Berridge and Waterhouse, 2003). In support of this view, studies in awake monkeys performing a visual search task have shown that responses to visual stimulation recorded in cortical area V4 were often reduced or even completely blocked when animals became drowsy, whereas the background neuronal activity changed to the burst-pause pattern typically observed in sleep (Pigarev et al., 1997). Similarly, state-dependent alterations of auditory receptive fields have been reported in rats (Edeline et al., 2000). In the somatosensory system of rats, it has been shown recently that behavioral change during waking states (QW, AE, and WT) determines parallel changes in the tactile responses of neuronal ensembles in the main thalamocortical loop of the trigeminal system (Fanselow and Nicolelis, 1999; Nicolelis and Fanselow, 2002; Wiest and Nicolelis, 2003). Enhancements of neuronal responsiveness by changes in synaptic background activity have also been reported in computational studies (Ho and Destexhe, 2000). In conclusion, the accurate identification of global brain state within a synaptic input landscape such as the one presented in this study is likely to provide substantial insight about the effects of central states on evoked responses and spontaneous behaviors.

Functional coupling of brain areas during state transitions

The interest in measures of neural coherence to study global brain states derives from the fact that waking and sleep involve very different levels of in-phase LFP oscillations. The fast LFP oscillations that characterize waking occur simultaneously in multiple cortical spots but result in overall low coherence over long distances (Destexhe et al., 1999). In contrast, SWS is characterized by a gradual increase of low-frequency LFP coherence (Steriade et al., 1993; Achermann and Borbely, 1998a,b; Destexhe et al., 1999). Here, we presented for the first time direct evidence that global brain state transitions occur simultaneously across multiple forebrain areas as transient and drastic changes in neural synchronization. These changes are effected by the prominent narrow-band neural oscillations that mark state boundaries. Our results show that distant forebrain areas tightly coordinate the processing of neural information as one global brain state evolves into another, indicating a very high degree of functional integration in the forebrain across the entire wake-sleep cycle.

Our results also clearly indicate that the extent of functional coupling across multiple forebrain areas in the theta and gamma ranges is smaller than in the delta frequency band, supporting the inverse relationship between synchronization-frequency and the distance between functionally connected areas, i.e., long-range synchronization at low frequencies and short-distance coupling at high frequencies (Achermann and Borbely, 1998a; Gross and Gotman, 1999; von Stein and Sarnthein, 2000).

Synchronization within neuronal ensembles and coherence among interconnected brain areas have been proposed to underlie the integration of behavior (Hebb, 1949; Nicolelis et al., 1995; Singer, 1995; Engel et al., 2001; Varela et al., 2001; O'Connor et al., 2002). Our data indicate that the spontaneous succession of global brain states is nearly always accompanied by marked changes in LFP coherence at selected frequency bands across multiple forebrain areas. Transitions between states with very different coherence bandwidths thus seem to require either gradual spectral change at selected bandwidths, or abrupt broadband synchronization. These distributed and highly coherent LFP oscillations presumably reflect transient synchronizations of synaptic inputs at the termination and initiation of global brain states. We propose that such transient synchronization at the boundaries of global states may function as a “handshake protocol” within and across brain areas. By simultaneously adjusting the functional connectivity between areas (Glenn and Steriade, 1982; Lopes da Silva, 1991), these transient events may allow spatially distributed structures to organize and share a continuous flow of information throughout the wake-sleep cycle, building neural representations in a state-dependent manner.

From a behavioral point of view, a mechanism to facilitate the exchange of neural information within and across brain areas may be critical when animals shift from waking to sleep. Whereas wakefulness can be described as a state for real-time processing of sensorimotor information, sleep is rather involved in the off-line processing and consolidation of newly acquired information (Jenkins and Dallenbach, 1924; Fishbein, 1971; Hennevin et al., 1971; Smith et al., 1980; Smith and Butler, 1982; Buzsaki, 1989; Pavlides and Winson, 1989; Wilson and McNaughton, 1994; Hennevin et al., 1995; Stickgold, 1998; Laureys et al., 2001; Maquet, 2001; Stickgold et al., 2001; Ribeiro et al., 2004). A stronger functional coupling between areas at the boundaries of waking and sleep states might not only ensure a sustained and accurate transfer of sensorimotor information but also allow an overall reinforcement of selected pathways, further promoting the consolidation of specific memory traces acquired during waking.