5-MeO-DMT induces sleep-like LFP spectral signatures in the hippocampus and prefrontal cortex of awake rats.

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a potent classical psychedelic known to induce changes in locomotion, behaviour, and sleep in rodents. However, there is limited knowledge regarding its acute neurophysiological effects. Local field potentials (LFPs) are commonly used as a proxy for neural activity, but previous studies investigating psychedelics have been hindered by confounding effects of behavioural changes and anaesthesia, which alter these signals. To address this gap, we investigated acute LFP changes in the hippocampus (HP) and medial prefrontal cortex (mPFC) of freely behaving rats, following 5-MeO-DMT administration. 5-MeO-DMT led to an increase of delta power and a decrease of theta power in the HP LFPs, which could not be accounted for by changes in locomotion. Furthermore, we observed a dose-dependent reduction in slow (20-50 Hz) and mid (50-100 Hz) gamma power, as well as in theta phase modulation, even after controlling for the effects of speed and theta power. State map analysis of the spectral profile of waking behaviour induced by 5-MeO-DMT revealed similarities to electrophysiological states observed during slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. Our findings suggest that the psychoactive effects of classical psychedelics are associated with the integration of waking behaviours with sleep-like spectral patterns in LFPs.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Sleep disrupts complex spiking dynamics in the neocortex and hippocampus.

Neuronal interactions give rise to complex dynamics in cortical networks, often described in terms of the diversity of activity patterns observed in a neural signal. Interestingly, the complexity of spontaneous electroencephalographic signals decreases during slow-wave sleep (SWS); however, the underlying neural mechanisms remain elusive. Here, we analyse in-vivo recordings from neocortical and hippocampal neuronal populations in rats and show that the complexity decrease is due to the emergence of synchronous neuronal DOWN states. Namely, we find that DOWN states during SWS force the population activity to be more recurrent, deterministic, and less random than during REM sleep or wakefulness, which, in turn, leads to less complex field recordings. Importantly, when we exclude DOWN states from the analysis, the recordings during wakefulness and sleep become indistinguishable: the spiking activity in all the states collapses to a common scaling. We complement these results by implementing a critical branching model of the cortex, which shows that inducing DOWN states to only a percentage of neurons is enough to generate a decrease in complexity that replicates SWS.

Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex.

Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition, and physiology.

The cerebral cortex is the most recently evolved region of the mammalian brain. There, millions of neurons can synchronize their activity to create brain waves, a series of electric rhythms associated with various cognitive functions. Gamma waves, for example, are thought to be linked to brain processes which require distributed networks of neurons to communicate and integrate information. These waves were first discovered in the 1940s by researchers investigating brain areas involved in olfaction, and they are thought to be important for detecting and recognizing smells. Yet, scientists still do not understand how these waves are generated or what role they play in sensing odors. To investigate these questions, González et al. used a battery of computational approaches to analyze a large dataset of brain activity from awake mice. This revealed that, in the cortical region dedicated to olfaction, gamma waves arose each time the animals completed a breathing cycle ­ that is, after they had sampled the air by breathing in. Each breath was followed by certain neurons relaying olfactory information to the cortex to activate complex cell networks; this included circuits of cells known as feedback interneurons, which can switch off weakly activated neurons, including ones that participated in activating them in the first place. The respiration-driven gamma waves derived from this 'feedback inhibition' mechanism. Further work then examined the role of the waves in olfaction. Smell identification relies on each odor activating a unique set of cortical neurons. The analyses showed that gamma waves acted to select and amplify the best set of neurons for representing the odor sensed during a sniff, and to quieten less relevant neurons. Loss of smell is associated with many conditions which affect the brain, such as Alzheimer's disease or COVID-19. By shedding light on the neuronal mechanisms that underpin olfaction, the work by González et al. could help to better understand how these impairments emerge, and how the brain processes other types of complex information.

Respiration and rapid eye movement (REM) sleep substructure: short versus long episodes.

Rapid eye movement (REM) sleep in rodents is defined by the presence of theta rhythm in the absence of movement. The amplitude and frequency of theta oscillations have been used to distinguish between tonic and phasic REM sleep. However, tonic REM sleep has not been further subdivided, although characteristics of network oscillations such as cross-frequency coupling between theta and gamma vary within this sub-state. Recently, it has been shown that theta-gamma coupling depends on an optimal breathing rate of ~5 Hz. The frequency of breathing varies strongly throughout REM sleep, and the duration of single REM sleep episodes ranges from several seconds to minutes, whereby short episodes predominate. Here we studied the relation between breathing frequency, accelerometer activity, and the length of REM sleep periods. We found that small movements detected with three-dimensional accelerometry positively correlate with breathing rate. Interestingly, breathing is slow in short REM sleep episodes, while faster respiration regimes exclusively occur after a certain delay in longer REM sleep episodes. Thus, merging REM sleep episodes of different lengths will result in a predominance of slow respiration due to the higher occurrence of short REM sleep periods. Moreover, our results reveal that not only do phasic REM sleep epochs predominantly occur during long REM sleep episodes, but that the long episodes also have faster theta and higher gamma activity. These observations suggest that REM sleep can be further divided from a physiological point of view depending on its duration. Higher levels of arousal during REM sleep, indicated by higher breathing rates, can only be captured in long REM sleep episodes.

Respiratory entrainment of units in the mouse parietal cortex depends on vigilance state.

Synchronous oscillations are essential for coordinated activity in neuronal networks and, hence, for behavior and cognition. While most network oscillations are generated within the central nervous system, recent evidence shows that rhythmic body processes strongly influence activity patterns throughout the brain. A major factor is respiration (Resp), which entrains multiple brain regions at the mesoscopic (local field potential) and single-cell levels. However, it is largely unknown how such Resp-driven rhythms interact or compete with internal brain oscillations, especially those with similar frequency domains. In mice, Resp and theta (θ) oscillations have overlapping frequencies and co-occur in various brain regions. Here, we investigated the effects of Resp and θ on neuronal discharges in the mouse parietal cortex during four behavioral states which either show prominent θ (REM sleep and active waking (AW)) or lack significant θ (NREM sleep and waking immobility (WI)). We report a pronounced state-dependence of spike modulation by both rhythms. During REM sleep, θ effects on unit discharges dominate, while during AW, Resp has a larger influence, despite the concomitant presence of θ oscillations. In most states, unit modulation by θ or Resp increases with mean firing rate. The preferred timing of Resp-entrained discharges (inspiration versus expiration) varies between states, indicating state-specific and different underlying mechanisms. Our findings show that neurons in an associative cortex area are differentially and state-dependently modulated by two fundamentally different processes: brain-endogenous θ oscillations and rhythmic somatic feedback signals from Resp.

Breathing modulates gamma synchronization across species.

Nasal respiration influences brain dynamics by phase-entraining neural oscillations at the same frequency as the breathing rate and by phase-modulating the activity of faster gamma rhythms. Despite being widely reported, we still do not understand the functional roles of respiration-entrained oscillations. A common hypothesis is that these rhythms aid long-range communication and provide a privileged window for synchronization. Here we tested this hypothesis by analyzing electrocorticographic (ECoG) recordings in mice, rats, and cats during the different sleep-wake states. We found that the respiration phase modulates the amplitude of cortical gamma oscillations in the three species, although the modulated gamma frequency bands differed with faster oscillations (90-130 Hz) in mice, intermediate frequencies (60-100 Hz) in rats, and slower activity (30-60 Hz) in cats. In addition, our results also show that respiration modulates olfactory bulb-frontal cortex synchronization in the gamma range, in which each breathing cycle evokes (following a delay) a transient time window of increased gamma synchrony. Long-range gamma synchrony modulation occurs during quiet and active wake states but decreases during sleep. Thus, our results suggest that respiration-entrained brain rhythms orchestrate communication in awake mammals.

Differential modulation of parietal cortex activity by respiration and θ oscillations.

The simultaneous, local integration of information from widespread brain regions is an essential feature of cortical computation and particularly relevant for multimodal association areas such as the posterior parietal cortex. Slow, rhythmic fluctuations in the local field potentials (LFPs) are assumed to constitute a global signal aiding interregional communication through the long-range synchronization of neuronal activity. Recent work demonstrated the brain-wide presence of a novel class of slow neuronal oscillations that are entrained by nasal respiration. However, whether there are differences in the influence of the respiration-entrained rhythm (RR) and the endogenous theta (θ) rhythm over local networks is unknown. In this work, we aimed at characterizing the impact of both classes of oscillations on neuronal activity in the posterior parietal cortex of mice. We focused our investigations on a θ-dominated state (rapid eye movement sleep) and an RR-dominated state (wake immobility). Using linear silicon probes implanted along the dorsoventral cortical axis, we found that the LFP-depth distributions of both rhythms show differences in amplitude and coherence but no phase shift. Using tetrode recordings, we demonstrate that a substantial fraction of parietal neurons is modulated by either RR or θ or even by both rhythms simultaneously. Interestingly, the phase and cortical depth dependence of spike-field coupling differ for these oscillations. We further show through intracellular recordings in urethane-anesthetized mice that synaptic inhibition is likely to play a role in generating respiration-entrainment at the membrane potential level. We conclude that θ and respiration differentially affect neuronal activity in the parietal cortex.NEW & NOTEWORTHY Nasal respiration generates a rhythmic signal that entrains large portions of the mammalian brain into respiration-coupled field potentials. Here, we report the simultaneous presence of respiratory rhythm (RR) and θ oscillations in the parietal association cortex of mice. Despite their overlapping frequencies, both rhythms differ in their state-dependent power and differentially entrain the discharge behavior of units. We conclude that network activity in the parietal cortex is synchronized by two different physiological oscillation patterns.

Hippocampal-prefrontal interactions during spatial decision-making.

The hippocampus has been linked to memory encoding and spatial navigation, while the prefrontal cortex is associated with cognitive functions such as decision-making. These regions are hypothesized to communicate in tasks that demand both spatial navigation and decision-making processes. However, the electrophysiological signatures underlying this communication remain to be better elucidated. To investigate the dynamics of the hippocampal-prefrontal interactions, we have analyzed their local field potentials and spiking activity recorded from rats performing a spatial alternation task on a figure eight-shaped maze. We found that the phase coherence of theta peaked around the choice point area of the maze. Moreover, Granger causality revealed a hippocampus → prefrontal cortex directionality of information flow at theta frequency, peaking at starting areas of the maze, and on the reverse direction at delta frequency, peaking near the turn onset. Additionally, the patterns of phase-amplitude cross-frequency coupling within and between the regions also showed spatial selectivity, and hippocampal theta and prefrontal delta modulated not only gamma amplitude but also inter-regional gamma synchrony. Finally, we found that the theta rhythm dynamically modulated neurons in both regions, with the highest modulation at the choice area; interestingly, prefrontal cortex neurons were more strongly modulated by the hippocampal theta rhythm than by their local field rhythm. In all, our results reveal maximum electrophysiological interactions between the hippocampus and the prefrontal cortex near the decision-making period of the spatial alternation task, corroborating the hypothesis that a dynamic interplay between these regions takes place during spatial decisions.

Theta-gamma coupling during REM sleep depends on breathing rate.

Temporal coupling between theta and gamma oscillations is a hallmark activity pattern of several cortical networks and becomes especially prominent during REM sleep. In a parallel approach, nasal breathing has been recently shown to generate phase-entrained network oscillations which also modulate gamma. Both slow rhythms (theta and respiration-entrained oscillations) have been suggested to aid large-scale integration but they differ in frequency, display low coherence, and modulate different gamma sub-bands. Respiration and theta are therefore believed to be largely independent. In the present work, however, we report an unexpected but robust relation between theta-gamma coupling and respiration in mice. Interestingly, this relation takes place not through the phase of individual respiration cycles, but through respiration rate: the strength of theta-gamma coupling exhibits an inverted V-shaped dependence on breathing rate, leading to maximal coupling at breathing frequencies of 4-6 Hz. Noteworthy, when subdividing sleep epochs into phasic and tonic REM patterns, we find that breathing differentially relates to theta-gamma coupling in each state, providing new evidence for their physiological distinctiveness. Altogether, our results reveal that breathing correlates with brain activity not only through phase-entrainment but also through rate-dependent relations with theta-gamma coupling. Thus, the link between respiration and other patterns of cortical network activity is more complex than previously assumed.

Temporal Relations between Cortical Network Oscillations and Breathing Frequency during REM Sleep.

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or temporal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent θ and γ oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing frequency strongly correlates with the instantaneous frequency and amplitude of both θ and γ oscillations. Cross-correlograms and Granger causality revealed specific directionalities for different rhythms: changes in θ activity precede and Granger-cause changes in breathing frequency, suggesting control by the functional state of the brain. On the other hand, the instantaneous breathing frequency Granger causes changes in γ frequency, suggesting that γ is influenced by a peripheral reafference signal. These findings show that changes in breathing frequency temporally relate to changes in different patterns of rhythmic brain activity. We hypothesize that such temporal relations are mediated by a common central drive likely to be located in the brainstem.

Diazepam causes sedative rather than anxiolytic effects in C57BL/6J mice.

Diazepam has been broadly accepted as an anxiolytic drug and is often used as a positive control in behavioral experiments with mice. However, as opposed to this general assumption, the effect of diazepam on mouse behavior can be considered rather controversial from an evidence point of view. Here we revisit this issue by studying the effect of diazepam on a benchmark task in the preclinical anxiety literature: the elevated plus maze. We evaluated the minute-by-minute time-course of the diazepam effect along the 10 min of the task at three different doses (0.5, 1 and 2 mg/kg i.p. 30 min before the task) in female and male C57BL/6J mice. Furthermore, we contrasted the effects of diazepam with those of a selective serotoninergic reuptake inhibitor (paroxetine, 10 mg/kg i.p. 1 h before the task). Diazepam had no anxiolytic effect at any of the tested doses, and, at the highest dose, it impaired locomotor activity, likely due to sedation. Noteworthy, our results held true when examining male and female mice separately, when only examining the first 5 min of the task, and when animals were subjected to one hour of restrain-induced stress prior to diazepam treatment. In contrast, paroxetine significantly reduced anxiety-like behavior without inducing sedative effects. Our results therefore suggest that preclinical studies for screening new anxiolytic drugs should be cautious with diazepam use as a potential positive control.

EEG Gamma Band Alterations and REM-like Traits Underpin the Acute Effect of the Atypical Psychedelic Ibogaine in the Rat.

Ibogaine is a psychedelic alkaloid that has attracted large scientific interest because of its antiaddictive properties in observational studies in humans as well as in animal models. Its subjective effect has been described as intense, vivid dream-like experiences occurring while awake; hence, ibogaine is often referred to as an oneirogenic psychedelic. While this unique dream-like profile has been hypothesized to aid the antiaddictive effects, the electrophysiological signatures of this psychedelic state remain unknown. We previously showed in rats that ibogaine promotes a waking state with abnormal motor behavior along with a decrease in NREM and REM sleep. Here, we performed an in-depth analysis of the intracranial electroencephalogram during "ibogaine wakefulness". We found that ibogaine induces gamma oscillations that, despite having larger power than control levels, are less coherent and less complex. Further analysis revealed that this profile of gamma activity compares to that of natural REM sleep. Thus, our results provide novel biological evidence for the association between the psychedelic state and REM sleep, contributing to the understanding of the brain mechanisms associated with the oneirogenic psychedelic effect of ibogaine.

On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. Additionally, by varying parameters such as the levels of protein synthesis and degradation, we predict behavioral outcomes, and thus boundary conditions that can be tested experimentally.

Spike sorting with Gaussian mixture models.

The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant features using a combination of common techniques (e.g., principal components, wavelets) and GMM fitting parameters (e.g., Gaussian distances). Then, we developed an approach to perform unsupervised clustering using GMMs, estimating cluster properties in a data-driven way. We found the proposed GMM-based framework outperforms previously established methods in simulated and real extracellular recordings. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface to run our algorithm, which allows manual adjustments.

Characterizing Speed Cells in the Rat Hippocampus.

Spatial navigation relies on visual landmarks as well as on self-motion information. In familiar environments, both place and grid cells maintain their firing fields in darkness, suggesting that they continuously receive information about locomotion speed required for path integration. Consistently, "speed cells" have been previously identified in the hippocampal formation and characterized in detail in the medial entorhinal cortex. Here we investigated speed-correlated firing in the hippocampus. We show that CA1 has speed cells that are stable across contexts, position in space, and time. Moreover, their speed-correlated firing occurs within theta cycles, independently of theta frequency. Interestingly, a physiological classification of cell types reveals that all CA1 speed cells are inhibitory. In fact, while speed modulates pyramidal cell activity, only the firing rate of interneurons can accurately predict locomotion speed on a sub-second timescale. These findings shed light on network models of navigation.

Ventral hippocampal OLM cells control type 2 theta oscillations and response to predator odor.

Dorsal and ventral hippocampus regions exert cognition and emotion-related functions, respectively. Since both regions display rhythmic activity, specific neural oscillatory pacemakers may underlie their functional dichotomy. Type 1 theta oscillations are independent of cholinergic transmission and are observed in the dorsal hippocampus during movement and exploration. In contrast, type 2 theta depends on acetylcholine and appears when animals are exposed to emotionally laden contexts such as a predator presence. Despite its involvement in emotions, type 2 theta has not been associated with the ventral hippocampus. Here, we show that optogenetic activation of oriens-lacunosum moleculare (OLM) interneurons in the ventral hippocampus drives type 2 theta. Moreover, we found that type 2 theta generation is associated with increased risk-taking behavior in response to predator odor. These results demonstrate that two theta oscillations subtypes originate in the two hippocampal regions that predominantly underlie either cognitive or emotion-related functions.

OLMα2 Cells Bidirectionally Modulate Learning.

Inhibitory interneurons participate in mnemonic processes. However, defined roles for identified interneuron populations are scarce. A subpopulation of oriens lacunosum-moleculare (OLM) interneurons genetically defined by the expression of the nicotinic receptor α2 subunit has been shown to gate information carried by either the temporoammonic pathway or Schaffer collaterals in vitro. Here we set out to determine whether selective modulation of OLMα2 cells in the intermediate CA1 affects learning and memory in vivo. Our data show that intermediate OLMα2 cells can either enhance (upon their inhibition) or impair (upon their activation) object memory encoding in freely moving mice, thus exerting bidirectional control. Moreover, we find that OLMα2 cell activation inhibits fear-related memories and that OLMα2 cells respond differently to nicotine in the dorsoventral axis. These results suggest that intermediate OLMα2 cells are an important component in the CA1 microcircuit regulating learning and memory processes. VIDEO ABSTRACT.

Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain.

Slow brain oscillations are usually coherent over long distances and thought to link distributed cell assemblies. In mice, theta (5-10 Hz) stands as one of the most studied slow rhythms. However, mice often breathe at theta frequency, and we recently reported that nasal respiration leads to local field potential (LFP) oscillations that are independent of theta. Namely, we showed respiration-coupled oscillations in the hippocampus, prelimbic cortex, and parietal cortex, suggesting that respiration could impose a global brain rhythm. Here we extend these findings by analyzing LFPs from 15 brain regions recorded simultaneously with respiration during exploration and REM sleep. We find that respiration-coupled oscillations can be detected in parallel with theta in several neocortical regions, from prefrontal to visual areas, and also in subcortical structures such as the thalamus, amygdala and ventral hippocampus. They might have escaped attention in previous studies due to the absence of respiration monitoring, the similarity with theta oscillations, and the highly variable peak frequency. We hypothesize that respiration-coupled oscillations constitute a global brain rhythm suited to entrain distributed networks into a common regime. However, whether their widespread presence reflects local network activity or is due to volume conduction remains to be determined.