Respiratory Entrainment of Units in the Mouse Parietal Cortex Depends on Vigilance State

doi:10.21203/rs.3.rs-1445724/v1

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, which entrains multiple brain regions at the mesoscopic (local field potential) and single-cell levels. However, it is largely unknown how such respiration-driven rhythms interact or compete with internal brain oscillations, especially those with similar frequency domains. In mice, respiration and theta (θ) oscillations have overlapping frequencies and co-occur in various brain regions. Here we investigated the effects of respiration 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 respiration has a larger influence, despite the concomitant presence of θ oscillations. In most states, unit modulation by θ or respiration increases with mean firing rate. The preferred timing of respiration-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 respiration.

posterior parietal cortex

unit firing

respiration

θ rhythm

sleep

wakefulness

Neuronal network oscillations are important for coordinating multi-neuronal activity patterns within and across different brain regions [15]. Multiple lines of evidence show that such coordinated patterns, in turn, are a key mechanism underlying cognition and behavior [17, 18, 24]. While most oscillation patterns result from brain-endogenous network dynamics, there is growing evidence that they can also be generated or modulated by feedback from rhythmic activity in the body. Such rhythmic feedback signals include respiration [23, 29, 57], cardiac activity [13, 25, 60] and the gastric rhythm [45, 46]. Modulation of neuronal behavior occurs across species, including humans [26, 64], and it affects widespread regions of the brain [47, 57]. Respiration-driven oscillations are not only visible at the network level but do modulate spiking of single neurons in different regions including frontal cortex [5, 35], dentate gyrus [12] and parietal cortex [32]. In line with these findings, several lines of evidence show that oscillating somatic feedback signals affect cognition in humans [41, 64] and rodents [4, 23].

Neuronal network oscillations cover a large range of frequency bands, and different oscillation patterns occur in different functional states of the brain which, in turn, reflect specific cognitive-behavioral states of the organism [15]. This raises the question of how brain-endogenous and somatically generated oscillations co-occur and interact. A recent example illustrates the complexity of such interactions: brain-endogenous theta (θ, 5–12 Hz) strongly modulate local gamma (γ, 40–160 Hz) oscillations, a fundamental pattern for cortical computation and cognition [6, 10, 17, 37, 41, 55, 56]. Recordings from freely behaving mice have shown that the prominent θ-γ-coupling during REM sleep [48] is modulated by respiration rate, expressing maximum coupling at a moderate respiration frequency of around 5 Hz [20]. This modulation may be associated to an intermediate level of arousal which is reflected by breathing frequency during REM sleep [54, 62].

Thus, brain-endogenous and body rhythms show complex, state-dependent interactions which are likely to be functionally relevant. It is therefore important to untangle the effects of different oscillations on neuronal discharges, especially in regions where oscillations with overlapping frequency bands co-occur. This is the case for θ- and respiration-related (RR) oscillations in cortical networks of the mouse [12, 63]. Here, we studied the impact of both oscillation patterns on neuronal discharge behavior in the posterior parietal cortex, a multi-modal associative area with known modulation by both θ [51] and respiration [32]. As a reference signal for respiratory activity (‘Resp’), we measured rhythmic pressure fluctuations in a plethysmograph, which is much more stable than the fluctuating field potentials reflecting RR[30]. Unit activity was recorded in two vigilance states with prominent θ activity (active waking and REM sleep) and in two states without significant θ power (waking immobility and non-REM sleep (NREM)). Our results reveal a strong modulation of unit discharges by both θ and Resp. The relative impact of either oscillation differs among states, suggesting different and independent functions of brain-endogenous and somatic feedback-generated oscillations for cognition and behavior.

All procedures complied with guidelines of the European Science Foundation and the US National Institute of Health Guide for the Care and Use of Laboratory Animals, and were approved by the State Supervisory Panel on Animal Experiments of Baden-Württemberg (35–9185.81/G-84/13, 35-9185.81/G-137/17, 35-9185.81/G-44/16). Parts of the data recorded during REM sleep and waking immobility have been previously published in a different context [32].

Animal care and housing

Mice of the C57BL/6N strain were obtained from Charles River and housed inside a ventilated Scantainer with an inverted 12/12 hour light-dark cycle (light on at 8:00 p.m.) and free access to water and food.

Surgery and electrode implantation

A total of thirteen male mice weighing 23–30 g (13–21 weeks old) were anesthetized with a mixture of isoflurane and medical oxygen (4% isoflurane for induction, 1.5–2.5% for maintenance). In 10 animals, between 5 to 7 tetrodes were chronically implanted into the right and left parietal cortex at various depths (2 mm posterior bregma, 1.5 mm lateral, 0 to 0.8 mm ventral). In three animals, 16-channel silicon probes (A1x16-3mm-50-177-CM16LP; NeuroNexus Technologies) were chronically implanted into the right parietal cortex. Inter-electrode distance was 50 µm, and implantation was perpendicular to the cortical surface such that the uppermost electrode was located superficially, the lowest at 750 µm). For further details, see [32].

Electrophysiology in freely moving mice

After one week of recovery, the animals were habituated to a whole-body plethysmograph (EMKA Technologies, S.A.S., France; [20, 30] which was adapted for simultaneous recording of respiration (Resp) and brain electrophysiology. Although respiration-related rhythms (RR) are detectable in the parietal cortex [57], they vary strongly depending on vigilance states, respiration frequency [19, 30], and other hitherto unknown factors. To avoid signal instabilities due to this variability of RR, we used the Resp signal instead, which is directly derived from respiration-induced pressure changes in the plethysmograph (see Methods) and remains stable throughout all vigilance states. Movements were detected with 3-D accelerometry. Animals were recorded in several sessions of up to 4 hours on consecutive days.

Behavioral staging

Artifact-free periods of recorded potentials were visually identified. Animals with linear silicon probes had excessive amounts of movement artefacts during active waking. We therefore limited analysis of this particular state to the remaining 10 animals with implanted tetrodes. Behavioral and vigilance state was assessed according to accelerometer activity (active waking > wake immobility or sleep), slow-wave activity (NREM > wake immobility, active waking, REM) and regular θ rhythm (preset in REM and active waking). For further details, see [7].

Data analysis

Built-in and custom-written routines in MATLAB (The MathWorks) were used for data analysis. For analysis of power spectral density, we used the pwelch.m function (50% overlap, 4-s Hamming windows). Spike detection, sorting and quality assessment was done using the sorting algorithm “Wave_clus” [11, 44], similar to [32]. Units with fewer than 100 spikes during the total recording time were excluded. Four quality tests were applied to the initial results of sorting: 1) the Hill test [27]: <1% of detected spikes occur within the first 1 ms after a spike; 2) isolation distance [21], which estimates the distance of each cluster to other clusters (> 15); 3) cluster quality L_ratio (< 0.25). L specifies the degree of separation between two clusters, a low value of L indicates that the cluster is well separated. However, clusters may be of different size and L divided by the number of spikes (L_ratio) allows larger clusters to tolerate more contamination [49]; 4) correlation coefficient (r < 0.98) between spike waveforms for each unit across all recording sites (four for tetrodes and three for probes). Firing rates: Periods of non-spiking (larger than the longest inter-spike interval which was validated for each unit by using all spikes) and bursts (intra-burst interval of 5 ms and less) were excluded from the calculation of firing rates. Spike-field coupling: Phase time series were obtained by using the hilbert.m function (for details, see [32]). Spike-phase distributions were then calculated by relating the spike times with the instantaneous phases of the oscillating field potential. The Rayleigh test for uniformity of circular distributions was used to check for statistical significances. Coupling strength to Resp and θ was estimated using the circular mean resultant length (R).

Histology

After the last experiment, the animals were deeply anaesthetized with ketamine-xylazine (30mg/kg + xylazine 10 mg/kg) and perfused transcardially with PBS followed by 4% paraformaldehyde. The position of tetrodes and multichannel probes was then verified by fluorescence (staining with DAPI: 4,6-Diamidin-2-phenylindol) and light microscopy. For further details, see [32].

Statistics

Data are presented as means ± SEM for Gaussian distributions or as medians ± 25/75% percentiles for non-Gaussian distributions. The Mann Whitney U test was applied for comparing coupling strength. For comparisons of proportions, the Chi-squared test was used. Two-sample testing of circular data was performed with the Watson’s U2 test. p values of < 0.05 were considered as statistically significant.

We recorded field potentials and respiration (Resp) from 13 freely behaving male mice (see Methods). In line with previous publications [7, 57, 65], we found a strong state-dependence of θ oscillations and slow-wave activity in the posterior parietal cortex: During REM sleep and in active waking (AW), local field potentials (LFP) were dominated by θ. In contrast, high voltage slow waves were characteristic during NREM sleep. During waking immobility (WI), both slow waves and θ were absent.

Unit firing is modulated by respiration

The presence of respiratory rhythms (RR) in the parietal cortex depends on behavioral states [57] and varies strongly with respiration frequency [30] and probably further factors [19]. We therefore used the respiration (Resp) signal from plethysmography which could be reliably recorded during all four behavioral or vigilance states (see Methods). In the θ–containing states (AW, REM), however, the effects of Resp and θ on neuronal activity could only be separated when the oscillations exhibited different frequencies. Figure 1A shows an example recording during AW with Resp rates exceeding and partly overlapping the frequency of θ in the LFP (see power spectral densities in Fig. 1B). In this example, we illustrate the detection, waveform and rhythmic modulation of an identified unit (Fig. 1C-F). This unit is significantly coupled to respiration when the breathing frequency is > 4 Hz (fast respiration, coupling strength R = 0.123, Fig. 1D). In this situation, firing probability is highest between maxima of expiration (Ex, 90 deg) and inspiration (In, 270 deg, Fig. 1E). At the same time, this example unit is not modulated by θ (5–15 Hz, Fig. 1F; for modulation by slow respiration see below). Similar analyses were applied to all recorded units in all animals and states (n = 1543), giving a systematic account of state-dependent unit entrainment by θ and Resp.

Unit coupling depends on vigilance state

Power spectral densities of Resp and θ were largely overlapping in AW (Fig. 2A right panel) and partially overlapping in REM (Fig. 2A left panel), whereas θ was absent in NREM and WI (Fig. 2A middle panels). Depending on vigilance state, the animal’s breathing frequency varied strongly between 1 and 14 Hz. For further analysis, we differentiated between slow (1–4 Hz) and fast (> 4 Hz) Resp. Both slow (sR) and fast Resp (fR) were present in REM and WI. In contrast, NREM contained almost exclusively sR, and AW almost exclusively fR. The variability in breathing rate reflects the differences in vigilance and in physical activity among the respective states. The percentage of units modulated by θ, sR, fR or by both θ and Resp simultaneously (referred to as “both” units) varied among vigilance states (Fig. 2B). As expected, the percentage of units modulated by any of the slow rhythms (θ and Resp, pooled) was significantly higher in states with θ present compared to non-θ states (see Table 1 for details). Moreover, the percentage of units coupled to Resp and both Resp and θ simultaneously was larger in AW compared to REM. Interestingly, in spite of the presence of θ in both states, significantly more units were modulated by θ in REM compared to AW. In contrast, significantly more units were modulated by Resp in AW compared to REM. Finally, coupling percentage to Resp was not different between WI and NREM. In summary, the coupling of units to θ or Resp is highly state-dependent. More units are modulated by θ in REM whereas more units are modulated by Resp in AW.

Unit coupling depends on firing rate

Next we analyzed whether unit coupling to θ or Resp was influenced by the mean firing rate (FR). For estimation of FR, bursts and spiking gaps were excluded (see Methods). We found that the percentage of units coupled to θ or Resp varied with FR in a state-dependent manner. During REM, coupling percentage to θ increased with increasing FR (Fig. 2C left, see details in Table 1); in contrast, during AW the percentage of coupled units showed a non-significant trend to decrease (Fig. 2C right). Coupling to Resp increased with FR in REM, NREM and AW but not in WI (Fig. 2C and Table 1). In conclusion, unit coupling to either rhythm varies with FR, but this correlation depends on behavioral or vigilance state.

Coupling strength depends on vigilance state.

We next measured the strength of coupling between individual units and the underlying oscillations. Table 1 shows the outcome of statistical tests for the most relevant comparisons. We found that coupling strength for units exclusively coupled to θ (and not Resp) was higher in REM compared to AW (Fig. 2D left). Coupling strength to sR was lower in NREM compared to REM and WI (Fig. 2D). In WI, coupling strength to fR was higher than to sR (Fig. 2D). Coupling strength to fR was highest in WI compared to REM and lowest in AW (Fig. 2D right). In all, we conclude that coupling strength differs among vigilance states, between the two types of slow rhythms, and also with respiration frequency.

Unit firing and phase preference.

Finally, we looked for phase preference of units modulated by θ or Resp and found pronounced state-specific differences. Preference to θ phase differed between REM and AW (Fig. 3A). Preference to either inspiration or expiration in units coupled to sR was different in NREM compared to REM and WI (Fig. 3B, upper row). For units modulated by fR, Resp phase preference differed in all three states: REM, WI and AW (Fig. 3B, lower row). For statistics, see Table 1.

Table 1

Details of unit behavior. AW: active waking; WI: wake immobility; NREM: non-REM sleep; REM: REM sleep; Both: units modulated by and respiration simultaneously; : units modulated by ; Resp: units modulated by respiration; fR: units modulated by fast respiration; sR: units modulated by slow respiration; None: units not modulated, neither by nor by respiration.
Overview of statistical differences
Percentage of units with coupling to θ or Resp
Unit type			Property			Test	p
All except None			θ-states > non-θ states			Chi-square	< 0.0001
All except None			REM < AW			Chi-square	0.0017
θ + Both			REM > AW			Chi-square	0.0007
Resp + Both			REM < AW			Chi-square	< 0.0001
Resp + Both			WI = NREM			Chi-square	0.6541
Dependence of coupling on firing rate
Unit type		State			Property	Test	p
θ + Both		REM			↑ with firing rate	Chi-square	< 0.0001
θ + Both		AW			no change	Chi-square	0.9971
Resp + Both		REM			↑ with firing rate	Chi-square	0.003
Resp only		NREM			↑ with firing rate	Chi-square	< 0.0001
Resp only		AW			↑ with firing rate	Chi-square	0.0008
Resp only		WI			no change	Chi-square	0.1052
Coupling strength
Unit type	Rhythm			Property		Test	p
θ only	θ			REM > AW		Mann-Whitney	0.0136
Both	θ			REM = AW		Mann-Whitney	0.9476
sR only	Resp			REM > NREM		Dunn’s multiple comparison	< 0.0001
sR only	Resp			REM = WI		Dunn’s multiple comparison	0.9999
sR only	Resp			NREM < WI		Dunn’s multiple comparison	< 0.0001
fR only	Resp			REM > AW		Dunn’s multiple comparison	< 0.0001
fR only	Resp			WI > AW		Dunn’s multiple comparison	< 0.0001
fR only	Resp			REM = WI		Dunn’s multiple comparison	0.4658
State	Rhythm			Property		Test	p
REM	θ			θ only > Both		Mann-Whitney	0.0028
WI	Resp			sR only < fR only		Mann-Whitney	0.0013
REM	Resp			sR only = fR only		Mann-Whitney	0.9421
Phase preference
Unit type	Property					Test	p
θ + Both	REM vs. AW/ different					Watson U²	< 0.0001
sR only	REM vs. NREM/ different					Watson U²	< 0.0001
sR only	NREM vs. WI/ different					Watson U²	< 0.0001
sR only	REM vs. WI/ not changed					Watson U²	0.1955
fR only	REM vs. WI/ different					Watson U²	0.0223
fR only	REM vs. AW/ different					Watson U²	0.0058
fR only	WI vs. AW/ different					Watson U²	< 0.0001

Our data shows specific and robust modulation of units in the parietal cortex by both respiration and θ. However, the rhythmic entrainment of units by either of the two slow oscillations depends strongly on (i) vigilance state and (ii) mean firing rate (FR). In order to gain a systematic overview, we compared all major sleep and waking states. In two of these states, active waking (AW) and REM sleep, both oscillations are simultaneously present. At the field potential level θ oscillations are dominant in both states. At the cellular (unit) level, however, entrainment by respiration is prevailing during AW while θ is more efficient during REM sleep. This observation emphasizes potential discrepancies between the observed power of network-level oscillations and behavior of individual neurons which should be kept in mind when interpreting collective neuronal signals (field potentials, EEG, MEG). A second finding of the present study is that in several states coupling of units to θ or Resp, respectively, was dependent on discharge frequency. Whether this activity-dependence of entrainment reflects differences in network integration of different cell types or of differentially active neurons of the same type is presently unclear. In any case, the correlation between neuronal activity and coupling to slow network oscillations may be important for understanding ensemble activity in oscillating networks. A further new observation is that units couple to different phases of respiration depending on the behavioral state. This finding may be explained by state-dependent differences in respiration-related synaptic input to the parietal network, by recruitment of different types of interneurons within the parietal cortex, or by other, more complex state-dependent interactions between different networks. In any case, all findings underline the strong state-dependence of unit entrainment by both, respiration and θ oscillations.

Some of the present findings from freely moving mice confirm our earlier results from head-fixed [12] and urethane-anaesthetized animals [63]. In these preparations, we had already reported respiration-driven modulation of hippocampal [57, 63] and posterior parietal and prelimbic neurons [32, 65]. While our previous observations were mainly done in REM sleep or wake immobility, we here provide a systematic comparison of modulation by θ and respiration during all major states of vigilance/behavior: REM sleep, NREM sleep, waking immobility and active waking. The latter is of particular interest since it contains θ oscillations and, at the same time, strong respiration-coupled inputs from sniffing behavior. In this state, entrainment of neurons by respiration was dominant, with particularly strong effects on neurons with high discharge frequency. It is well feasible that this reflects the integration of sensory signals from nasal respiration or sniffing in the multimodal association network of the parietal cortex.

Earlier reports have shown that neurons in several neocortical regions including the parietal cortex of rats and mice are modulated by theta oscillations during locomotion and REM sleep [51]. Here, we confirm this finding for REM sleep while foraging behavior could not be tested within the limited space inside the plethysmograph. During active waking, neurons were entrained by both θ and respiration (sniffing), with dominant modulation by respiration. A similar concomitant modulation of neurons by θ and respiration has been described by Biskamp and colleagues for medial prefrontal cortex neurons in mice [5]. It may well be that theta becomes more efficient for unit entrainment in states of locomotion, but it may also be worth-while testing the strength of respiration-driven entrainment in this situation. In fact, theta and respiration can easily be confounded in mice due to their strongly overlapping frequency ranges [53].

It is unclear how respiration-entrained signals are transmitted to distant cortical brain areas. Clearly, feedback from the nasal airstream plays a role, since RR diminishes after tracheotomy [63], bulbectomy [4, 5], chemogenetic inhibition of the OB [38], depletion of the olfactory epithelium [33, 39] or nasal occlusion [39]. It may well be that mechanic stimulation of olfactory epithelial cells by the airflow [1] are critical for RR generation [53]. However, Karalis and Sirota [33] recently demonstrated that lesioning the olfactory epithelium abolishes RR at the field potential level but does not eliminate neuronal entrainment by respiration. This finding argues for additional non-nasal sources of respiration-related activity modulation, possibly by collateral discharges from the rhythm-generating respiratory networks in the brainstem. An additional possibility are direct mechanical effects on neuronal activity. Cortical neurons do react to weak mechanical stimulation [42], and mechanosensitive Piezo2 ion channels have recently been shown to be present in cortical pyramidal cells, opening the possibility that minor pressure fluctuations in the brain parenchyma may translate into rhythmic entrainment of activity [3, 14]. Mechanical transduction processes could likewise mediate the heartbeat-dependent modulation of activity [13, 25, 60] for which there is no central rhythm generator. Our present results on modulation of neuronal activity by respiration do not allow to distinguish between the different possible mechanisms which are, notably, not mutually exclusive. In any case, we confirm that the power of the local field potential is not consistently correlated with the strength of unit entrainment [33], suggesting that lamina-specific synaptic input is not the only mechanism of respiration-entrained neuronal discharges. Which of the two other mechanisms (corollary discharges from brainstem rhythm generators or mechanical stimulation of pyramidal cells) is responsible for the observed modulation of neuronal activity remains presently unclear.

Respiration-related network activity is a brain-wide phenomenon [57], and respiratory modulation of unit activity was demonstrated in a large number of brain regions [4, 5, 12, 32, 33, 35, 63]. This poses the question of its putative role for brain function. It has been suggested that the brain-wide coordination of activity by slow network oscillations contributes to signal integration between different neuronal networks [15, 28, 31, 51]. Respiration-related network oscillations (RR) may provide such a synchronizing signal, independent from their immediate relation to respiration or olfaction. The parietal cortex was found to play a critical role in decision-making processes [2, 22, 36, 40, 43] that strongly rely on the brain-wide integration of sensory information, behavioral and internal state and intended actions. It seems possible that RR provides a temporal scaffold for the underlying computations in the parietal cortex. Moreover, the parietal cortex serves important roles in spatial navigation [22, 36, 61]. Whether spatial cognition is specifically modulated by respiration is currently unknown, but would be well compatible with our present findings, especially the state-dependent expression of RR and its coordination with θ oscillations. A closely related cognitive process is spatial or declarative memory formation [9]. Of note, respiratory signals modulate hippocampal sharp-wave ripples [33, 38] – a biomarker of memory consolidation [8] – implicating a role of respiration in the underlying processes [16, 23]. Importantly, the presence of localized, concurrent ripple oscillations in the parietal cortex was recently observed in rats [34] possibly aiding information transfer from hippocampal to neocortical networks during memory consolidation. Interestingly, a recent study by Tingley and colleagues [52] found that hippocampal sharp wave-ripples additionally influence metabolic processes, highlighting the embeddedness of brain function within the whole body [50, 58] in which respiratory signals potentially serve a critical role [59].

Taken together, accumulating evidence shows the impact of bodily signals such as respiration on brain dynamics, opening possibilities to formulate and test novel hypotheses on the interaction between neuronal activity, behavior and cognition. In addition, our present findings underline the state-dependence of entrainment of neocortical neurons, which can be driven by θ, respiration or both oscillations depending on firing rate and behavioral state.

Data availability

The data will be available upon request.

Acknowledgement

We thank Katja Lankisch for expert technical assistance.

Funding

This work was supported by the German Research Foundation (DR 326 15-1), the Brazilian National Council for Scientific and Technological Development (CNPq 308653/2018-1; 408508/2018-3), the Brazilian Coordination for the Improvement of Higher Education Personal (CAPES 99999.000558/2016-08), and the Alexander von Humboldt Foundation.

Competing interests

The authors have no financial or non-financial interests to declare.

Author contributions

FJ and YY performed experiments, contributed to data analysis, figures and manuscript writing. JB designed experiments and performed data analysis, validation of results and manuscript writing. ABLT designed and applied advanced methods of data analysis. AD supervised the laboratory work and finalized the manuscript. All authors reviewed the manuscript.

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No competing interests reported.

Respiratory Entrainment of Units in the Mouse Parietal Cortex Depends on Vigilance State

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