The world's most-cited Neurosciences journals

General Commentary ARTICLE

Front. Syst. Neurosci., 05 June 2018 | https://doi.org/10.3389/fnsys.2018.00025

Commentary: Respiration-Entrained Brain Rhythms Are Global but Often Overlooked

  • 1Cognition and Philosophy Laboratory, School of Philosophical, Historical and International Studies, Monash University, Melbourne, VIC, Australia
  • 2Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy

A commentary on
Respiration-Entrained Brain Rhythms Are Global but Often Overlooked

by Tort, A. B. L., Brankačk, J., and Draguhn, A. (2018). Trends Neurosci. 41, 186–197. doi: 10.1016/j.tins.2018.01.007

Tort et al. (2018a) provide a timely review of the growing body of evidence implicating respiration-entrained oscillations as a distinctive class of rhythmic brain activity. In rodents, these olfactory-driven “respiratory rhythms” are dissociable from concomitant low-frequency oscillations in the delta and theta bands, and have been shown to modulate high-frequency (gamma) activity in non-olfactory regions. Tort et al. (2018a; see also Zhong et al., 2017) hypothesize that these rhythms help co-ordinate the integration of distributed neural assemblies, echoing recent claims that breathing-related afferent input constitutes a fundamental organizing principle of oscillatory brain activity (Heck et al., 2017; Herrero et al., 2018). They stop short however of speculating about the precise functional role of these dynamics within the scheme of cognitive processing.

Certainly, more work needs to be done to establish the pervasiveness of respiratory rhythms with respect to non-olfactory processing, and to rule out potential confounds such as volume conduction (see Tort et al., 2018b). This caveat notwithstanding, we propose an elaboration of Tort et al.'s (2018a) hypothesis that interprets respiration-entrained oscillations in the context of active sensing, before briefly considering how this account might be subsumed under the active inference framework.

1. Active Sensing and Temporal Prediction

Active sensing captures the idea that biological sensation is typically contingent upon selective sampling routines involving motor and attentional processes (Schroeder et al., 2010). The dependence of mammalian olfaction on the induction of airflow through the nasal cavity, and the ease with which respiration can be modulated to optimize odor perception (Verhagen et al., 2007), make it a paradigmatic example of active sensing (Wachowiak, 2011).

In freely-exploring rodents, sniffing bouts are systematically coupled with rhythmic nasal, head, and whisking movements, resulting in complex ensembles of phase-locked behavior (Welker, 1964; Deschênes et al., 2012; Moore et al., 2013; Ranade et al., 2013). As previously suggested (see for e.g., Buonviso et al., 2003; Kepecs et al., 2006; Kleinfeld et al., 2014; Kurnikova et al., 2017), one advantage of this arrangement is that multiple sensorimotor channels can be co-ordinated to optimize the selective integration (or segregation) of various streams of information. Active sensing implies that the rhythmically-patterned activity induced by sniffing and whisking establishes a temporal regime under which discrete bursts of afferent input coincide with phasic cycles of neuronal excitability. This process is posited to enhance the gain on relevant sensory information, effectively amplifying (or filtering) salient signals at the expense of competing or irrelevant stimuli (Schroeder et al., 2010; Morillon et al., 2015). From a predictive processing perspective, rhythmic activity induced by sniffing and whisking may specifically support predictive timing and the necessary temporal alignment of bottom-up sensory streams with top-down predictive streams (Arnal and Giraud, 2012; Pezzulo et al., 2017).

2. Respiration as Active Sensing

We believe that framing respiration-entrained oscillations in terms of active sensing and temporal prediction delivers at least three useful insights.

First, this perspective contextualizes respiratory rhythms within a more general account of oscillatory-driven attentional selection and sensory enhancement (Lakatos et al., 2008; Schroeder and Lakatos, 2009). This scheme is of course entirely consistent with Tort et al.'s (2018a) hypothesis that respiratory rhythms facilitate inter-regional communication via cross-frequency coupling. Furthermore, it also accommodates complementary behavioral evidence that non-olfactory sensorimotor processing is modulated by respiratory phase (Schulz et al., 2016), thus yielding a framework that may unify these lines of research.

Second, the intrinsically cyclical nature of the sensorimotor dynamics assumed under active sensation encourages us to conceive of respiratory rhythms as being coherent with (or even the consequence of) central processes—namely, endogenous brain oscillations—rather than in terms of purely bottom-up sensory entrainment. That is, even though the activity of the neural circuits controlling respiration may not be sufficient to induce respiration-entrained oscillations per se (Tort et al., 2018a), these generators are ultimately responsible for dictating the precise timing of nasal stimulation, and thus for promoting the temporal co-ordination between internal brain dynamics and the active sampling of sensory stimuli. This remark highlights a subtle distinction between neural entrainment to (centrally-driven) respiratory cycles, vs. more commonly-described forms of entrainment to (passively received) external stimuli.

Relatedly, we note that the central co-ordination of respiratory timing likewise distinguishes breathing-related (re)afferent input from the periodic interoceptive signals generated by autonomous peripheral pacemakers, such as the heart and gut. This raises the question of whether the reafferent feedback loops established by respiration are in some sense unique, or whether other sources of rhythmically-patterned autonomic activity might similarly contribute to the organization of neural oscillations. Indeed, given that the cardiac cycle is known to influence sensory and cognitive processing (Critchley and Garfinkel, 2018), is itself modulated by respiratory phase (Berntson et al., 1993), and correlates with sniffing/whisking-entrained limbic oscillations in rats (Komisaruk, 1970), future research might profit from delineating the intricate oscillatory dynamics structuring neuro-cardio-respiratory interactions.

3. Respiration as Active Inference

Finally, active sensing implies the instantiation of spatiotemporal predictions about the existence of relevant sensory objects in the external world (Morillon et al., 2015). We believe this account is compatible with (and indeed, subsumed by) active inference, a biologically-plausible implementation of the free energy principle (Friston et al., 2017a). Under this scheme, perception and action serve to minimize variational free energy, an information-theoretic bound on the surprise (negative log-probability) induced by sensory states, given a probabilistic (generative) model of the causes of those states (Friston, 2010). While perception minimizes the difference between current and expected states to infer the most probable causes of sensory inputs, action seeks new sensory inputs that realize expected (i.e., goal) states. In other words, agents are most likely to select actions (and action sequences or policies) that minimize the expected free energy of future states (Friston et al., 2015).

A corollary of this formulation is that action serves to optimize the trade-off between epistemic value (i.e., information gain) and expected utility (i.e., reward). On this view, sniffing/whisking cycles can be construed as instances of “epistemic foraging,” where the rodent acts to reduce uncertainty about the state of their environment by engaging in behaviors that disclose its structure and affordances (Friston et al., 2015, 2017a,b). (Note that bouts of active sensory sampling may be embedded within larger-scale periodic activities, such as repetitive patterns of spatial navigation; see Lebedev et al., 2018). Once ambiguity or uncertainty has been sufficiently resolved, the rodent may then switch to policies that exploit the resources availed by the environment. This perspective thus situates active sensation within the broader contexts of predictive processing, information gain and policy selection, while also relating respiratory-driven oscillatory dynamics to more general imperatives governing physiological regulation—as in active inference, uncertainty-reduction is ultimately functional to selecting adaptive action (Pezzulo et al., 2015, 2018).

4. A Global Scheme of Hierarchically-Nested Rhythms?

Beyond the rodent literature, Tort et al. (2018a) briefly consider emerging evidence that nasal-respiration exerts analogous influences over non-olfactory cortical oscillations in humans. Since human respiratory rates typically fall below the delta band (<0.5 Hz), these findings support the notion that the low-frequency rhythms typically studied in M/EEG experiments might themselves be nested within slow (and perhaps in turn, ultra-slow) oscillations (cf. Penttonen and Buzsáki, 2003; Klimesch, 2013). Extending this idea, we suggest that additional, interoceptive sources of slow oscillatory input (e.g., baroreceptor feedback) might likewise constitute global (but often overlooked) rhythms structuring higher-frequency brain activity (see Richter et al., 2017; Mather and Thayer, 2018; Rebollo et al., 2018, for evidence corroborating this view). This picture invites consideration of the inherent causal circularity at the heart of brain-body interactions, whereby endogenous neural oscillations and rhythmic physiological fluctuations reciprocally condition and constrain one another. Active inference, insofar as it encompasses hierarchical temporal predictions integrating across autonomic and somato-sensorimotor loops, presents an ideal framework for generating and testing such hypotheses.

Author Contributions

AC drafted the first version of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

AC is supported by an Australian Government Research Training Program (RTP) scholarship. JH is supported by The Australian Research Council DP160102770 and by the Research School Bochum and the Center for Mind, Brain, and Cognitive Evolution, Ruhr-University Bochum.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We wish to thank the reviewers for their helpful comments on an earlier version of this manuscript.

References

Arnal, L. H., and Giraud, A.-L. (2012). Cortical oscillations and sensory predictions. Trends Cogn. Sci. 16, 390–398. doi: 10.1016/j.tics.2012.05.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Berntson, G. G., Cacioppo, J. T., and Quigley, K. S. (1993). Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology 30, 183–196.

PubMed Abstract | Google Scholar

Buonviso, N., Amat, C., Litaudon, S. R., Royet, J.-P., Farget, V., and Sicard, G. (2003). Rhythm sequence through the olfactory bulb layers during the time window of a respiratory cycle. Eur. J. Neurosci. 17, 1811–1819. doi: 10.1046/j.1460-9568.2003.02619.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Critchley, H. D., and Garfinkel, S. N. (2018). The influence of physiological signals on cognition. Curr. Opin. Behav. Sci. 19, 13–18. doi: 10.1016/j.cobeha.2017.08.014

CrossRef Full Text | Google Scholar

Deschênes, M., Moore, J., and Kleinfeld, D. (2012). Sniffing and whisking in rodents. Curr. Opin. Neurobiol. 22, 243–250. doi: 10.1016/j.conb.2011.11.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Friston, K. J. (2010). The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138. doi: 10.1038/nrn2787

PubMed Abstract | CrossRef Full Text | Google Scholar

Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P., and Pezzulo, G. (2017a). Active inference: a process theory. Neural Comput. 29, 1–49. doi: 10.1162/NECO

PubMed Abstract | CrossRef Full Text | Google Scholar

Friston, K. J., Lin, M., Frith, C. D., Pezzulo, G., Hobson, J. A., and Ondobaka, S. (2017b). Active inference, curiosity and insight. Neural Comput. 29, 2633–2683. doi: 10.1162/neco

PubMed Abstract | CrossRef Full Text | Google Scholar

Friston, K. J., Rigoli, F., Ognibene, D., Mathys, C. D., Fitzgerald, T., and Pezzulo, G. (2015). Active inference and epistemic value. Cogn. Neurosci. 6, 187–224. doi: 10.1080/17588928.2015.1020053

PubMed Abstract | CrossRef Full Text | Google Scholar

Heck, D. H., McAfee, S. S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W. J., et al. (2017). Breathing as a fundamental rhythm of brain function. Front. Neural Circuits 10:115. doi: 10.3389/fncir.2016.00115

PubMed Abstract | CrossRef Full Text | Google Scholar

Herrero, J. L., Khuvis, S., Yeagle, E., Cerf, M., and Mehta, A. D. (2018). Breathing above the brain stem: volitional control and attentional modulation in humans. J. Neurophysiol. 119, 145–159. doi: 10.1152/jn.00551.2017

PubMed Abstract | CrossRef Full Text | Google Scholar

Kepecs, A., Uchida, N., and Mainen, Z. F. (2006). The sniff as a unit of olfactory processing. Chem. Senses 31, 167–179. doi: 10.1093/chemse/bjj016

PubMed Abstract | CrossRef Full Text | Google Scholar

Kleinfeld, D., Deschênes, M., Wang, F., and Moore, J. D. (2014). More than a rhythm of life: breathing as a binder of orofacial sensation. Nat. Neurosci. 17, 647–651. doi: 10.1038/nn.3693

PubMed Abstract | CrossRef Full Text | Google Scholar

Klimesch, W. (2013). An algorithm for the EEG frequency architecture of consciousness and brain body coupling. Front. Hum. Neurosci. 7:766. doi: 10.3389/fnhum.2013.00766

PubMed Abstract | CrossRef Full Text | Google Scholar

Komisaruk, B. R. (1970). Synchrony between limbic system theta activity and rhythmical behavior in rats. J. Compar. Physiol. Psychol. 70, 482–492.

PubMed Abstract | Google Scholar

Kurnikova, A., Moore, J. D., Liao, S.-M., Deschênes, M., and Kleinfeld, D. (2017). Coordination of orofacial motor actions into exploratory behavior by rat. Curr. Biol. 27, 688–696. doi: 10.1016/j.cub.2017.01.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., and Schroeder, C. E. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320, 110–113. doi: 10.1126/science.1154735

PubMed Abstract | CrossRef Full Text | Google Scholar

Lebedev, M., Pimashkin, A., and Ossadtchi, A. (2018). Navigation patterns and scent marking: underappreciated contributors to hippocampal and entorhinal spatial representations? Front. Behav. Neurosci. 12:98. doi: 10.3389/fnbeh.2018.00098

CrossRef Full Text | Google Scholar

Mather, M., and Thayer, J. F. (2018). How heart rate variability affects emotion regulation brain networks. Curr. Opin. Behav. Sci. 19, 98–104. doi: 10.1016/j.cobeha.2017.12.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Moore, J. D., Deschênes, M., Furuta, T., Huber, D., Smear, M. C., Demers, M., et al. (2013). Hierarchy of orofacial rhythms revealed through whisking and breathing. Nature 497, 205–210. doi: 10.1038/nature12076

PubMed Abstract | CrossRef Full Text | Google Scholar

Morillon, B., Hackett, T. A., Kajikawa, Y., and Schroeder, C. E. (2015). Predictive motor control of sensory dynamics in auditory active sensing. Curr. Opin. Neurobiol. 31, 230–238. doi: 10.1016/j.conb.2014.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Penttonen, M., and Buzsáki, G. (2003). Natural logarithmic relationship between brain oscillations. Thalamus Relat. Syst. 2, 145–152. doi: 10.1017/S1472928803000074

CrossRef Full Text | Google Scholar

Pezzulo, G., Kemere, C., and van der Meer, M. A. A. (2017). Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Ann. N.Y. Acad. Sci. 1396, 144–165. doi: 10.1111/nyas.13329

PubMed Abstract | CrossRef Full Text | Google Scholar

Pezzulo, G., Rigoli, F., and Friston, K. J. (2015). Active inference, homeostatic regulation and adaptive behavioural control. Progr. Neurobiol. 134, 17–35. doi: 10.1016/j.pneurobio.2015.09.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Pezzulo, G., Rigoli, F., and Friston, K. J. (2018). Hierarchical active inference: a theory of motivated control. Trends Cogn. Sci. 22, 294–306. doi: 10.1016/j.tics.2018.01.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Ranade, S., Hangya, B., and Kepecs, A. (2013). Multiple modes of phase locking between sniffing and whisking during active exploration. J. Neurosci. 33, 8250–8256. doi: 10.1523/JNEUROSCI.3874-12.2013

PubMed Abstract | CrossRef Full Text | Google Scholar

Rebollo, I., Devauchelle, A.-D., Béranger, B., and Tallon-Baudry, C. (2018). Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans. eLife 7:e33321. doi: 10.7554/eLife.33321

PubMed Abstract | CrossRef Full Text | Google Scholar

Richter, C. G., Babo-Rebelo, M., Schwartz, D., and Tallon-Baudry, C. (2017). Phase-amplitude coupling at the organism level: the amplitude of spontaneous alpha rhythm fluctuations varies with the phase of the infra-slow gastric basal rhythm. Neuroimage 146, 951–958. doi: 10.1016/j.neuroimage.2016.08.043

PubMed Abstract | CrossRef Full Text | Google Scholar

Schroeder, C. E., and Lakatos, P. (2009). Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci. 32, 9–18. doi: 10.1016/j.tins.2008.09.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Schroeder, C. E., Wilson, D. A., Radman, T., Scharfman, H., and Lakatos, P. (2010). Dynamics of active sensing and perceptual selection. Curr. Opin. Neurobiol. 20, 172–176. doi: 10.1016/j.conb.2010.02.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Schulz, A., Schilling, T. M., Vögele, C., Larra, M. F., and Schächinger, H. (2016). Respiratory modulation of startle eye blink: a new approach to assess afferent signals from the respiratory system. Philos. Trans. R. Soc. B 371, 1–9. doi: 10.1098/rstb.2016.0019

PubMed Abstract | CrossRef Full Text | Google Scholar

Tort, A. B. L., Brankačk, J., and Draguhn, A. (2018a). Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci. 41, 186–197. doi: 10.1016/j.tins.2018.01.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Tort, A. B. L., Ponsel, S., Jessberger, J., Yanovsky, Y., Brankačk, J., and Draguhn, A. (2018b). Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain. Sci. Rep. 8:6432. doi: 10.1038/s41598-018-24629-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Verhagen, J. V., Wesson, D. W., Netoff, T. I., White, J. A., and Wachowiak, M. (2007). Sniffing controls an adaptive filter of sensory input to the olfactory bulb. Nat. Neurosci. 10, 631–639. doi: 10.1038/nn1892

PubMed Abstract | CrossRef Full Text | Google Scholar

Wachowiak, M. (2011). All in a sniff: olfaction as a model for active sensing. Neuron 71, 962–973. doi: 10.1016/j.neuron.2011.08.030

PubMed Abstract | CrossRef Full Text | Google Scholar

Welker, W. I. (1964). Analysis of sniffing of the albino rat. Behavior 22, 223–244.

Google Scholar

Zhong, W., Ciatipis, M., Wolfenstetter, T., Jessberger, J., Müller, C., Ponsel, S., et al. (2017). Selective entrainment of gamma subbands by different slow network oscillations. Proc. Natl. Acad. Sci. U.S.A. 114, 4519–4524. doi: 10.1073/pnas.1617249114

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: respiration, neural oscillation, active sensing, olfaction, active inference, free energy principle, prediction, interoceptive rhythm

Citation: Corcoran AW, Pezzulo G and Hohwy J (2018) Commentary: Respiration-Entrained Brain Rhythms Are Global but Often Overlooked. Front. Syst. Neurosci. 12:25. doi: 10.3389/fnsys.2018.00025

Received: 04 April 2018; Accepted: 16 May 2018;
Published: 05 June 2018.

Edited by:

Mikhail Lebedev, Duke University, United States

Reviewed by:

Ivan N. Pigarev, Institute for Information Transmission Problems (RAS), Russia

Copyright © 2018 Corcoran, Pezzulo and Hohwy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Andrew W. Corcoran, andrew.corcoran1@monash.edu orcid.org/0000-0002-0449-4883

Giovanni Pezzulo, orcid.org/0000-0001-6813-8282
Jakob Hohwy, orcid.org/0000-0003-3906-3060