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Abstract and Figures

The locus coeruleus (LC) has established functions in both attention and respiration. Good attentional performance requires optimal levels of tonic LC activity, and must be matched to task consistently. LC neurons are chemosensitive, causing respiratory phrenic nerve firing to increase frequency with higher CO2 levels, and as CO2 level varies with the phase of respiration, tonic LC activity should exhibit fluctuations at respiratory frequency. Top-down modulation of tonic LC activity from brain areas involved in attentional regulation, intended to optimize LC firing to suit task requirements, may have respiratory consequences as well, as increases in LC activity influence phrenic nerve firing. We hypothesize that, due to the physiological and functional overlaps of attentional and respiratory functions of the LC, this small neuromodulatory nucleus is ideally situated to act as a mechanism of synchronization between respiratory and attentional systems, giving rise to a low-amplitude oscillation that enables attentional flexibility, but may also contribute to unintended destabilization of attention. Meditative and pranayama practices result in attentional, emotional, and physiological enhancements that may be partially due to the LC's pivotal role as the nexus in this coupled system. We present original findings of synchronization between respiration and LC activity (via fMRI and pupil dilation) and provide evidence of a relationship between respiratory phase modulation and attentional performance. We also present a mathematical dynamical systems model of respiratory-LC-attentional coupling, review candidate neurophysiological mechanisms of changes in coupling dynamics, and discuss implications for attentional theory, meditation, and pranayama, and possible therapeutic applications.
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Coupling of Respiration and Attention via the Locus Coeruleus:
Effects of Meditation and Pranayama
Corresponding Author:
Michael Christopher Melnychuk
Institute of Neuroscience
Trinity College Dublin
Dublin, Ireland
Phone: +353 86 408 8459
Email: melnychm@tcd.ie
Paul M. Dockree
Institute!of!Neuroscience!and!School!of!Psychology!!
Trinity!College!Dublin!
Dublin,!Ireland!
Redmond G. O’Connell
Institute!of!Neuroscience!and!School!of!Psychology!
Trinity!College!Dublin!
Dublin,!Ireland!!
Peter R. Murphy
Department of Neurophysiology and Pathophysiology
University Medical Center Hamburg-Eppendorf
Hamburg, Germany
Joshua H. Balsters
Department of Psychology
Royal Holloway University of London
Egham, United Kingdom
Ian H. Robertson
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Institute of Neuroscience and Global Brain Health Institute
Trinity College Dublin
Running Head: LC Couples Respiratory-Attentional System
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1. Abstract*
The locus coeruleus (LC) has established functions in both attention and respiration. Good
attentional performance requires optimal levels of tonic LC activity, and must be matched to
task consistently. LC neurons are chemosensitive, causing respiratory phrenic nerve firing to
increase frequency with higher CO2 levels, and as CO2 level varies with the phase of
respiration, tonic LC activity should exhibit fluctuations at respiratory frequency. Top-down
modulation of tonic LC activity from brain areas involved in attentional regulation, intended
to optimize LC firing to suit task requirements, may have respiratory consequences as well, as
increases in LC activity influence phrenic nerve firing. We hypothesize that due to the
physiological and functional overlaps of attentional and respiratory functions of the LC, this
small neuromodulatory nucleus is ideally situated to act as a mechanism of synchronization
between respiratory and attentional systems, giving rise to a low-amplitude oscillation that
enables attentional flexibility, but may also contribute to unintended destabilization of
attention. Meditative and pranayama practices result in attentional, emotional, and
physiological enhancements that may be partially due to the LC’s pivotal role as the nexus in
this coupled system. We present original findings of synchronization between respiration and
LC activity (via fMRI and pupil dilation) and provide evidence of a relationship between
respiratory phase modulation and attentional performance. We also present a mathematical
dynamical systems model of respiratory-LC-attentional coupling, review candidate
neurophysiological mechanisms of changes in coupling dynamics, and discuss implications for
attentional theory, meditation and pranayama, and possible therapeutic applications.
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2 Introduction*
चले वाते चलं िच)ं िन+चले िन+चलं भवेत्
योगी 2थाणु6वम् आ9नोित ततो वायुं िनरोधयेत्
Chale vāte chala chitta niśchale niśchala bhavet
Yoghī sthāutvamāpnoti tato vāyu nirodhayet
Respiration, being disturbed, the mind becomes
Disturbed. By restraining respiration, the Yogi gets
Steadiness of mind.
Hatha Yoga Pradapika, Yogi Svatmaram
Yogis and Buddhist practitioners have long considered the breath an especially suitable object
for meditation. The choice of the breath over other possible items arose presumably not simply
because respiration provides a subtle and readily available object of focus, but because the
characteristics of respiration can be observed to change in specific ways with attentional and
emotional states. It is believed that by observing the breath, and regulating it in precise ways,
a practice known as pranayama, changes in arousal, attention, and emotional control that can
be of great benefit the meditator are realized. Innumerable anecdotal reports support these
claims, and physical, emotional, and attentional improvements have been noted in many studies
(reviews: Chiesa, 2011; Grossman, et al., 2004; Lippelt, et al., 2014; Sengupta, 2012).
We know from behavioural and imaging studies that meditative practices are associated with
improvements that activate and strengthen the frontal attentional system (Holzel, et al., 2011;
Lazar, et al., 2005; Luders, et al., 2009; Vestergaard-Poulsen, et al., 2009) and that default
mode network (DMN) activity, associated with mind-wandering states, is reduced (Brewer et
al., 2011; Taylor, et al., 2013; Wells, et al., 2013). Changes in cortical volume and white matter
connectivity have also been observed (Laneri, et al., 2015; Luders, et al., 2011; Tang, et al.,
2010, 2012) even following short periods of practice. Meditation and pranayama also produce
changes in respiration (Vyas, et al., 2002; Wallace, et al., 1972) and autonomic nervous system
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activity, as measured by habituation, frequency, and spontaneous GSR response to stressors
(Orme-Johnson, 1973).
One might suppose that the object of focus in meditation should be irrelevant, that it is the act
of focusing attention and not the object of focus – in this case the breath - that is important. But
the Buddha states clearly, in the Ananda Sutra: from the development, from the repeated
practice, of respiration-mindfulness concentration, there comes to be neither wavering nor
trembling of body, nor wavering nor trembling of mind (Sai, 2010). According to
Svatmarama, in the Hatha Yoga Pradapika (2:2) "…when the breath wanders the mind is
unsteady. But when the breath is calmed, the mind too will be still” (Muktibodhananda, 2013).
Patanjali, in the Yoga Sutras (2.53) instructs that “…through these practices and processes of
pranayama, which is the fourth of the eight steps, the mind acquires or develops the fitness,
qualification, or capability for true concentration (dharana)…” (Satchidananda, 2012). The
focus upon the breath is of clear importance in traditional practice, but how might respiration
and attention influence each other from a neurophysiological perspective?
While a few scattered scientific attempts at examining the relationship between respiration and
attention have been made (Gelhorn, et al., 1936; Lehmann, 1893; Porges, et al., 1969; Taylor,
1901; Winkler, 1898), a comprehensive theory and concrete neurobiological mechanism that
can explain the effects of respiratory monitoring and control on cognition, and vice versa, has
not been proposed. One interesting possibility is that the respiratory and attentional systems
are coupled at the neural level, such that information transfer between the two systems occurs
bi-directionally at an anatomical point where the respiratory and attentional systems overlap.
In this review, we describe respiration and attention as a coupled dynamical system.
Specifically, we hypothesize that they can be described as autonomous oscillatory systems
exhibiting coupling via information transfer through a third autonomous oscillator, the locus
coeruleus (LC). We review the neurophysiological knowledge of the relevant systems,
emphasize the influence of CO2 on LC tonic activity, the importance of LC activity to
attentional state and stability, and discuss how these may be synchronized with top-down
influences from attentional areas.
2.1 Coupled*Systems*
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Coupling, or synchronization, is a common phenomenon in nature, particularly in biological
systems. Weakly interactive forces (mechanical vibrations, heat, or sound, for example) cause
autonomously oscillating systems to tend toward a synchronized state (Huuygens, 1673;
Pikovsky, et al., 2001; Strogatz & Stewart, 1993).
This phenomenon was first described by Huygens after he lay sick in bed on a long sea journey,
observing two pendulum clocks hanging upon a common wooden beam. He noted the gradual
synchronization of the pendula, and eventually discovered that very small vibrations were
passing between clocks, through the beam upon which they were fixed, causing the pendula to
drift into, and then remain, permanently fixed, in one of two states - either perfect
synchronization, or anti-synchronization (a stable phase relationship of 180°).
As it turns out, examples of this type of synchronized behaviour are common in the natural
world (flashing fireflies, flocks of birds in flight, and slime mold behaviour, for example), and
synchronization is thought to play an important role in neural and physiological systems as
well. Neural systems exhibit phase and frequency synchronization (Buszaki & Draguhn, 2004),
both between larger functional areas (Engel, et al., 1991; Konig, et al., 1993), and individual
proximal neurons (Gray, et al., 1989). Neuronal coupling is thought to subserve perceptual
binding (Eckhorn, et al., 1988; Gray, et al., 1989), cortical communication and coordination
(Fries, 2005), and influence attention and saliency (Biederlack, et al., 2006). Non-linear
physiological coupling between the heart and respiration in human beings has also been
observed (Jamsek, et al, 2004; Schafer, et al., 1998). It is important to point out that
synchronization in the context of dynamical systems is the result of independently oscillating
systems interacting and tending toward stable inter-dynamics in the absence of external forcing
or entrainment.
2.2 Locus Coeruleus and Cognition
In mammals, the LC, a small blue bilateral nucleus in the pons, is the main source of cortical
noradrenaline (NA), and through a very specific, nearly complete and exclusive innervation of
the cortex (Loughlin, et al., 1986), plays a significant role in regulating brain function. The LC
can be loosely considered a cortical analogue of the adrenal glands, influencing arousal and
helping optimize cognitive states for varied environmental and volitional demands.
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NA is a modulatory neurotransmitter, known to be involved in regulating sleep-waking states
(Aston-Jones & Bloom, 1981; Aston-Jones, 1991), cortical arousal (Carter, et al., 2010), signal
detection threshold (Sara & Hervé-Minvielle, 1995; Segal & Bloom, 1976; Waterhouse, et al.,
1998), and decision processes (Aston-Jones, et al., 1997; Bouret and Sara, 2004; Clayton, et
al., 2004; Rajkowski, et al., 2004; Usher, et al., 1999). The LC exhibits a continuum of
behaviour, ranging from high sustained tonic firing during episodes of distractibility (Aston-
Jones, et al., 1996; Usher, et al., 1999) to near-absolute, GABA-inhibited quiescence during
REM sleep (Aston-Jones & Bloom, 1981).
While the role of the LC in waking and arousal has long been known, a recent focus on its
specific cognitive and attentional functions has revealed two distinct modes of firing that are
associated with equally distinct modes of attentional strategy (Aston-Jones & Cohen, 2005).
Projections from the orbitofrontal cortex (OFA) and anterior cingulate (ACC) are thought to
drive LC-noradrenergic (LC-NA) system into one of two stable states of activity, a high tonic
(sustained) mode or a phasic (bursting) mode accompanied by moderate tonic activity (Aston-
Jones & Cohen, 2005). The OFA and ACC are known to play a role in calculating task utility
and there are prominent efferent connections to the LC from both (Aston-Jones, et al., 2002;
Rajkowski, et al., 2000; Zhu, 2004), with few sparse connections from other cortical areas.
The switching of attentional state via tonic LC activity theoretically results in a flexible
attentional system that allows cycling between exploitative and exploratory behaviours to find
and meet task demands in a changing environment, and is known as the Adaptive Gain Theory
(Aston-Jones, et al., 2005). In brief, during exploitative behaviour, when an agent is focused
on a singular task with high perceived utility, high-amplitude transient phasic bursting is
observed preceding behavioural responses to task-relevant stimuli, sustained firing is
maintained at a moderate level and task performance is relatively strong. As task utility wanes,
and tonic LC firing increases, phasic firing is reduced. This level of tonic LC activity facilitates
a decoupling of attention from the current object of focus, which allows a re-evaluation of the
current environments (both mental and physical), and the generation of hypotheses, goals, and
sub-goals of potentially greater value (Hayes and Petrov, 2016).
Attention is also known to exhibit regular oscillations between task-focused and mind
wandering states (Fox, et al., 2007; Franson, 2006; Songua-Barke, et al., 2007), which would
necessitate, according to the Adaptive Gain Theory, a corresponding oscillation in LC tonic
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activity that temporarily broadens the focus of awareness, by increasing neural gain, and
functional connectivity (Eldar, et al., 2013). The mechanism responsible for this oscillation is
believed to be a metabolic process (Songua-Barke & Castellanos, 2007), but remains unknown.
This “refresh cycle” of attention essentially opens an opportunistic window for attentional
reallocation, and is thought to be periodic, with a frequency somewhat greater than 0.1Hz
(Langner, et al., 2013; Robertson, et al., 1997).
2.3 Locus Coeruleus and CO2 Chemosensitivity
While the cognitive and attentional aspects of LC activity are interesting and impressive on
their own, the LC simultaneously carries out a second, phylogenetically more primitive role,
as an important part of the brainstem respiratory network. It is well-established that brainstem
respiratory nuclei initiate respiration when intracellular or extracellular CO2 levels increase.
LC neurons exhibit chemosensitivity to hypercapnic states (Gargaglioni, 2010), increasing
inspiratory drive when CO2 (H+)1 levels are increased (Biancardi, et al., 2008, 2012; Filosa, et
al., 2002; Gargaglioni, et al., 2010; Oyamada, et al., 1998; Pineda, et al., 1997). In vitro LC
neurons have been shown to fire in synchrony with the respiratory phrenic nerve (Oyamada, et
al., 1998), and increased LC firing frequency of up to 126% has been observed with controlled
decrease in pH levels in the LC (Filosa, et al., 2002). Chemical ablation of LC neurons results
in a significant attenuation of the hypercapnic respiratory effect (Bianciardi, et al., 2008;
Noronha-deSouza, et al., 2006). Connections from the LC to pre-inspiratory neurons have also
been identified (Dobbins & Feldman, 1994; Yackle, et al., 2017). Importantly, because arterial
CO2 levels are known to fluctuate with respiration (Band, et al., 1980; Band, et al., 1969;
Honda & Ueda, 1961; see Figure 1), this should induce a corresponding fluctuation of LC tonic
activity at the same frequency, as these chemosensitive neurons are bathed in arterial blood.
1!Positive!hydrogen!ions!result!from!CO2!combining!with!H2O!to!produce!carbonic!acid,!which!is!then!broken!
down!into!bicarbonate,!resulting!in!a!surplus!H+!ion.!
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Figure 1. Oscillation of CO2(pH) at respiratory frequency (from Band, et al., 1980; reprinted with permission)
2.4 Locus Coeruleus and Meditation
Given the wide-ranging influence the LC has on attention, respiration, and autonomic activity,
it is unsurprising that it has been hypothesized to play an important role in the effects of
meditation. Craigmyle (2013) theorizes that via activation by the ACC, which is a part of the
salience, orienting, and executive attention networks (Peterson & Posner 2012; Posner, et al.,
1990), the LC adapts the cortical and peripheral nervous systems of the organism to optimize
behaviour to a constantly changing environment, and that meditation improves the individual’s
ability to do this. Importantly, cortical NA has been directly observed to decrease during
meditation (Infante, et al., 2001; Walton et al., 1995), and increased gray matter density in the
pons, the location of the LC and other important respiratory nuclei, has been found in a cross-
section of long-term breath-focused meditators (Vestergaard-Poulsen, et al., 2009), as well as
in a randomized study (Holzel, et al., 2011).
2.5 Locus Coeruleus as Respiratory-Attentional Coupling Mechanism
The LC is obviously an interesting candidate as a potential coupling mechanism in a
hypothetical respiratory-attentional system, as it has important simultaneous roles in both
attention and respiration. Consisting of approximately 25,000 neurons per hemisphere, this
small nucleus could offer important insights into attentional dynamics and play an important
role in an empirical explanation of the ancient insights of yogis and meditators. It could also
potentially explain some of the cognitive and emotional benefits observed with various breath-
centered practices by helping explain how respiration and attention linked (Figure 2).
Figure 2. Diagram showing hypothesized coupled information transfer between respiratory and attentional
systems via the LC. Frontal attentional systems influence LC tonic/phasic activity. Oscillation of CO2 levels cause
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tonic fluctuations in LC at respiratory frequency. LC tonic activity in turn influences both attentional state and
respiration. Note the bi-directionality of the coupling. Such coupled systems tend to evolve toward stable,
nonlinear, or chaotic synchrony. Sine wave inside of circle indicates autonomously oscillating system.
3 *Initial*Research*Findings*
Recent research from our lab has revealed that LC activity, as measured by BOLD imaging
and pupil dilation, a known proxy of LC activity (Joshi, et al., 2016; Murphy, et al., 2011,
2014; Rajkowski, et al., 1993), exhibits phase coherence, or is synchronized, with respiration
(Figure 3).
Figure 3. A. Respiratory locked LC activity (respiratory peak versus trough) during oddball task session: B.
Respiratory locked LC activity (peak versus trough) during rest. C. Normalized pupil and respiratory global
averaged waveforms during task locked to the trough of respiration. D. Normalized pupil and respiratory global
averaged waveforms from resting state locked to the trough of respiration. C and D are illustrative of respiratory-
pupil synchronization and their phase offset.
In brief, respiration, pupil dilation and blood oxygen level dependency (BOLD) activation were
measured in 14 individuals during an 8-minute resting scan and a 20-minute auditory oddball
task (see Murphy, et al., 2014). Stimulus presentation during task was pseudo-random with an
interstimulus interval of 2.5 - 3.5 seconds. A pupil-covariant subset of LC neurons was isolated,
and corrected for instantaneous physiological noise (RETROICOR). Covariance with activity
in the fourth ventricle, an area proximal to the LC and known to exhibit significant respiratory
artefacts during MRI, was also regressed from the LC time series to control for physiological
respiratory artefacts.
To examine respiratory related LC activity, normalized LC BOLD signals were locked to the
instant of the peak and trough of respiration (± 8 s) and vector averaging was performed. Task
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LC activation showed clear synchronization with respiratory phase (Figure 3A), and an
apparent 180° phase difference in LC BOLD activity between respiratory peak and respiratory
trough was verified by cross-correlation. Paired T-tests for dependent samples (2-tailed)
showed highly significant signal separation at 4-8 seconds following the peak/trough of
respiration (Table 1, df = 7772). Corresponding resting state analysis of peak versus trough
locked activity showed a sustained anti-synchronized pattern of activity (Figure 3B), and this
was verified by cross correlation and with a circular test for non-uniformity (R-test, df = {9,9},
z = 7.84, p < .0001) performed on the angular phase differences (calculated with Hilbert
Transform) of the two signals. Amplitude comparisons however, while suggestive of a trend
(Table 2, Paired T-tests for dependent samples (single-tailed); df = 3358), were not highly
significant. Possible reasons for this are the shorter duration of the resting scans relative to task
and the absence of task-driven phasic LC amplitude contributions.
As a supplement to the paired T-tests, and to further examine the null hypothesis that LC
activation did not differ between respiratory peak and trough, bootstrap tests were conducted
by aggregating LC activation time locked to each peak and trough for each time point (-8 to +8
seconds) and resampling vectors of the same length with replacement (n = 50,000). A
comparison of the true mean peak-trough difference in LC activation with the bootstrapped
distributions for both task and resting state produced results roughly comparable with paired
T-tests for both task (Table 1) and resting state (Table 2).
Time to
respiratory
peak/trough
-8
-6
-4
-2
0
2
4
6
8
Paired
t-score
0.064
0.235
0.533
0.213
0.427
1.014
4.349
5.530
2.715
p-value
0.949
0.814
0.594
0.831
0.669
0.314
1.4e-5
3.4e-8
0.007
Bootstrap
(n = 50000)
p-value
0.52
0.592
0.71
0.42
0.34
0.16
0
0
3.6e-3
Table 1. Task respiratory-locked LC BOLD analysis results. Significant anti-synchronization observed from 4-8
seconds following respiration.
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Time to
respiratory
peak/trough
-8
-6
-4
-2
0
2
4
6
8
Paired
t-score
0.094
0.115
0.976
2.002
1.923
1.620
1.403
0.892
0.1757
p-value
0.537
0.454
0.165
0.023
0.027
0.053
0.080
0.186
0.430
Bootstrap
(n = 50000)
p-value
0.626
0.555
0.230
0.041
0.04
0.056
0.070
0.163
0.433
Table 2. Resting state respiratory-locked LC BOLD analysis results. Effects are marginal but a trend toward
significance is present.
Because increasing evidence suggests that pupil diameter provides a non-invasive proxy of LC
activity (Alnaes, et al., 2014; Joshi, et al., 2016; Liu, et al., 2017; Reimer, et al., 2016; Unsworth
& Robinson, 2016; Varazzani et al., 2015;), a corresponding analysis was conducted on
respiratory-locked normalized, blink-corrected pupil waveforms. This revealed a clear pattern
of synchronization during both task and rest (Figure 2C, D). Phase coherence, a measure of the
angular difference of the instantaneous phase between two signals, (Equation 1) was calculated
to be R = 0.977, with a mean phase difference Dq = -1.629 rad for task and R = 0.803, Dq = -
1.79 during rest. We interpret both the LC and pupil findings as suggestive of synchronization
between respiratory, LC, and pupil activity.
! " #
$%& '()*+',)*
-
./0
Equation 1. Method used to calculate phase coherence (R), where N is the sample size of the
angular distribution, i = 1# (imaginary operator), e is the natural logarithm, and 23456728
are instantaneous phase angles (in radians) from two different signals at time 9.. R returns an
average vector of length : ; ! ; #<:. Instantaneous phase values are calculated using the
Hilbert Transform.
To examine how respiratory activity might be related to attentional performance, participants
were binned into low and high reaction time variability groups (RTV). RTV is known to
correlate well with attentional performance (Jensen, 1991) and is higher in groups with
compromised attention, such as attention deficit hyperactivity disorder (ADHD; Kofler, et al.,
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2013; Tamm, et al., 2012), dementia (Hultsch, et al., 2000), Alzheimer’s Disease (Gorus, et al.,
Jackson, et al., 2012; 2008; Tse, et al., 2010), and traumatic brain injury (TBI; Whyte, et al.,
1995). Importantly, high RTV is present more so in TBI patients with focal frontal lesions as
opposed to non-frontal lesions (Stuss, et al., 2003). RTV also co-varies with LC tonic firing
rate (Usher, et al., 1999) and pre-stimulus pupil diameter (Murphy, et al., 2011; Van den Brink,
et al., 2016) in simple target detection tasks.
The angular phase of respiration at the instant of stimulus presentation was calculated for all
trials for all participants, and mean participant phase-locking angles were calculated. Clear
clustering near the trough of respiration (-2.53 rad) was observed in the low RTV group (figure
4A), while the HRTV group exhibited greater variability, with an advanced mean phase angle
(-1.37 rad) approaching the top of the respiratory cycle. The mean phase angle difference was
highly significant (Watson-Williams Test, F = 279.6, df = 6240, p < .0001) at the individual
trial level, and marginally significant at the participant level (Watson-Williams Test, F = 4.1,
df = 1,14, p = .06). Variability (concentration) of phase locking angle was significantly different
between the high and low RTV groups at both item (K-test, F = 1.095, df = 1,6271, p <. 0001)
and the participant level (K-test, F = 5.98, df = 1,14, p = .02).
Figure 4. Analysis of participants binned into high and low RTV. A. Stimulus-locked instantaneous respiratory
phase angle x RTV (Low v High groups). Participant mean stimulus locked respiratory phases were used for
clarity of presentation. RTV on radial axis. B. Stimulus-locked respiratory waveform averaged across all trials for
Low and High RTV groups.
3.1 Present Findings Discussion
Respiration has been observed to exhibit phase synchronization to stimulus presentation in
another study (Huijbers, et al., 2014), and recent research (Yackle, et al., 2017) has shown that
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removal of respiratory pattern generator (Cahedrin-9) pre-Botzinger neurons reduces arousal
in mice, possibly via termination of the connection from this respiratory pattern generator to
the LC. A commentary on this finding further suggested that respiration could affect arousal,
and perhaps cognition, via the LC (Sheikhbahaei & Smith, 2017), and a very recent study has
observed coupling between respiration and fluctuations of electrical activity in rodent brains.
Stimulus-locked respiratory phase has further been shown to affect perception of fear and the
encoding of memory (Zelano, et al., 2016), and respiration exhibits coupling with frontal theta
activity (Stankovski, et al., 2017), which is inversely related to DMN activity (Braboszcz and
Delorme, 2010; Scheeringa, et al., 2007). These studies, our findings reported above, and the
existing functional and anatomical knowledge of the LC and its connectivity, together suggest
that the respiratory and attentional systems may indeed be coupled. The lack of strong LC
BOLD amplitude findings during the resting state condition above require cautious
interpretation, however, and further higher powered studies would be required to decisively
determine if this synchronization can be observed by direct imaging of the LC. This caveat
notwithstanding, the strong LC-respiratory synchronization during task, and pupil-respiratory
synchronization in both conditions do suggest that these signals may indeed be coupled during
rest as well as during an attentionally demanding exercise. The present findings also indicate
an attentional advantage related to a more accurate and precise phase modulation of respiration.
4 Proposed*Mathematical*Model*of*Respiratory-LC-Attentional*Coupling*
We outline below a model of the relationship between respiration and attention, and their
hypothetical coupling via the LC. The LC, the attentional system, and respiratory activity all
exhibit regular oscillations, and can be considered autonomous noisy oscillators, exhibiting
weak, possibly transient, and/or nonlinear coupling (Fig. 1). Such a system admits of
mathematical modeling by a group of coupled differential equations. Models of this type have
previously been used to describe system dynamics of neural and physiological rhythms
(Mirollo & Strogatz, 1990; Pikovsky, et al., 2001). Our proposed system of equations
describing the coupling between respiration, attention, and LC oscillatory systems is described
in Equation 2.
There are obviously other factors, considered “noise terms” presently in the model, that will
affect the ultimate dynamics expressed by the individual oscillators, and the coupled system as
a whole, such as environmental exigencies (stimuli), autonomic influences, and other neural
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connections, so this “sandboxed” model can be viewed as an abstracted, idealized expression
of the isolated dynamics of the hypothesized respiratory-attentional system with the LC as its
nexus. It would be possible, of course, to expand the model to include other oscillatory (e.g.,
autonomic fluctuations) and pulsatile (e.g., environmental stimuli) influences.
=>" ?>@ 7 A>B
>=>C =DE @ F>
=DE " ?DE @ 7 ADEBDE =DEC =>C =G@ FDE
=G" ?G@ 77 ADEBG=GC =DE @ FG
Equation 2. Description of the coupled dynamical system of autonomous oscillators of
respiration, LC tonic activity, and task-focused/DMN oscillation (“attentional refresh
cycle”), where = is a first order derivative describing a variable’s phase evolution with
respect to time, w is the natural frequency of the oscillator, e is the coupling strength, F
is the coupling function (a 2π-modular function), f is the instantaneous phase, and x are
stochastic, linear, or nonlinear (potentially 2π-periodic) noise terms.
A similar mathematical description was employed recently by Stankovski, et al. (2016) to
examine the effect of anaesthesia on coupling dynamics of heart rate, respiration and frontal
EEG signal, in which they observed coupling between respiration and frontal theta rhythm. As
mentioned, frontal theta amplitude is a negatively correlated index of DMN activity, which is
active during task-unrelated thought, or mind wandering, so this finding is of direct relevance
to our hypothesis.
The present model also shares similarities with mathematical models for schizophrenia in
which shallower basins of attraction and decreased attractor stability lead to decreased memory
and increased distractibility (Loh, et al., 2007; Rolls, et al., 2008), and differences in phase
locking dynamics and coupling strength of the auditory cortex and thalamus contribute to
changes in auditory evoked potentials (Popovych, et al., 2009; Rosjat, et al., 2014).
4.1 Proposed*Modulators*of*Respiratory-LC-Attentional*Coupling**
As we theorize not only that these systems are coupled, but also that breath-focused practices
can alter the nature of this coupling, we speculate below on five possible mechanisms by which
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the coupling strengths (e) and noise terms (x) in the above equations might be modulated,
thereby modifying the dynamics of the coupling between respiration and attention.
4.1.1 Attentional*and*Executive*Systems*
As mentioned earlier, meditation is associated with functional, electrical, morphometric, and
connective changes in the brain, indicative of increased frontal control (see Tang, et al., 2015
for a review), along with decreased oscillation between mind-wandering and focused states.
The ACC, an integral part of the attentional system, is known to directly modulate LC activity
(Craigmyle, 2013; Sara, et al., 1995). While there have been no direct recording studies of LC
activity in meditators, increased attentional stability, a known result of meditative practices
(Lutz, et al., 2008; Lutz, et al., 2009) should logically be accompanied by stabilized tonic
modulation of the LC. Is it worth reiterating here that our preliminary data show that natural
variation in attentional performance (RTV) dissociates the indices of LC function, therefore it
follows that meditation should further flatten the tonic dynamics of the LC.
4.1.2 Insula*and*Interoceptive*Feedback*
The insula is known to incorporate visceral information about the physiological organism
(Craig, 2002), and its activity has been shown to correlate with the ability to consciously
monitor physiological processes (Critchley, et al., 2004), including respiration (Daubenmeier
2013; Farb, et al., 2013a,b). Research on the morphology and activity of the insula in meditators
shows overall increased volume and activity (Holzel, et al., 2007; Lazar, et al., 2005; Manna,
et al., 2010; but see Luders, et al., 2009), and increased gyrification (Luders, et al., 2012).
Decoupling of the insula and DMN also occurs in trained meditators relative to controls (Farb,
et al., 2007). This makes sense, as most types of meditation and pranayama involve paying
strict attention to respiration and other visceral sensations, and reducing mind wandering and
distractive thoughts.
This increased sensitivity to physical sensations, particularly of ongoing respiratory activity,
resulting from the augmented activation and morphology outlined above, could play an
important role in neural changes that allow for more precise predictive targeting of tonic LC
phase angle and amplitude. More specifically, it is possible that insular changes could alter
coupling between respiration and attention by improving signal transmission of respiratory
activity back to the cortex, allowing more effective synchronization of respiration to task.
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4.1.3 Autonomic*Regulation!
The autonomic nervous system maintains the balance of arousal, matching sympathetic and
parasympathetic influences with internal and external demands (Thayer and Lane, 2000). The
LC plays a complementary cognitive role to the autonomic arousal systems. This is necessary
for effective behaviour, and an inability to appropriately balance cortical and peripheral arousal
can be observed in ADHD (Anderson, et al., 2000; Nagai, et al., 2009; Satterfield & Dawson,
1971).
In general, LC activity increases sympathetic activity and decreases parasympathetic activity
via its projections to the spinal cord and various autonomic nuclei. Parasympathetic influence
is reduced via inhibitory projections to the vagal nuclei, while the LC’s excitatory effect on
sympathetic activity is more complex, involving combinations of excitatory and inhibitory
projections. For an in-depth treatment of this complex subject see Samuels and Szabadi (2008).
Meditation and pranayama are known to alter the sympathetic-parasympathetic balance of the
nervous system (Ditto, et al., 2006; Fundeburke, 1977; Stancak et al., 1991; Tang, et al., 2009;
Takahashi, et al., 2005; Telles, et al, 2013; Wallace, 1970), as indicated by changes in heart
rate, heart rate variability, respiration frequency and depth, blood pressure, and galvanic skin
response. Pranayama has been observed to alter this balance toward sympathetic or
parasympathetic activation depending on the method practiced (Rhaguraj, et al., 1998), and
focused states are associated with increased autonomic stability (Porges & Raskin, 1969;
Porges, 1992).
It is possible that some of the beneficial effects of meditation are mediated by altered autonomic
functioning, and via the LC, given its intimate relationship to arousal.
4.1.4 CO2*Sensitivity*
Because LC activity is known to vary with CO2, it is important to consider not only the level
of blood-CO2 but also the sensitivity of the organism to it. There is evidence that CO2 is
reduced during meditation (Wallace & Benson, 1972; Wolkove, et al., 1984) and studies also
suggest that CO2-sensitivity in the respiratory centers of the brainstem is decreased with
prolonged practice of pranayama (Joshi, 1992; Miyamura, 2002; Stanescu, et al., 1981). If true,
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this could cause a reduction in the amplitude and variability of the LC oscillation at respiratory
frequency. While it is not known if the LC specifically is affected in this way, a reduction in
LC tonic variability could increase attentional settling into a stable attentional attractor state,
thereby making unintentional attentional shifts due to chemosensitive (CO2) fluctuations less
likely.
The evidence for CO2 sensitivity from pranayama studies is supported by research on deep-
sea divers (Earing, et al., 2014; Florio, et al., 1979; Froeb, et al., 1960) and people living at
extremely high altitudes (Chiodi, 1957), all of whom show habituation to elevated levels of
CO2. Interestingly, people suffering from anxiety related disorders show an increased
sensitivity and an inability to habituate to high CO2 levels (Blechert, et al., 2010).
4.1.5 Possible*Interaction*of*Vagal*and*CO2*Influences!
It is well established that LC neurons are chemosensitive to fluctuating CO2 levels, and should
therefore result in an oscillation of LC tonic activity at respiratory frequency. Vagal activity
also modulates LC tonic discharge (Groves, et al., 2005a). This fact is exploited in vagal nerve
stimulation (VNS), which increases LC activity (Fornai, et al., 2011; Svensson & Thorien,
1979; Takigawa & Mogenson, 1977), and is used therapeutically to suppress seizures and treat
drug-resistant depression (Groves & Brown, 2005). This effect is both immediate (Groves, et
al., 2005a) and has been observed to last up to 3 days following treatment (Dorr & Debonnel,
2006). Lesioning and inactivation of the LC block the seizure-attenuating effects of VNS
(Krahl, et al., 1998). The exact mechanism of action for this is not known, but it thought to
possibly involve the nucleus of the solitary tract (NTS), as the area is richly innervated by vagal
fibres (Groves & Brown, 2005b).
It has been suggested by several authors that stretch receptors in the lungs inhibit vagal input
to the LC, possibly via the NTS. This would hypothetically result in a second sinusoidal
oscillation of tonic LC activity at respiratory frequency. Pulmonary vagal fibres terminate in
the NTS (Kubin, et al., 2006), and the cardiovascular area of the NTS has an established
efferent pathway to the peri-LC (van Bokstaele, et al., 1999), which in turn innervates the LC
proper (Aston-Jones, et al., 2004; Jin, et al., 2016). Physiological inhibition of the LC via the
vagus nerve has also been shown to occur following controlled baroceptor (blood pressure)
loading (Elam, et al., 1984, 1985; Murase, 1994). There are, however, presently no direct
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stimulation studies in the literature showing that respiratory vagal information is relayed to the
LC, so this remains a speculative, though intriguing, idea. We note this as a possible significant
contribution to respiratory-attentional coupling, but remain keenly aware that this is
hypothetical until definitive direct stimulation studies have been performed.
4.2 Illustration*of*Modulation*of*Model*Coupling*Dynamics*
According to our proposed model, any change in a system parameter will have global results
upon the dynamics of the entire system. To illustrate this concept more clearly, we examine
here dynamical changes in an extremely simple case where only respiratory frequency is
modulated. We chose this parameter because decreased respiratory frequency, as low as one
breath per minute for an hour (Miyamura, et al., 2002), is an established effect of pranayama
practice (Joshi, et al., 1992; Pinheiro, et al., 2007), and also because respiratory dynamics play
a fundamental role in our theory.
As can be seen in figure 4, the three-dimensional stable attractor states of the coupled systems
exhibit qualitative changes in response to modulation of respiratory frequency (=G). As
respiratory frequency is decreased, the resulting limit cycle becomes increasingly stable or
tightly coupled2, the plane of the attractor changes, and the resulting attentional oscillation
decreases in frequency, as do its magnitude and slope (Figure 5B). The frequency and slope
magnitude changes observed in the model suggest corresponding frequency and slope changes
in the underlying attentional “refresh cycle”. Changes of this sort could be of benefit for
stabilizing attention to task due to dilated periods of stable LC tonic activity, reduced frequency
and amplitude of attentional oscillations, and decreased unintended mind-wandering
interruptions.
2 It is possible to quantify the variability of the coupled systems by their Lyapunov exponents (Wolf, et al., 1985;
Rosenstein, et al., 1993) and approximate entropy (Pincus, 1991), which are measures of the divergence and
complexity of system, respectively, but we refrain from doing so here as we wish solely to describe the model in
general terms.
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Figure 5. Phase space plots derived from Equation 2. A. Left hand column shows limit cycle attractors at three
different respiratory frequencies (.35 Hz, .25 Hz, and .12 Hz). Note changes in variability and orientation of
attractor in phase space. Coupling coefficient (eps) required to achieve stability was identical at .35 Hz and .25
Hz, but noticeably higher for slowest respiratory frequency (.12 Hz), possibly suggestive of increased connectivity
requirements. B. Resulting slope of estimated frontal (attentional) oscillation. Frequency of attentional oscillation
decreases with decreasing respiratory frequency, suggestive of decelerated and attenuated attentional “refresh
cycle” component underlying attentional stability.
5 Discussion!
Given our knowledge of the LC’s involvement in attention, cognition, and arousal, its
susceptibility to top-down control, its concurrent chemosensitive respiratory function, and the
possible respiratory-induced vagal influence on LC firing, we hypothesize that the LC is a
critical node in facilitating coupling between respiration and attentional state. It is important to
stress that this coupling is bi-directional. Craigmyle (2013) has articulated that the LC, via
ACC activation, is likely an integral contributor to the beneficial effects of breath-centered
practices on arousal and attention. By introducing bottom-up respiratory influences on the LC
into this picture, we can then imagine the LC as a nexus of information transfer between these
two systems, and visualize the system as bi-directionally coupled (Figure 2).
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As previously mentioned, the human attentional system exhibits regular fluctuations between
a task-positive network and the DMN, associated with task-focused and mind wandering states,
respectively. Likewise, respiration exhibits regular oscillations that are normally highly
dependent on CO2 levels in the brainstem. With breath-focused practice, respiration decreases
in frequency, as does the frequency of mind wandering (Brewer, et al., 2011; Mrazek, et al.,
2013), with an increased ability to remain in a focused state. A decoupling of attention is
characterized by increased LC tonic activity and subsequent increase in neural gain and
functional connectivity, allowing a temporary competition for attentional resources. Given
the known effect magnitude of CO2/pH on tonic LC activity, it is possible that respiratory
induced LC fluctuations could provide a window of attentional flexibility, or a “refresh cycle”,
to a single attentional system that must address task demands, internal hypothesis generation,
and external exigencies by nimbly alternating between them as appropriately and as efficiently
as possible.
Sources of noise in LC activity, such as fluctuating arousal levels, CO2 sensitivity, and possibly
poor vagal tone, are attenuated by meditation practice. This attenuation could reduce the
amount of frontal input or effort necessary to maintain attentional state on task, and reduce the
probability of unintended attentional shifting due to fluctuations in neural gain and functional
connectivity. In fact, it has been noted that meditators of intermediate experience (~19,000h)
show increased activation in attentional areas compared to novices or non-meditators, but
extremely advanced meditators (~ 44,000h) show lower activation in those areas than all
groups (Brefczynski-Lewis, et al., 2007). Interviews confirmed this: after a prolonged period
of practice very little effort is required to maintain attention in a conscious focused state. The
stabilization of attentional states by reducing and/or adapting to the respiratory influences on
LC tonic variability in long-term practitioners could be one contributing factor.
We propose that the coupled respiratory-LC-attentional system can be described as a dynamical
system consisting of three coupled autonomous oscillators, which can be characterized by a
stable three-dimensional attractor in phase space. In this model, the attentional network
maintains stable states due to its own internal dynamics, and shifts between these states can
occur by either inhibitory processes (e.g., frontal input), energy dissipation (e.g., waning task
utility or fatigue) or novel injections of energy into the system (e.g., environmental urgency or
altered CO2/pH levels). Evolution of the internal dynamics of this system, resulting from
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22!
breath-focused meditation and pranayama, could influence the stability and/or depth of these
attractor basins, lowering the requirements of energy needed to maintain attentional states, and
decreasing the frequency of unintended attentional shifting (Figure 6).
Figure 6. Abstract representation of hypothetical attentional basins of attraction and tonic LC activity in
meditators versus controls. As system dynamics change (e.g., coupling function increases), the depth and stability
of attractor states hypothetically increase, requiring less energy input to sustain and resulting in a lower probability
of unintended attentional shifting.
It is important to point out that there is a fundamental difference between mindfulness practices
in which the breath is passively monitored with no effort to control it, and pranayama practices,
where the breath is actively regulated. Simple observation of the breath is an extremely
challenging vigilance task and practice will likely improve attentional function, and affect LC
tonic activity, via strengthening of the fronto-parietal attentional system, including the insula
and the ACC. Such monitoring tasks would therefore improve ability to target appropriate
adjustments of LC activity, and possibly to fine-tune respiratory phase angle predominantly in
a top-down way, however, it is unlikely that they would have as large an effect on bottom-up
mechanisms of physiological regulation of LC function, excluding possible stabilization of
autonomic states. Pranayama and other breath control practices, on the other hand, should
reduce respiratory frequency, modulate arousal, improve vagal tone and reduce Co2 sensitivity,
and so most of the resulting benefits should be physiologically derived. There will likely be
some overlap in these general categorizations, as merely observing the breath will undoubtedly
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alter it to an extent, and breath regulation will improve focused attention to a degree.
Classifying breath-centric practices in this way, however, could prove useful in targeting
practices in a therapeutic sense, and aid in understanding the specific effects of different breath-
centered practices.
The hypotheses that respiration and attention comprise a coupled system via the LC, and that
breath-focused practices will alter its dynamics, have the potential to increase our
understanding of the attentional system and how it interacts with physiological processes such
as respiration. We have briefly summarized the current understanding of the LC as it relates to
both attention and respiration, and described several mechanisms that could be involved in the
coupling dynamics of this system, and their possible evolution through these practices. This
could open a window into a deeper scientific understanding of the cognitive benefits of breath-
centered practices, and possibly offer a scientific explanation as to why the breath may offer
an ideal object of focus for meditation. Research on this hypothesis could further result in non-
pharmacological therapeutic possibilities for attentionally compromised populations (such as
ADHD, TBI and elderly populations), with different practices targeting specific problems with
either maintenance of physiological states of arousal or frontal control mechanisms.
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... A systematic search of the scientific literature databases MEDLINE, Web of Science, and PsycInfo was performed in November 2021 by author MS with the help of university librarians. The complete search strategy for each database can be found in the Supplementary Tables 2, 3, and 4. The search strategy was validated by correctly including six articles that had been identified as relevant before initiating the systematic search [18,43,47,50,65,75]. ...
... The main outcome for this systematic review was to assess the evidence for an influence of breathing phase on pupil dynamics. Six studies included in this review measured effects of breathing phase [5,18,28,43,44,47]. ...
... From the studies directly investigating whether breathing phase affects pupil dynamics, phase coherence between breathing and changes in pupil size was reported both at rest and while participants performed a visual oddball task. These findings were interpreted as suggestive of synchronization between breathing and pupil activity [43]. Similarly, in a separate study, pupil dilation amplitudes were shown to exhibit significant The x-axis lists a variety of study characteristics, and the y-axis lists all studies included in this systematic review. ...
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... It is possible that the neural contribution to the respiration-related brain network is mediated by arousal changes, which is associated with both neural excitability and respiration (Yackle et al., 2017;Shea, 1996;Fan et al., 2012). The cerebral blood flow can be regulated by the innervation from the basal forebrain and locus coeruleus, and both regions are directly related to arousal changes (Yackle et al., 2017;Lecrux and Hamel, 2016;Melnychuk et al., 2018). Previous studies in humans demonstrated correlations among the fMRI signal, low-frequency fluctuations of respiration, and EEG alpha power (Yuan et al., 2013). ...
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... Overall, they had lower Beta and Gamma power during both meditation and rest, compared to other practitioners, suggesting their greater flexibility in attentional control processes. Due to the neurophysiological coupling of the respiratory cycle and the LC-NE system (Melnychuk et al., 2018), vigorous breathing during Tummo is likely to lead to higher LC-NE activation and greater release of NE compared to imagery alone. During the years of practice, Tummo experts become highly proficient in reaching and controlling states of heightened arousal and related LC-NE activity. ...
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... The SYB practice may activate specific brain regions such as the dorsal pons, periaqueductal grey matter, cerebellum, hypothalamus, thalamus, lateral and anterior insular cortices in the human brain as revealed in the volitional controlled slow breathing study (Nagai et al., 2015). A recent study reported that yoga breathing and meditation increase synchronization between respiratory and attentional systems via LC activity thereby improving attentional, emotional, and physiological functioning (Melnychuk et al., 2018). Moreover, unilateral yoga breathing at a slow rate (5-8 bpm) demonstrated increased oxygenation and blood volume in the left prefrontal cortex (PFC) after the RNB session as compared to the BAW session (Singh et al., 2015). ...
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... As neurons from pre-Bötzinger complex neurons are linked with inspiratory phase of respiration [98], one would expect that there will be a direct relationship between the inspiratory phase of respiration and LC activity, if there is indeed a link between respiration and LC activity. Consistent with this idea, pupil diameter, an index of LC activity [102], rises in phase with the pre-inspiratory/ inspiratory phase of respiration, and falls during the expiratory phase of respiration [61] (Fig. 2), consistent with a direct role in activation of LC from pre-Bötzinger complex. Different pools of neurons in the pre-Bötzinger complex also project to many nuclei across the brain, including the dorsomedial hypothalamus and lateral preoptic area [118]. ...
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Book
First recognized in 1665 by Christiaan Huygens, synchronization phenomena are abundant in science, nature, engineering and social life. Systems as diverse as clocks, singing crickets, cardiac pacemakers, firing neurons and applauding audiences exhibit a tendency to operate in synchrony. These phenomena are universal and can be understood within a common framework based on modern nonlinear dynamics. The first half of this book describes synchronization without formulae, and is based on qualitative intuitive ideas. The main effects are illustrated with experimental examples and figures, and the historical development is outlined. The remainder of the book presents the main effects of synchronization in a rigorous and systematic manner, describing classical results on synchronization of periodic oscillators, and recent developments in chaotic systems, large ensembles, and oscillatory media. This comprehensive book will be of interest to a broad audience, from graduate students to specialist researchers in physics, applied mathematics, engineering and natural sciences.
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We revisit recent evidence showing that nasal respiration entrains oscillations at the same frequency as breathing in several regions of the rodent brain. Moreover, respiration modulates the amplitude of a specific gamma sub-band (70-120Hz), most prominently in frontal regions. Since rodents often breathe at delta and theta frequencies, we caution that previous studies on delta and theta power and their cross-regional synchrony, as well as on delta-gamma and theta-gamma coupling, may have detected the respiration-entrained rhythm and respiration-gamma coupling. We argue that the simultaneous tracking of respiration along with electrophysiological recordings is necessary to properly identify brain oscillations. We hypothesize that respiration-entrained oscillations aid long-range communication in the brain.
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It is widely accepted that cardiac and respiratory rhythms in humans are unsynchronised. However, a newly developed data analysis technique allows any interaction that does occur in even weakly coupled complex systems to be observed. Using this technique, we found long periods of hidden cardiorespiratory synchronization, lasting up to 20 minutes, during spontaneous breathing at rest.