Breathing is not just for oxygen; it’s now linked to brain function and behavior.
Northwestern Medicine scientists have discovered for the first time that the rhythm of breathing creates electrical activity in the human brain that enhances emotional judgments and memory recall.
These effects on behavior depend critically on whether you inhale or exhale and whether you breathe through the nose or mouth.
In the study, individuals were able to identify a fearful face more quickly if they encountered the face when breathing in compared to breathing out. Individuals also were more likely to remember an object if they encountered it on the inhaled breath than the exhaled one. The effect disappeared if breathing was through the mouth.
“One of the major findings in this study is that there is a dramatic difference in brain activity in the amygdala and hippocampus during inhalation compared with exhalation,” said lead author Christina Zelano, assistant professor of neurology at Northwestern University Feinberg School of Medicine.
“When you breathe in, we discovered you are stimulating neurons in the olfactory cortex, amygdala and hippocampus, all across the limbic system.”
The study was published Dec. 6 in the Journal of Neuroscience.
The senior author is Jay Gottfried, professor of neurology at Feinberg.
Northwestern scientists first discovered these differences in brain activity while studying seven patients with epilepsy who were scheduled for brain surgery. A week prior to surgery, a surgeon implanted electrodes into the patients’ brains in order to identify the origin of their seizures.
This allowed scientists to acquire electro-physiological data directly from their brains. The recorded electrical signals showed brain activity fluctuated with breathing. The activity occurs in brain areas where emotions, memory and smells are processed.
This discovery led scientists to ask whether cognitive functions typically associated with these brain areas — in particular fear processing and memory — could also be affected by breathing.
The amygdala is strongly linked to emotional processing, in particular fear-related emotions.
So scientists asked about 60 subjects to make rapid decisions on emotional expressions in the lab environment while recording their breathing. Presented with pictures of faces showing expressions of either fear or surprise, the subjects had to indicate, as quickly as they could, which emotion each face was expressing.
When faces were encountered during inhalation, subjects recognized them as fearful more quickly than when faces were encountered during exhalation. This was not true for faces expressing surprise.
These effects diminished when subjects performed the same task while breathing through their mouths. Thus the effect was specific to fearful stimuli during nasal breathing only.
In an experiment aimed at assessing memory function — tied to the hippocampus — the same subjects were shown pictures of objects on a computer screen and told to remember them. Later, they were asked to recall those objects. Researchers found that recall was better if the images were encountered during inhalation.
The findings imply that rapid breathing may confer an advantage when someone is in a dangerous situation, Zelano said.
“If you are in a panic state, your breathing rhythm becomes faster,” Zelano said. “As a result you’ll spend proportionally more time inhaling than when in a calm state. Thus, our body’s innate response to fear with faster breathing could have a positive impact on brain function and result in faster response times to dangerous stimuli in the environment.”
Another potential insight of the research is on the basic mechanisms of meditation or focused breathing. “When you inhale, you are in a sense synchronizing brain oscillations across the limbic network,” Zelano noted.
Other Northwestern authors include Heidi Jiang, Guangyu Zhou, Nikita Arora, Dr. Stephan Schuele and Dr. Joshua Rosenow.
Funding: The study was supported by grants R00DC012803, R21DC012014 and R01DC013243 from the National Institute on Deafness and Communication Disorders of the National Institutes of Health.
A wealth of historical literature has focused on automatic breathing driven by brain stem structures (Butler 2007; Tenney and Ou 1977) with more recent interest in cortical involvement (Feldman et al. 2013).
While motor cortex stimulation elicits diaphragmatic contraction at expected somatotopic loci (Başar et al. 2000; Bruce and Ackerson 1986; Foerster 1936; Gandevia and Rothwell 1987), the fact that the rate of breathing can be influenced by stimulation of the hippocampus, amygdala, and insula (Frysinger and Harper 1989, 1990; Harper et al. 1998; Kaada and Jasper 1952) suggests a more complex breathing circuitry above the brain stem level. Scalp EEG (Fumoto et al. 2004; Vialatte et al. 2009), transcranial magnetic stimulation (TMS; Locher et al. 2006), and neuroimaging studies (Evans et al. 1999; McKay et al. 2002), albeit with limited spatial and temporal resolution, have implicated premotor, motor, and supplementary motor cortices in the voluntary control of breathing.
More direct electrophysiological measures in rodents show local field potential (LFP) oscillations in hippocampus (Nguyen Chi et al. 2016; Tsanov et al. 2014), and the barrel (Ito et al. 2014) and prefrontal cortices (Biskamp et al. 2017) to closely track the inhalation-exhalation cycle. This link between LFP oscillations and the breathing cycle can be tracked with high temporal resolution by measuring their coherence.
The human intracranial EEG (iEEG) provides information regarding neuronal activity with high (~5 mm) spatial and millisecond temporal resolution and may be recorded in the special circumstance of invasive monitoring for epilepsy surgery (Miller et al. 2012; Zhang et al. 2015).
In these clinical protocols, patients with partial epilepsy, where much of the brain is relatively normal, undergo invasive electrode sampling to define pathological regions. In these circumstances, it is possible to record from relatively normal brain regions, because much of the sampled regions lie within relatively normal cortex that lie outside of the seizure onset zone (SOZ) either for the purpose of functional mapping or to rule out their participation in the network of areas participating in seizures (Borchers et al. 2011; Hamberger et al. 2014).
This provides a unique window into human brain physiology, one which we can use to study cortical control of breathing. With the use of these methods, recent reports have demonstrated a correlation between the breathing cycle and iEEG oscillatory phenomena in limbic and neocortical areas during natural breathing (Heck DH, McAfee SS, Liu Y, Babajani-Feremi A, Rezaie R, Freeman WJ, Wheless JW, Papanicolaou AC, Ruszinko M, Kozma R, unpublished observations; Heck et al. 2017; Zelano et al. 2016).
However, a detailed survey of effects across multiple intracranial sites has not been conducted. To provide further description of this circuitry, we tested for iEEG synchronization to breathing throughout a wide base of sampling across limbic and cortical areas.
Whereas memory retrieval and fear discrimination appear to be sensitive to respiratory phase, and nasal breathing produces a greater deal of iEEG coherence than mouth breathing (Zelano et al. 2016), task-dependent dynamics of this coherence with respect to attention and volitional control have not been explored (see Krupnik et al. 2015 for a related psychophysical study).
Such tasks are commonly used in therapeutic practices as an effort to gain interoceptive attention and control. To gain better insight into neurophysiological mechanisms engaged in these processes, we examined the areal specificity of the change in iEEG-breath coherence while subjects performed tasks aimed at controlling the pace of the respiratory rate or increasing awareness to the respiratory cycle.
Given extensive brain sampling that included frontal cortex, limbic structures, and the insula, structures known for their involvement in behavioral planning and interoceptive monitoring of body states, we expected to observe increased coherence during both tasks in these structures (Chan et al. 2015; Craig 2003a; Farb et al. 2013).
Finally, whereas oscillatory activity in the iEEG likely reflects mainly neuronal contributions, it is possible that chemical/mechanical artifacts that also follow the breathing cycle may contribute to the recorded signals (Brenner and Schaul 1990; Cardoso et al. 1983; Urigüen and Garcia-Zapirain 2015).
To distinguish neuronal contributions from potential artifacts, we demonstrated that these effects are selective to gray matter vs. white matter and cerebrospinal fluid (CSF) spaces, all of which may be sampled by using depth electrodes in stereoelectroencephalography (sEEG) procedures.
To further rule out potential artifactual contamination and distinguish locally generated from propagated activity, we examine the iEEG-breath cycle coherence of the iEEG gamma-band envelope, a proxy of local neuronal firing (Miller et al. 2007a, 2010b).
Whereas breathing has been traditionally thought of as an automatic process driven by the brain stem (Butler 2007; Foerster 1936; Tenney and Ou 1977), cortical and limbic involvement in breathing is increasingly being recognized (Aleksandrov et al. 2000; Biskamp et al. 2017; Ito et al. 2014; Tsanov et al. 2014).
In the present study, we demonstrate respiration-locked oscillations in the human brain in cortical and limbic areas more widespread than shown previously and provide further evidence for the neuronal basis of these measurements by demonstrating gray matter (GM) specificity and cross-frequency coupling (CFC; coupling of the respiration phase to the gamma amplitude).
The relevance of higher brain circuits to respiration-locked oscillations is highlighted by their sensitivity to cognitive factors such as volitional and attentive breathing. We further demonstrate that interoceptive attention to breath increases respiration-locked oscillations particularly in the anterior cingulate cortex, an effect that is distinct from general arousal.
Synchronization of neuronal activity to the breathing cycle.
Because the breathing cycle is oscillatory, observed iEEG-breath coherence may be due to cyclical nonneural artifacts, such as intracranial pressure, cerebral perfusion, Po2, and Pco2 (Brenner and Schaul 1990; Cardoso et al. 1983; Urigüen and Garcia-Zapirain 2015). However, these properties should be distributed throughout the intracranial space, with the more compliant and dynamically flowing CSF more prone to mechanical and chemical artifacts. Because depth electrodes often traverse white matter (WM) and CSF spaces as well as GM, it is possible to examine for GM specificity of the observed coherence. We demonstrated such coherence predominantly in GM (41.3%), rarely in WM (12.6%), and never in the CSF.
We also differentiate neuronal from nonneuronal signals by examining the coupling of high-frequency gamma-band activity to the phase of the observed low-frequency oscillation related to breathing. Similar to the case for coherence, GM specificity of effects supports a neuronal basis. We demonstrated phase-amplitude coupling of the breathing oscillation to the gamma band in a subset (12%) of sites (Fig. 5), with nearly all sites (136/138) also showing strong coherence. Respiratory modulation of intrinsic gamma network oscillations could be an organizing principle of cortical excitability, in line with recent hypotheses (Heck et al. 2017).
Sites where coherence occurs without CFC (2/138) likely reflect propagated lower frequency modulatory inputs below action potential threshold (Kajikawa and Schroeder 2011; König et al. 1996).
Respiratory rhythm represents a fundamental and ubiquitous neuronal oscillation.
Our results support and corroborate rodents studies demonstrating respiration-locked LFP and spike activity in the hippocampus (Nguyen Chi et al. 2016; Tsanov et al. 2014), barrel (Ito et al. 2014), and prefrontal cortices (Biskamp et al. 2017) and extend observations recorded from the rodent (Tort AB, Ponsel S, Jessberger J, Yanovsky Y, Brankačk J, Draguhn A, unpublished observations) and human brains (Heck DH, McAfee SS, Liu Y, Babajani-Feremi A, Rezaie R, Freeman WJ, Wheless JW, Papanicolaou AC, Ruszinko M, Kozma R, unpublished observations; Heck et al. 2017). In rodents, respiration is tightly coupled to olfaction (Welker 1964), as the respiration cycle strongly influences the information flow into the olfactory bulb (OB) and piriform cortex (Fontanini et al. 2003; Jiang et al. 2017; Kepecs et al. 2007).
In the present study, we demonstrated respiration-locked oscillations in the OB, piriform cortex, and medial and lateral OFC (Fig. 5), extending such observations from the piriform cortex to humans (Jiang et al. 2017; Zelano et al. 2016). Furthermore, with widespread sampling, we demonstrated respiration-locked oscillations well beyond those structures and in 41% of the GM sites sampled (Fig. 3).
Most consistent effects were recorded in the hippocampus, amygdala, insula, frontal, parietal, and primary olfactory cortices (Figs. 4 and and5E).5E). Simultaneous recordings from the OB and the hippocampus in the mouse (Nguyen Chi et al. 2016) reveal that the OB drives the hippocampal respiration-rhythm and that theta oscillations in the OB are driven by the hippocampus.
In humans, breathing not only entrains higher frequency rhythms in the hippocampus, but the phase of respiration-related oscillations affects memory retrieval (Zelano et al. 2016). Our findings of breathing-related coherence in both OB and hippocampus corroborate previous findings, further establishing the connection between olfaction and memory (Jiang et al. 2017).
Whereas in rodents, breathing is close to the theta rhythm, respiration-related oscillations have a frequency peak at ~3 Hz (during natural breathing) and appear to be clearly distinct from co-occurring theta (4–10 Hz) oscillations (Nguyen Chi et al. 2016). Given that the human correlate to rodent hippocampal theta is slower, at 1–4 Hz (Jacobs 2014), we also observed clearly distinct respiration and hippocampal peaks (Fig. 2B): a respiration-related oscillation with a frequency peak at 0.28 Hz (equal to the respiration rate in that subject) and a co-occurring theta peak at 1.4 Hz (similar to the hippocampal peaks recently reported in humans; Jacobs 2014). Within the hippocampus (n = 30), theta peaks ranged from 1 to 3 Hz, whereas frequency of peaks related to natural breathing ranged from 0.24 to 0.37 Hz.
Oscillatory activity in the EEG appears to be hierarchically organized such that amplitude of the oscillations at each characteristic frequency band (gamma, theta, etc.) is modulated by the oscillatory phase of a lower frequency oscillation (Lakatos et al. 2005). This implies that the low-frequency oscillatory activity related to breathing can drive a broadband range of oscillatory activity in the iEEG.
The influence of breathing on neuronal activity is not only spatially distributed across the brain but also is in a position to drive a broad frequency range of oscillatory phenomena, as recently suggested by a graph theoretic modeling study (Heck DH, McAfee SS, Liu Y, Babajani-Feremi A, Rezaie R, Freeman WJ, Wheless JW, Papanicolaou AC, Ruszinko M, Kozma R, unpublished observations).
Voluntary pacing of breath.
It is known that the automatic vegetative control of breathing can be superseded by higher cognitive factors (Loucks et al. 2007; McKay et al. 2002). Such voluntary pacing of breathing is often employed in cognitive behavioral therapy and meditative/yoga techniques.
When our subjects were asked to breathe faster, respiration-locked oscillations were greater in power and continued to follow the breathing frequency at the higher rate, particularly in premotor, caudal-medial frontal, orbitofrontal, and motor cortex, insula, superior temporal gyrus, and amygdala (Fig. 8).
As subjects returned to natural breathing, respiration-locked oscillations gradually decreased in power and continued to track at a lower frequency. Previous work (Ito et al. 2014) showed a similar “tracking” effect, with oscillations in the mouse somatosensory cortex when breathing rate was manipulated by producing hypoxic conditions. Although we found respiration-locked oscillations in the somatosensory (parietal) cortex (Fig. 4), we did not observe different coherence between active and passive breathing.
The somatosensory cortex involvement is supported by scalp-EEG studies where occlusion of the airways during natural breathing yields an evoked potential over this cortical area (Chan et al. 2015; Davenport et al. 1986). Whereas diaphragm contraction can be seen following stimulation at the vertex of the motor cortex in humans (Gandevia and Rothwell 1987; Foerster 1936), we observed effects throughout the precentral gyrus, including areas corresponding to the face and extremities.
Respiration-locked oscillations were also found in the amygdala, in line with previous human research (Frysinger and Harper 1989a, 1990b). Respiratory patterns are affected by fear/anxiety in humans, indicating involvement of higher cortical and limbic centers (Boiten 1998; Masaoka and Homma 1997; Masaoka et al. 2012).
We found stronger respiration-locked oscillations in the amygdala during voluntarily faster breathing compared with natural breathing (Fig. 8). Fast breathing often accompanies high-anxiety states, and it is possible that voluntarily increasing the breathing rate can trigger mechanism similar to those triggered by anxiety (Homma and Masaoka 2008; Masaoka and Homma 1997).
Behavioral performance was recently shown to depend on the breathing cycle by Zelano et al. (2016). They showed fear recognition was faster when the stimuli appeared around the inhalation peak, suggesting that oscillatory synchrony in the amygdala depends on the respiratory phase. Our study elaborates on this finding by demonstrating respiration-locked oscillations that follow an experimentally manipulated rate of breathing.
Both amygdala and insula receive projections from the nucleus tractus solitarii (NTS) and medullary cardiorespiratory centers (Gaytán and Pásaro 1998), providing an anatomic pathway potentially subserving this link. We found strong respiration-locked oscillations in the insula, particularly during volitional breathing.
The insula is involved in autonomic regulation and interoception (Craig 2003a, 2009b), and neuroimaging studies show posterior insular activation during volitional breathing in humans (Evans et al. 1999; McKay et al. 2002) and rodents (Aleksandrov et al. 2000). Our results also show volitional breathing to result in stronger respiration-locked oscillations in the premotor and olfactory cortices, in line with neuroimaging studies (Evans et al. 1999; Loucks et al. 2007) and in the caudal-medial frontal cortex, an area important for executive control (Evans et al. 1999; Šmejkal et al. 2000) and breathing during exercise (Forster et al. 2012).
Volitional breathing also resulted in stronger respiration-locked oscillations in the temporal cortex. Although this may represent breathing-related oscillations driven in sensory cortices, we cannot distinguish this mechanism from the sensory effect of the louder sound emitted during faster breathing, which may entrain the auditory cortex (Schroeder et al. 2014).
Attention to breathing.
Distinct from volitional breathing, one can concentrate on the breath itself, a technique used in cognitive behavior therapy and meditation practices (Farb et al. 2013; Levinson et al. 2014). Involvement of frontal lobe areas during breath tracking is suggested by scalp-EEG studies (Giannakodimos et al. 1995; Milz et al. 2014).
At a more mechanistic level, we demonstrate increased iEEG-breath coherence in the anterior cingulate cortex (Acc) and premotor cortex (Figs. 9 and and10)10) when subjects appeared to be more aware of their breath, consistent with the role of these structures in processing moment-to-moment awareness (Farb et al. 2013; Tang et al. 2010).
Notably, the Acc did not show a high degree of respiration-locked oscillations at rest (see Figs. 4 and and5E).5E). However, this iEEG-breath coherence changed markedly during breath-awareness (Fig. 9, B and C). We also found that brain areas involved in interoception, such as the insula (Craig 2003a, 2009b), had stronger respiration-locked oscillations when subjects correctly tracked their breath.
The finding of increased hippocampal coherence during breath tracking is consistent with that structure’s involvement in the task, which required remembering a count (Sveljo et al. 2010).
The observed stronger iEEG-breath coherence when subjects correctly tracked their breath, compared with when they did not, cannot be explained simply by changes in arousal state. First, alpha power, a proxy of arousal (Cantero et al. 1999; Klimesch et al. 1998), did not differ between correct and incorrect breath-count blocks (Fig. 9A, inset).
Second, respiration rate changes did not impact the effect of attention on iEEG-breath coherence (Fig. 10F): both subjects showed increases in coherence despite having different respiratory strategies (subject 7 showed reduced rate with attention to breath, whereas that for subject 8 increased).
Third, when subjects participated in a task requiring sustained attention to an external stimulus (with arousal levels approximately similar to those during breath tracking; Fig. 10A), iEEG-breath coherence did not increase from that observed during natural breathing (Fig. 10, B–E). If anything, iEEG-breath coherence in the Acc was weakly reduced as arousal increased (subject 7: r = 0.27, P = 0.0001; subject 8, r = 0.2, P = 0.017).
In other areas, no significant effects of arousal during the exteroceptive attention task were noted. Indeed, iEEG-breath coherence in the Acc during tasks requiring exteroceptive attention may impair performance: because strong iEEG-breath coherence results in increased slow oscillatory power (particularly at the respiratory frequency, Fig. 2B), it is tempting to speculate that it may be incompatible with concurrent increases in faster oscillatory rhythms that often support efficient performance in exteroceptive attention tasks (Başar et al. 2001).
Conversely, when attention is focused interoceptively, on the breath, as in some meditative practices (e.g., Breath Theravada), high iEEG-breath coherence could be an important neural phenomenon supporting effective breathing concentration.
Respiration rates differed across participants during correct breath counting (Fig. 10F; subject 7 showed slower respiration rate, whereas subject 8 showed a faster rate). Importantly, both subjects show increased iEEG-breath coherence.
A similar effect may occur in the volition rate experiment, suggesting that the increased coherence observed during faster breathing may be explained by the volitional aspects of respiration and not the respiration rate. Future studies using volitional pacing of breathing might explore a pacing that is slower than the spontaneous breathing rate.
This would allow us to disambiguate whether the increased coherence observed in our volitional pacing experiment was due to the rate of breathing or the volitional aspects of it. In addition, important statistical efforts have been applied to CFC and coherence computations (e.g., projected CFC/coherence in the complex plane; Miller et al. 2012), rather than the long-time resampling used in the present study), and future studies should take this approach into account.
In summary, our data support the link between respiration and brain activity, proposing respiration rhythms as an organizing principle of cortical oscillations in the human brain, as recently proposed in mice (Tort AB, Ponsel S, Jessberger J, Yanovsky Y, Brankačk J, Draguhn
A, unpublished observations). Moreover, respiration rate in humans can be controlled volitionally or attended to while brain activity is measured. In implementing these conditions with concurrent monitoring of breath and intracranial EEG, we demonstrate a network of areas involved in volitional (caudal-medial frontal, premotor, orbitofrontal, and motor cortex, insula, superior temporal gyrus, and amygdala) and attentive breathing (anterior cingulum, premotor, insula, hippocampus), providing insight into potential brain mechanisms involved in therapeutic breathing exercises (Farb et al. 2013; Kemmer et al. 2015; Levinson et al. 2014).