Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function

Abstract

The need to breathe links the mammalian olfactory system inextricably to the respiratory rhythms that draw air through the nose. In rodents and other small animals, slow oscillations of local field potential activity are driven at the rate of breathing (∼2-12 Hz) in olfactory bulb and cortex, and faster oscillatory bursts are coupled to specific phases of the respiratory cycle. These dynamic rhythms are thought to regulate cortical excitability and coordinate network interactions, helping to shape olfactory coding, memory, and behavior. However, while respiratory oscillations are a ubiquitous hallmark of olfactory system function in animals, direct evidence for such patterns is lacking in humans. In this study, we acquired intracranial EEG data from rare patients (Ps) with medically refractory epilepsy, enabling us to test the hypothesis that cortical oscillatory activity would be entrained to the human respiratory cycle, albeit at the much slower rhythm of ∼0.16-0.33 Hz. Our results reveal that natural breathing synchronizes electrical activity in human piriform (olfactory) cortex, as well as in limbic-related brain areas, including amygdala and hippocampus. Notably, oscillatory power peaked during inspiration and dissipated when breathing was diverted from nose to mouth. Parallel behavioral experiments showed that breathing phase enhances fear discrimination and memory retrieval. Our findings provide a unique framework for understanding the pivotal role of nasal breathing in coordinating neuronal oscillations to support stimulus processing and behavior.

Significance statement: Animal studies have long shown that olfactory oscillatory activity emerges in line with the natural rhythm of breathing, even in the absence of an odor stimulus. Whether the breathing cycle induces cortical oscillations in the human brain is poorly understood. In this study, we collected intracranial EEG data from rare patients with medically intractable epilepsy, and found evidence for respiratory entrainment of local field potential activity in human piriform cortex, amygdala, and hippocampus. These effects diminished when breathing was diverted to the mouth, highlighting the importance of nasal airflow for generating respiratory oscillations. Finally, behavioral data in healthy subjects suggest that breathing phase systematically influences cognitive tasks related to amygdala and hippocampal functions.

Keywords: amygdala; hippocampus; local field potential; piriform cortex; respiration; respiratory oscillations.

Copyright © 2016 the authors 0270-6474/16/3612448-20$15.00/0.

Figures

Figure 1.

Respiratory analysis method and breathing frequency data across patients. A, A representative trace of the raw respiratory signal from one patient is shown in blue. To define respiratory events for the LFP analyses, the instantaneous phase of the respiratory time series (obtained from the angle of the Hilbert transform) was computed (red trace). The peak of inspiratory flow occurs at the abrupt transition in the instantaneous phase from π to −π, and can be detected as a deflection in the derivative of the phase of the respiratory signal (green tick marks). The small black circles on the respiratory phase waveform (in red) denote the points of peak flow, which align well to the inspiratory peaks of the raw respiratory signal (in blue). B, Fast Fourier transform analysis was used to characterize the dominant breathing frequency in each patient. Each panel represents one patient (P1–P7).

Figure 2.

Slow oscillations in human PC are in phase with respiration. A, Representative traces of the raw LFP time series from five patients with PC coverage show that slow fluctuations in PC (black) are in phase with inhalation (blue) across a series of breaths. Inspiration is in the upward direction in this and all panels. Patients are labeled, for example, as P1, P2, P3, etc., in chronological order of study enrollment. Note that the Nihon Kohden acquisition system allows recording oscillations as slow as 0.08 Hz, well below the respiratory range. B, Patient-specific time-course plots depict the mean respiratory waveform (red) and the mean LFP signal in PC (black), amygdala (dotted line), and hippocampus (dashed line), filtered between 0 and 0.6 Hz, temporally aligned to the peak of inspiratory flow (at 2 s), and averaged over all trials. Across all patients, the LFP signal most consistently conforms to the respiratory rhythm in PC (each row represents data from one patient). C, The correlation (R value) between the mean respiratory signal and the mean LFP signal is shown as a red dot for each patient in PC, amygdala, and hippocampus. These values are overlaid on histograms of R value null distributions (z-normalized) computed from 6 s LFP trials randomly aligned to the onset times of peak inspiratory flow. Correlations were statistically significant in all patients in PC. *p < 0.05.

Figure 3.

Analysis pipeline for correlating respiratory and LFP time series. (1) First, the respiratory data were synchronized with the LFP data, after being low-pass filtered at

Figure 4.

Respiration entrains higher-frequency oscillations in PC, amygdala, and hippocampus. A–C, Time–frequency spectrograms for each patient were computed across trials and aligned to peak inspiratory flow at time = 2 s (vertical black lines). Each patient's averaged respiratory signal (black waveform) is overlaid on the corresponding spectrogram. The pseudocolor scale represents the mean spectral power (z-normalized) averaged over all breaths, on a patient-by-patient basis, relative to a preinhalation baseline period between 0.2 and 0.8 s (horizontal black bars). In PC (A) and amygdala (B), delta power significantly emerges during the inspiratory phase of breathing in each patient. Significant increases in delta power were also observed in hippocampus (C), although effects did not reach corrected significance in P3, P4, and P5. Time–frequency clusters, where spectral power survived statistical correction (FDR) for multiple comparisons (at z > 3.2), are outlined in black. Note, data from PC were not recorded in P5 and P6.

Figure 5.

Dependence of respiratory oscillations on nasal airflow. A–C, Respiratory oscillations diminish when breathing is diverted from nose to mouth in PC (A), amygdala (B), and hippocampus (C). Time–frequency spectral plots are shown from one patient with PC coverage (P7), and three patients with amygdala and hippocampal coverage (P7, P5, and P6) who performed both nasal breathing (left panels) and oral breathing (middle panels) for 15 min each. (Spectrograms for the nasal breathing data are identical to those shown in Fig. 4.) The mean respiratory signals for nasal and oral respiration are plotted in black. The difference between nasal and oral spectrograms (nasal vs oral) is shown in the far right panels. Patients exhibited a consistent and significant decrement in respiratory oscillatory power from nasal to oral breathing for delta, theta, and beta frequency bands in PC, and for the delta frequency band in amygdala and hippocampus. Clusters outlined in black on the spectrograms survived FDR correction for statistical significance (z > 3.2).

Figure 6.

Comparison of slow respiratory oscillations in PC during nasal and oral breathing in P7. Top row, The correlation between the respiratory waveform and the raw (unfiltered) LFP time series in PC (averaged across 6 s breathing trials, aligned to peak inspiratory flow at time = 2 s) was robust during nasal breathing, but not during oral breathing. Middle and bottom rows, By comparison, in this same patient in amygdala (middle row) and hippocampus (bottom row), respiratory entrainment was not significant during either nasal or oral breathing.

Figure 7.

Consistent modulation of beta amplitude by theta phase in PC. A, Comodulograms were computed individually in all five patients with piriform coverage, revealing cross-frequency coupling between theta phase and beta amplitude in each patient (white ovals). Each row represents one patient. Three of five patients also showed theta–gamma coupling (white arrows). Comodulograms were generated by computing the z-normalized MI for each phase-amplitude pair extending from 1 to 10 Hz in the phase dimension and from 13 to 200 Hz in the amplitude dimension. B, In patient P7, the magnitude of cross-frequency coupling in PC was significantly diminished when breathing was directed through the mouth, as shown in the difference map between nasal and oral comodulograms (right). Note, the nasal comodulogram for P7 in this panel is identical to that shown for P7 in A.

Figure 8.

Respiratory phase modulates fear-related response times. A, Emotion discrimination task. Subjects viewed faces expressing either fear or surprise, and indicated which emotion was perceived. Interstimulus interval, 2–5 s. Colored dots indicate where in the breathing cycle stimuli were encountered. B, Fearful faces were detected more quickly during nasal inspiration vs expiration, but not during oral breathing. C, Emotion RT data, binned across four phases of breathing, revealed a significant two-way interaction between breathing time bin (4 levels) and breathing route (nasal/oral) for fearful faces, with maximal RT differences during nasal fear trials occurring between the onset of inspiration and the onset of expiration. *p < 0.05 in all panels. Error bars represent the SEM.

Figure 9.

Respiratory phase modulates episodic memory performance. A, In a recognition memory task, subjects viewed a series of different visual objects that occurred at different times within the breathing cycle. Interstimulus interval, 3–6 s. After a 20 min break, subjects were presented with the old pictures from the encoding session plus an equal number of new pictures. B, Memory performance was more accurate during inspiration than during expiration, with effects more pronounced for nasal than oral breathing, both for encoding and retrieval. C, An analysis of all “hit” trials revealed that recognition memory was significantly enhanced for pictures that had appeared during the inspiratory (vs expiratory) phase of retrieval, but it made no difference whether those same pictures had been encountered in the same phase during encoding. *p < 0.05 in all panels.

Figure 10.

Strength of respiratory modulation in the amygdala predicts emotional response times. A, One patient (P8) with intracranial coverage of the amygdala participated in the emotion discrimination task (as in Fig. 8). Analysis of RTs revealed a significant interaction between emotion (fear vs surprise) and respiratory phase (inhale vs exhale). B, A time–frequency spectrogram computed across all breaths highlights respiratory entrainment of oscillatory activity in the amygdala, as observed in the patients who took part in the passive breathing task. Significant spectral clusters (FDR-corrected) are outlined in black, and include delta-, theta-, and beta-frequency bands. Black line, Respiratory waveform; black horizontal bar, preinspiratory baseline period used for z-normalization. C, In a trial-by-trial analysis of inspiratory delta power, the 24 trials in which fearful faces appeared during the inspiratory phase of breathing were sorted by increasing RT, and suggest that fear–inhalation trials with higher oscillatory entrainment in amygdala (orange-to-red colors) were generally associated with faster behavioral responses, compared with trials associated with slower behavioral responses (green-to-blue colors). The respiratory signal for each trial is overlaid (vertically from top to bottom) in black. D, Trial-by-trial scatterplots of amygdala delta power vs emotion judgment RTs demonstrated a significant negative correlation for fear–inhalation trials only, whereby trials with greater inspiratory power were associated with lower (faster) RTs. Trialwise measures of amygdala delta power were averaged across the entire time-window of inhalation or exhalation separately for fear and surprise conditions.