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Featured ArticleResearch Articles, Systems/Circuits

Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function

Christina Zelano, Heidi Jiang, Guangyu Zhou, Nikita Arora, Stephan Schuele, Joshua Rosenow and Jay A. Gottfried
Journal of Neuroscience 7 December 2016, 36 (49) 12448-12467; https://doi.org/10.1523/JNEUROSCI.2586-16.2016
Christina Zelano
1Departments of Neurology and
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Heidi Jiang
1Departments of Neurology and
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Guangyu Zhou
1Departments of Neurology and
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Nikita Arora
1Departments of Neurology and
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Stephan Schuele
1Departments of Neurology and
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Joshua Rosenow
2Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, and
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Jay A. Gottfried
1Departments of Neurology and
3Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, Illinois 60208
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  • Figure 1.
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    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.
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    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.

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    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 <0.6 Hz. (2) Data were then epoched into 6 s trials, aligned to inspiratory peak flow at time = 2 s, and extended from 2 to 4 s after inspiratory peaks. (Note, individual 6 s trials generally spanned more than one breath, and often included LFP data from the trial-aligned breath as well as partial data corresponding to the next breath). (3) Next, the inspiratory peak-aligned LFP trials were averaged to generate a mean 6 s waveform, and (4) the temporal correlation was computed between LFP and respiratory signals. (5) Finally, a null distribution was generated for statistical testing by creating LFP trials aligned randomly according to the respiratory data.

  • Figure 4.
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    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.

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    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).

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    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.

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    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.

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    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.

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    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.

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    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.

Tables

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    Table 1.

    Patient demographics

    PatientDescription
    P1
        HandednessLeft
        Age at surgery33 years
        Seizure risk factorsReye's syndrome with convulsions as a child
        Family history of seizures(+) Maternal grandfather
        Age of seizure onset25 years
        Seizure semiologyAutomotor seizures; generalized tonic-clonic
        Working diagnosisNonlesional left temporal lobe epilepsy
        EEGLeft temporal interictal discharges
        MRI brainNormal
        Current medicationsLamotrigine, oxcarbazine, Vimpat, clorazepate
    P2
        HandednessLeft
        Age at surgery47 years
        Seizure risk factorsHead injury at age 3 with loss of consciousness, viral encephalopathy age 45 years
        Family history of seizuresNone
        Age of seizure onset45 years
        Seizure semiologyPsychic aura, gustatory aura, complex partial seizure
        Working diagnosisNonlesional left temporal lobe epilepsy
        EEGBitemporal ictal discharges; interictal left temporal sharp waves
        MRI brainNormal
        Current medicationsValproic acid, lamotrigine, phenytoin
    P3
        HandednessRight
        Age at surgery29 years
        Seizure risk factorsNone
        Family history of seizuresNone
        Age of seizure onset22 years
        Seizure semiologyGeneralized tonic-clonic seizures, dialeptic seizures, tonic seizures
        Working diagnosisNonlesional left temporal lobe epilepsy
        EEGLeft temporal spikes
        MRI brainNormal
        Current medicationsTopiramate, oxcarbazine, levetiracetam, lacosamide
    P4
        HandednessRight
        Age at surgery49 years
        Seizure risk factorsHodgkin's lymphoma age 17, splenectomy, stroke at age 48 years
        Family history of seizures(+) Niece
        Age of seizure onset23 years
        Seizure semiologyGeneralized tonic–clonic seizures, automotor seizures, postictal aphasia
        Working diagnosisLesional left temporal lobe epilepsy
        EEGLeft temporal sharp waves
        MRI brainChronic stroke/encephalomalacia in left putamen, insula, parietal cortex
        Current medicationsCarbamazepine, lamotrigine, levetiracetam
    P5
        HandednessRight
        Age at surgery48 years
        Seizure risk factorsHead trauma with loss of consciousness as a child
        Family history of seizuresNone
        Age of seizure onset37 years
        Seizure semiologyComplex partial seizure
        Working diagnosisNonlesional right temporal lobe epilepsy
        EEGRight temporal sharp waves
        MRI brainFew small T2 hyper-intense foci in frontal subcortical white matter
        Current medicationsLevetiracetam, lamotrigine, oxcarbazine, clonazepam
    P6
        HandednessRight
        Age at surgery57 years
        Seizure risk factorsNone
        Family history of seizuresNone
        Age of seizure onset12 years
        Seizure semiologyComplex partial seizures with secondary generalization; ictal aphasia
        Working diagnosisLeft temporal lobe epilepsy
        EEGLeft posterior temporal sharp waves and seizures
        MRI brainSubtle right hippocampal volume loss
        Current medicationsValproic acid, primidone, Vimpat
    P7
        HandednessRight
        Age at surgery34 years
        Seizure risk factorsNone
        Family history of seizuresNone
        Age of seizure onset24 years
        Seizure semiologyGeneralized convulsions; complex partial seizures; automotor seizures; aphasia
        Working diagnosisLeft temporal lobe epilepsy
        EEGLeft temporal slowing and seizures
        MRI brainNormal
        Current medicationsLamotrigine, phenobarbital, Vimpat
    P8
        HandednessRight
        Age at surgery59 years
        Seizure risk factorsNone
        Family history of seizuresNone
        Age of seizure onset12 years
        Seizure semiologyPsychic aura; automotor seizures; generalized tonic-clonic epilepsy
        Working diagnosisTemporal lope epilepsy
        EEGLeft posterior temporal-parietal sharp waves
        MRI brainSubtle volume loss in right hippocampus
        Current medicationsValproic acid, primidone, Topamax
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Journal of Neuroscience
Vol. 36, Issue 49
7 Dec 2016
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Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
Christina Zelano, Heidi Jiang, Guangyu Zhou, Nikita Arora, Stephan Schuele, Joshua Rosenow, Jay A. Gottfried
Journal of Neuroscience 7 December 2016, 36 (49) 12448-12467; DOI: 10.1523/JNEUROSCI.2586-16.2016

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Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
Christina Zelano, Heidi Jiang, Guangyu Zhou, Nikita Arora, Stephan Schuele, Joshua Rosenow, Jay A. Gottfried
Journal of Neuroscience 7 December 2016, 36 (49) 12448-12467; DOI: 10.1523/JNEUROSCI.2586-16.2016
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Keywords

  • amygdala
  • hippocampus
  • local field potential
  • piriform cortex
  • respiration
  • respiratory oscillations

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  • RE: Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
    Gregory Francis and Aaron M. Clarke
    Published on: 02 April 2017
  • Published on: (2 April 2017)
    Page navigation anchor for RE: Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
    RE: Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
    • Gregory Francis, Professor, Psychological Sciences, Purdue University
    • Other Contributors:
      • Aaron M. Clarke

    Zelano et al. (2016) reported multiple experimental findings to support their conclusion that nasal respiration entrains limbic oscillations and modulates human cognitive functioning. The presented data suggests, among other things, that breathing in/out affects memory retrieval more (d=0.86) than elaborate strategies designed to improve recall (d=0.49; Nairne et al., 2008). This claim is remarkable, if true. Our surprise at this finding led us to ask how likely it is that one would get such results with replication experiments using the same designs and sample sizes. Our analysis indicates that even when assuming the effects are real and accurately measured by the original experiments, the probability of success across three studies like those in Zelano et al. is estimated to be only 0.003. Given such low odds of replication success, we caution readers to be skeptical about the reported results and conclusions in Zelano et al.

    For each behavioral experiment, we estimated the probability that a replication study would produce the same degree of success as the original study. Using the reported test statistics and Figures 8b, 9b, and 10a, we derived the sample means, standard deviations, and correlations. These sample statistics were then used as population estimates in 100,000 simulated experiments with the same sample sizes and statistical analyses as in Zelano et al. (2016). The proportion of simulated samples that generated the patterns of significance reported...

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    Zelano et al. (2016) reported multiple experimental findings to support their conclusion that nasal respiration entrains limbic oscillations and modulates human cognitive functioning. The presented data suggests, among other things, that breathing in/out affects memory retrieval more (d=0.86) than elaborate strategies designed to improve recall (d=0.49; Nairne et al., 2008). This claim is remarkable, if true. Our surprise at this finding led us to ask how likely it is that one would get such results with replication experiments using the same designs and sample sizes. Our analysis indicates that even when assuming the effects are real and accurately measured by the original experiments, the probability of success across three studies like those in Zelano et al. is estimated to be only 0.003. Given such low odds of replication success, we caution readers to be skeptical about the reported results and conclusions in Zelano et al.

    For each behavioral experiment, we estimated the probability that a replication study would produce the same degree of success as the original study. Using the reported test statistics and Figures 8b, 9b, and 10a, we derived the sample means, standard deviations, and correlations. These sample statistics were then used as population estimates in 100,000 simulated experiments with the same sample sizes and statistical analyses as in Zelano et al. (2016). The proportion of simulated samples that generated the patterns of significance reported by Zelano et al. as support for their conclusions was: 0.08, 0.22, and 0.20 for experiments 1, 2, and 3, respectively. The estimated probability of all three experiments being successful is the product of these proportions. The R simulation code and related files are at https://osf.io/hdvy9/

    These success proportions are low because Zelano et al. (2016) based their conclusions and interpretations on many different statistical outcomes. All the reported tests were successful, but often with results near the significance criterion (e.g., p=0.04), and a replication study with the same sample size is estimated to produce a significant outcome for a given hypothesis test only a bit more than half the time, simply due to the inherent variability in random sampling. The odds of the full set (for example, 8 tests in Experiment 1) being uniformly successful for any given random sample are very low. Furthermore, Zelano et al. (2016) reported several additional tests that all supported their conclusions, but their manuscript does not provide enough information to include them in our simulations. Moreover, we did not include the reported neurophysiological studies because the analyses were too complicated for our simulations. The probability of these additional outcomes also being successful must be even lower than our above estimate.

    The excess success in Zelano et al. (2016) undermines their theoretical conclusions. Without knowing the details of the investigations, we cannot speculate on how their experiments produced their reported outcomes. The field of psychology has recently realized that some methods of sampling, analyzing, interpreting, and reporting empirical findings (e.g., Simmons et al., 2011; Francis, 2014) can inadvertently cause this kind of problem, and similar concerns may apply here. Whatever the cause, the findings in Zelano et al. (2016) do not provide appropriate empirical support for their conclusions.

    References
    Francis G (2014) The frequency of excess success for articles in Psychological Science. Psychonomic Bulletin & Review 21:1180-1187.

    Nairne JS, Pandeirada JNS, Thompson SR (2008) Adaptive memory: The comparative value of survival processing. Psychological Science 19:176-180.

    Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science 22:1359-1366.

    Show Less
    Competing Interests: None declared.

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