
==== Front
Biomed J
Biomed J
Biomedical Journal
2319-4170
2320-2890
Chang Gung University

S2319-4170(23)00120-8
10.1016/j.bj.2023.100683
100683
Original Article
Neural oscillatory markers of respiratory sensory gating in human cortices
Liang Kai-Jie ab1
Cheng Chia-Hsiung bcd1
Liu Chia-Yih c
von Leupoldt Andreas e
Jelinčić Valentina e
Chan Pei-Ying S. chanp@cgu.edu.tw
bc∗
a Department of Occupational Therapy, College of Medical Science and Technology, Chung Shan Medical University, Taichung, Taiwan
b Department of Occupational Therapy, College of Medicine, Chang Gung University, Taoyuan, Taiwan
c Department of Psychiatry, Chang Gung Memorial Hospital at Linkuo, Taoyuan, Taiwan
d Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan
e Research Group Health Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Belgium
∗ Corresponding author. Department of Occupational Therapy, Chang Gung University, No.259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 33302, Taiwan. chanp@cgu.edu.tw
1 The authors have equal contributions.

09 12 2023
10 2024
09 12 2023
47 5 10068321 7 2023
5 12 2023
© 2023 The Authors
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Human respiratory sensory gating is a neural process associated with inhibiting the cortical processing of repetitive respiratory mechanical stimuli. While this gating is typically examined in the time domain, the neural oscillatory dynamics, which could offer supplementary insights into respiratory sensory gating, remain unknown. The purpose of the present study was to investigate central neural gating of respiratory sensation using both time- and frequency-domain analyses.

Methods

A total of 37 healthy adults participated in this study. Two transient inspiratory occlusions were presented within one inspiration, while responses in the electroencephalogram (EEG) were recorded. N1 amplitudes and oscillatory activities to the first stimulus (S1) and the second stimulus (S2) were measured. The perceived level of breathlessness and level of unpleasantness elicited by the occlusions were measured after the experiment.

Results

As expected, the N1 peak amplitude to the S1 was significantly larger than to the S2. The averaged respiratory sensory gating S2/S1 ratio for the N1 peak amplitude was 0.71. For both the evoked- and induced-oscillations, time-frequency analysis showed higher theta activations in response to S1 relative to S2. A positive correlation was observed between the perceived unpleasantness and induced theta power.

Conclusions

Our results suggest that theta oscillations, evoked as well as induced, reflect the “gating” of respiratory sensation. Theta oscillation, particularly theta-induced power, may be indicative of the emotional processing of respiratory mechanosensation. The findings of this study serve as a foundation for future investigations into the underlying mechanisms of respiratory sensory gating, particularly in patient populations.

Highlights

• Human respiratory gating investigated by both time- and frequency-domain analysis

• The RREP N1 amplitude S2/S1 ratio averaged 0.71 with peak-to-peak measurements in time domain

• Cortical theta power showed associated with self-rated unpleasantness level of respiratory occlusions

• The attenuation of theta oscillation in S2 in comparison to S1 suggests its involvement in respiratory gating

Keywords

Respiratory sensation
Theta
Alpha
Mechanosensation
Respiratory-related evoked potentials
==== Body
pmcIntroduction

Accurate perception of internal stimuli, such as respiratory sensations, plays a crucial role in recognizing and interpreting internal bodily states, especially in individuals with strong and distressing levels of bodily sensations. Prolonged unpleasant sensations can lead to changes in top-down regulation generated at the cortical level and may contribute to the over-perception of bodily sensations [[1], [2], [3]]. In respiratory diseases (e.g., chronic obstructive pulmonary disease, COPD), subjective perception of dyspnea is an important clinical indicator to be monitored during disease progression [4]. Furthermore, COPD patients suffering from comorbid anxiety disorders often report more symptoms, such as breathlessness and chest tightness, than those without [[5], [6], [7], [8]]. Understanding the cortical processing and cortical control of respiratory sensations can provide valuable insights into the subjective experiences of individuals with respiratory conditions and potentially guide therapeutic interventions to alleviate corresponding symptoms.

Respiratory-related evoked potentials (RREP) are the type of event-related potentials elicited by respiratory stimuli and measured at the scalp using electroencephalogram (EEG). They are frequently used to measure the neural processing of respiratory sensations in the time domain [1,[9], [10], [11], [12], [13], [14]] with good reliability [15]. In addition to the single stimulus paradigm (analogous to the oddball paradigm in the auditory and somatosensory evoked potential literature), a paired inspiratory occlusion paradigm was developed to measure neural gating of respiratory sensations. Neural gating of respiratory sensations refers to a phenomenon that involves cortical inhibition of repetitive respiratory sensory stimuli. This mechanism is crucial for preventing cortical oversaturation by “gating” out redundant stimuli. Respiratory sensory gating can be measured by paired occlusions that are presented in rapid succession with a short inter-stimulus interval. The attenuation of the neural response to the second stimulus (S2) compared to the first stimulus (S1) is thought to reflect sensory gating or the ability of the brain to filter out redundant or irrelevant information. The RREP S2/S1 ratio is a common index to represent the ability of respiratory sensory gating. A person may be suspected to experience over-perception of respiratory sensation if their RREP S2/S1 ratio is relatively high (the ratio is approximately 0.5 or less for healthy populations [16]). Respiratory sensory gating has been investigated in both healthy adults [17,18] and those with anxiety disorders, with higher anxiety levels being associated with reduced respiratory sensory gating [19].

In addition to the time-domain information, analyzing brain oscillations in response to respiratory sensory input offers an alternative way of understanding the neural processing of repetitive respiratory stimuli [20]. Despite some evidence from auditory sensory gating [[21], [22], [23], [24]] and somatosensory gating [25,26], neural oscillatory dynamics elicited by paired respiratory stimuli are still unknown. For auditory sensory gating using a paired-click paradigm, the theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (>30 Hz) oscillations have been investigated in healthy controls (HC) and in patients with schizophrenia and autism spectrum disorder [22,24,26,27]. Moreover, the associations between these oscillatory components and the P50 amplitude or P50 S2/S1 ratio (P50, a common indicator in auditory sensory gating, presents as a positive deflection around 40–80 ms post-stimulus) have been investigated. For the HC group, evoked beta powers of S1 and S2 were positively associated with the P50 S2 amplitude [21]. The induced beta and low frequencies (including theta and alpha) of S1 were found to contribute to the P50 suppression of the S2 or the S2/S1 P50 ratio [22,23]. For schizophrenia patients, some studies showed significantly lower beta and gamma responses to S1, and others showed lower beta and gamma responses to both S1 and S2 compared to HC [21]. The relationships between oscillatory components and sensory gating also showed different patterns between HC and schizophrenia patients [24]. These studies suggested that the frequency range containing four bands (theta, alpha, beta, and gamma) might be relevant to the mechanism of sensory gating.

The purpose of this study in healthy adults was to investigate respiratory sensory gating in the time and frequency domains using the paired occlusion paradigm. We hypothesized that the neural oscillations in healthy adults would show gating phenomena in corresponding frequency bands. Depending on the analysis pipeline, oscillatory activities can be categorized into two distinct types: (i) evoked oscillations, which are elicited by and phase-locked to the stimulus, are computed by applying frequency transformation to the averaged ERPs, and (ii) induced oscillations, which are triggered by a stimulus but are not phase-locked to it, are computed by averaging the power of the frequency-transformed single epochs [28]. Operationally, in induced oscillations, the evoked power and background components are subtracted after averaging the power of each epoch. Even though the functional differences were found between evoked and induced oscillations [29], previous studies in the Auditory Evoked Potential (AEP) or Somatosensory Evoked Potential (SEP) did not systematically compare the oscillatory dynamics derived from these two methods [[22], [23], [24],26,27]. In the present study, both the evoked and induced oscillations were investigated. In addition, given the mixed results regarding the relationship between the perceived experience of respiratory stimuli and respiratory sensory gating [13,19,19,30], we were interested in the relationship between subjective reports of breathlessness and neural gating of respiratory sensations.

Materials and methods

Participants

A total of 37 healthy volunteers aged at least 20 years old were recruited to participate in the present study. All participants self-reported that they had no history of smoking nor current cardiovascular, respiratory, psychiatric, and neurological disorders. The demographic information is shown in [Table 1].Table 1 Demographic, respiratory variables, and breathlessness/unpleasantness of occlusions of participants (mean ± SD).

Table 1Variables		
N	37	
Age (yr)	26.30 ± 4.88	
Gender (female/male)	23/14	
FEV1 of predicted value (%)	86.08 ± 6.03	
FVC of predicted value (%)	82.86 ± 6.39	
FEV1/FVC (%)	103.32 ± 5.65	
Breathlessness (0–100)	42.79 ± 24.00	
Unpleasantness (0–100)	46.44 ± 27.95	
Accuracy of occlusions count (%)	90.06 ± 12.93	
Abbreviations: FEV1(L): forced expiratory volume in 1 s (liter); FVC (L): forced vital capacity (liter).

Experimental procedure

Written informed consent was obtained from the participants prior to the experiment. This study was approved by the Institutional Review Board of the Chang Gung Medical Foundation. Before starting the experiment, a pulmonary function test (PFT) was performed with a standard spirometry (Cardinal Health Inc., Dublin, OH, USA) in accordance to the guidelines of the American Thoracic Society and European Respiratory Society. All subjects required to meet the criteria of Forced Expiratory Volume in 1 s (FEV1) of greater than 70 % of the predicted normative value to continue.

Respiratory apparatus

The details of the respiratory apparatus were described previously [10]. Briefly, the participants were sitting in a comfortable chair with their neck, back, arms, and legs supported while breathing via a mouthpiece through a two-way non-rebreathing valve (Hans Rudolph Inc., Kansas City, USA) and a breathing circuit. The mouthpiece was suspended to minimize facial muscle activity. The participants' mouth pressure was measured and recorded at the center of the non-rebreathing valve using a differential pressure transducer, which was connected to both the pneumotachograph amplifier (1110 series, Hans Rudolph Inc., Kansas City, MO, USA) and a PowerLab signal recording unit (ADInstruments Inc., Bella Vista, NSW, Australia). The inspiratory port of the valve was connected with reinforced tubing to a custom-designed pressure-activated occluder that was controlled by a trigger system [16]. The occlusion valve closure was performed manually at the start of inspiration by triggering a solenoid using a pressurized air tank.

The RREP paradigm

The participants were provided with 100 paired inspiratory occlusions, consisting of two 150-ms occlusions delivered within a single inspiration and separated by a 500-ms inter-stimulus interval. The onset of occlusion was timed to the start of mouth pressure change (Labchart V7, ADInstruments Inc., Bella Vista, Australia). Paired occlusions were randomly delivered at the start of an inspiration every 2–4 breaths. At least 100 paired occlusions were administered to each participant. The trigger box was programmed to send parallel markers to the Neuroscan 4.5 recording software (Compumedics Neuroscan Inc., Charlotte, NC, USA). Participants were instructed to breathe normally and count the number of inspiratory occlusions they felt during the experiment.

EEG recordings and data analysis

The EEG signals were recorded using a 40-channel EEG system (NuAmps, Compumedics Neuroscan, Inc., Charlotte, NC). The spatial layout of the customized electrode locations was based on the extended international 10–20 system. All scalp electrodes were online referenced to the right mastoid. The data were sampled at 1 kHz, and electrode impedances were kept below 5 kΩ. Offline analyses were performed using BrainVision Analyzer (BVA 2.2.2, Brain Products GmbH., Gilching, Germany).

For the time domain measurement, raw EEG signals were filtered between 0.3 and 50 Hz (12 dB/octave roll-off). Then, the signals were downsampled to 500 Hz and re-referenced to the average of the bilateral mastoid electrodes. The time window of each epoch was 1500 ms (both S1 and S2 were thus included), with a 200 ms pre-stimulus baseline. Eyeblinks and ocular movements were corrected using the Gratton and Coles algorithm [30]. Epochs with artifacts were defined as containing signal amplitudes greater than 100 and 60 μV, baseline to peak, for the four eye electrodes and all other electrodes, respectively, and were rejected from averaging. The remaining epochs were subsequently averaged. In the present study, we specifically focused on the N1 peak at the Cz electrode. The N1 peak, the most notable component of the RREP, is thought to reflect both first- and second-order processing of respiratory sensations [16]. As in the previous study [27], the peak-to-peak amplitude of the N1 component was scored as the difference in amplitude between the P1 peak and the N1 peak. The P1 peak was defined as the maximum positive peak between 40 and 100 ms, and the N1 peak was defined as the most negative peak between 70 and 180 ms after the occlusion onset. The S2/S1 ratios were calculated for further analysis.

For time-frequency analyses, raw EEG signals were filtered with a high-pass of 0.3 Hz (12 dB/octave roll-off) and down-sampled to 500 Hz. They were then re-referenced to the average of the bilateral mastoid electrodes and epoched from 800 ms before to 2000 ms after the onset of the S1 occlusion. The same artifact correction procedure was applied as in the time domain analysis using the BVA software. To extract the neural oscillations, a complex Morlet wavelet with a range of frequencies from 1 to 60 Hz in 60 logarithmically spaced steps (wavelet length = 131.99 ms) was used. The Morlet parameter was set to 6 cycles to ensure an adequate balance between time and frequency precision. After wavelet convolution, time-frequency power was extracted from the complex signal and then normalized using decibel (dB) with reference to the −800 to −100 ms pre-stimulus baseline.

Two different measures of neural oscillatory activity, the evoked and induced oscillations, were obtained. For the evoked activity, the wavelet transformation was applied to the averaged stimulus-locked ERPs. For the induced activity, the time-frequency transformations of individual epochs were averaged [31]. The Cz channel, which exhibited the largest N1 peak amplitude, was used for analysis. Based on the grand-averaged time-frequency plots and previous studies [22,23,25], the time windows of interest for the evoked activity were selected as follows: 0–400 ms for the theta (4–7 Hz) frequency band, 0–300 ms for the alpha (8–12 Hz) frequency band, 0–200 ms for the beta (13–30 Hz) frequency band, and 0–100 ms for the gamma (30–50 Hz) frequency band. For the induced activity, the time window of 0–400 ms was selected for the theta (4–7 Hz) frequency band. All frequency activity values were obtained from the comparisons with the activity levels in the baseline interval, which was set to 0. Positive values indicate a higher power, whereas negative values indicate a lower power than that of the baseline.

Measurement of breathlessness

After the experiment, the participants were instructed to rate the perceived intensity of breathlessness and the unpleasantness of breathlessness that they experienced during the trial using a visual analog scale (VAS). The scale ranged from 0 to 100, where 0 indicated no breathlessness/unpleasantness, and 100 indicated maximal level of breathlessness/unpleasantness.

Statistical analysis

Descriptive statistics, including means and standard deviations, were calculated for all the continuous variables, while frequencies were calculated for categorical variables. The differences in peak-to-peak amplitude of N1 component and time-frequency power between S1 and S2 were compared via 2-tailed paired t-tests with an alpha level of 0.05. To prevent false positives resulting from multiple comparisons, the Benjamin and Yekutieli false-discovery rate method (B–Y FDR) was used [32,33], with the calculation α′=(α/∑i=1k(1/i)), and the number of tests k = 6 (N1 component and five frequency bands/time windows of interest). Therefore, the corrected alpha for paired t-tests was 0.0204. The B–Y FDR method is considered effective at minimizing Type-I and Type-II errors in datasets with the potential inter-correlation among the measured variables [33]. For investigating the relationships between subjective reports and time- and frequency-based parameters, Pearson correlations were used. The corrected alpha level of 0.0134 was determined by the B–Y FDR method with k = 23 [see Table 3]. All statistical analyses were performed using JAMOVI software version 2.2.Table 2 The results of time domain (RREP) and time-frequency domain (evoked and induced activities) in response to S1 and S2.

Table 2RREP peak-to-peak amplitude (μV)	S1 (mean ± SD)	S2 (mean ± SD)	
N1	−9.88 ± 5.43	−5.95 ± 2.42∗	
Evoked power (dB)	
Theta	12.59 ± 3.97	7.91 ± 4.03∗	
Alpha	6.52 ± 5.12	2.76 ± 3.95∗	
Beta	4.49 ± 3.32	3.55 ± 3.15	
Gamma	2.25 ± 2.89	3.39 ± 2.99	
Induced power (dB)	
Theta	1.69 ± 1.54	−0.91 ± 0.80∗	
∗corrected p value < 0.0204 using the Benjamin and Yekutieli false-discovery rate method.

Table 3 Pearson correlations between subjective reports, N1 S2/S1 ratio, and frequency-based measures.

Table 3Variables	Breathlessness	Unpleasantness	
Unpleasantness	0.68∗		
N1 S2/S1 ratio	0.11	−0.06	
Evoked power	
S1 theta	−0.15	0.06	
S2 theta	0.16	0.30	
S1 alpha	−0.15	−0.05	
S2 alpha	0.21	0.15	
S1 beta	−0.03	0.20	
S2 beta	0.11	0.23	
S1 gamma	−0.16	−0.15	
S2 gamma	−0.30	−0.10	
Induced power	
S1 theta	−0.19	−0.09	
S2 theta	0.33	0.43∗	
∗corrected p value < 0.0134 using the Benjamin and Yekutieli false-discovery rate method.

Results

Thirty-seven participants (age = 26.30 ± 4.88 years; 23 Females) were included in the final analysis. [Table 1] shows the demographic data, the pulmonary function test results as well as the subjective ratings on the perceived level of breathlessness and unpleasantness.

The group-average RREP waveforms at the Cz electrode for S1 and S2 are shown in [Fig. 1]. The S1 peak-to-peak amplitude for the N1 peak was significantly larger than the S2 N1 amplitude [−9.88 ± 5.43 μV and −5.95 ± 2.42 μV, respectively for S1 and S2, p < 0.0204; see [Table 2]. The group averaged RREP N1 peak amplitude S2/S1 ratio was 0.71 ± 0.36.Fig. 1 Grand average (N = 37) RREP waveform from the Cz electrode.

Fig. 1

In the time-frequency analysis, S1 evoked significantly stronger activity in the theta frequency band compared to S2 (12.59 ± 3.97. dB and 7.91 ± 4.03 dB for S1 and S2 theta band, respectively; p < 0.0204). Similar results were found for the alpha frequency band (6.52 ± 5.12 dB and 2.76 ± 3.95 dB for S1 and S2 alpha band, respectively; p < 0.0204). Although there were clearly visible evoked powers observed in the beta and gamma frequency bands [Fig. 2], the statistical results did not reveal any significant differences between S1 and S2 in these bands (4.49 ± 3.32 dB and 3.55 ± 3.15 dB respectively for S1 and S2 beta band; 2.25 ± 2.89 dB and 3.39 ± 2.99 dB respectively for S1 and S2 gamma band; both p > 0.0204). In addition, the S1 time window exhibited significantly stronger induced activity in the theta frequency band compared to S2 (1.69 ± 1.54 dB and −0.91 ± 0.80 dB respectively for S1 and S2 theta band; p < 0.0204) [Table 2]. In contrast to the evoked activities, no visible activities were observed in the alpha, beta, and gamma bands for either S1 or S2 in terms of induced power [Fig. 3]. Furthermore, no significant differences were observed between male and female participants in terms of the PFT results, VAS ratings, the S2/S1 ratios as well as the beta and theta band oscillations. For the relationships between subjective reports and neural oscillations, a significant positive relationship (r = 0.43, p < 0.0134) was found between breathlessness unpleasantness and induced theta power in response to S2 [Table 3].Fig. 2 Time-frequency plot of evoked EEG activity. The time axis in the figure is marked with a white line at 0 ms, indicating the onset of the first occlusion (S1), and another white line at 650 ms, indicating the onset of the second occlusion (S2). Compared to the activities in response to S2, the activities in response to S1 demonstrated stronger power of theta and alpha oscillations (red rectangles) in the paired occlusion paradigm.

Fig. 2

Fig. 3 Time-frequency plot of induced EEG activity. The time axis in the figure is marked with a white line at 0 ms, indicating the onset of the first occlusion (S1), and another white line at 650 ms, indicating the onset of the second occlusion (S2). Compared to the activities in response to S2, the activities in response to S1 demonstrated stronger power of theta oscillation (red rectangles) in the paired occlusion paradigm.

Fig. 3

Discussion

The current study examined the respiratory sensory gating in both the time- and frequency-domain using the paired-occlusion paradigm in healthy adults. Our time domain results demonstrated that the S2 N1 peak amplitude was reduced compared to the S1, which indicates that a commonly observed gating pattern [12,16]. For the hypothesis, we found stronger evoked theta power, evoked alpha power, and induced theta power, but not beta and gamma powers, in response to S1 compared to S2 occlusions. Reduced N1 peak, theta and alpha powers in response to S2 suggested that the respiratory sensory gating can be found not only in the time domain but also in the time-frequency domain. Moreover, our results showed a positive correlation between the level of unpleasantness of breathlessness and induced theta oscillation in response to the S2.

In the time domain, our result of reduced N1 S2 amplitude is in line with the findings in previous reports suggesting suppression of respiratory sensory stimuli at the cortical level. It is noted that the N1 peak S2/S1 ratio in this study was 0.71, which is somewhat higher than in previous studies showing RREP N1 peak S2/S1 ratio of less than 0.5 in healthy adults [9,16,34,35]. However, this may have arisen from the differences between baseline-to-peak and peak-to-peak methods of quantifying the N1 peak amplitude, as well as from subtracting only the pre-S1 baseline. Although the similar results of N1 peak amplitude of auditory sensory gating based on the two methods have been found [36], the outcomes of these different methods for the respiratory sensory gating have not yet been compared. Considering the different calculations between the two methods, the peak-to-peak method is recommended as it helps mitigate the potential exclusion of participants with negative gating ratios [2]. In this study, we, therefore, report the first instance of quantifying the N1 S2/S1 ratio of respiratory sensory gating by using the peak-to-peak method.

In the current study, a reduction of both evoked and induced powers in response to S2 was observed in the theta band, indicating the “gating” phenomenon. This result suggests that respiratory sensory gating can be observed in both time and frequency domains, which is similar to the evidence in the auditory sensory gating literature [22,23,26]. For example, Hong et al. found the suppression of theta rhythm to S2 in healthy adults by using a paired-click paradigm to elicit auditory evoked potentials [23]. Theta rhythm is critical for regulating information gating within the hippocampal-prefrontal network [37], which may explain its involvement in sensory gating. In addition, theta rhythm has been found to synchronize during the encoding of emotional stimuli [38]; therefore, it is expected to observe increased theta power during the delivery of unpleasant occlusions. Increased theta rhythm was also found in chronic pain patients [39,40], which suggests a potential relationship between altered theta rhythm and the processing of unpleasant bodily stimuli. In the present study, induced theta power to S2 was associated with the level of unpleasantness elicited by the occlusions. This indicates that lower desynchronization (less power decrease) of induced theta oscillations in response to S2 was associated with higher levels of unpleasantness. In addition, only the induced, rather than the evoked, theta power was associated with the level of unpleasantness, possibly because induced powers reflect the top-down neural processes that are more closely related to the emotional aspects of respiratory sensations [29,[41], [42], [43]]. Interestingly, theta oscillations in response to S1 were not associated with subjective reports of perceived unpleasantness in the present study. The current results may indicate that the induced brain theta activations in response to only S2, rather than S1, may be considered a potential central neural marker of subjective interoceptive sensations.

While evoked alpha power also displayed the gating phenomenon, the observed pattern was not consistent with our initial expectations. Decreased alpha powers or alpha desynchronization usually precedes cortical activation in response to relevant stimuli [23,44]. Therefore, when individuals filter out or inhibit the redundant stimulus, increased alpha powers may be expected. However, in a study employing paired auditory stimuli, it was observed that the power in the evoked alpha band (8–12 Hz) decreased just before or at the onset of S2 [45]. The authors argued that this reduction in alpha power preceding the onset of S2 may reflect a preparatory state that enhances the comparison of S2. In our study, the evoked alpha power was decreased following, but not before, the onset of S2, suggesting potentially increased cortical activation occurring to filter out S2. This mixed result may be due to the differences between modalities. So far, there is insufficient evidence to support the role of alpha band in gating phenomena; therefore, future studies are required to investigate the relationships between alpha oscillations and the gating phenomenon in more detail.

In the current study, we observed robust evoked powers in the beta and gamma frequency bands in response to both S1 and S2 stimuli with no significant differences between the two stimuli. In previous studies looking at auditory evoked potentials, attenuated evoked beta and gamma powers to S2 were observed in healthy controls but not in schizophrenic patients with the paired-click paradigm [22,26]. The activity in beta frequency has been linked to stimulus-driven salience processing, which is determined mainly by the physical characteristics of the stimulus [42,46]. In addition, immediately evoked gamma activations are believed to represent the early stages of sensory registration [47]. Therefore, patients with schizophrenia were considered to have impairments in the registration of sensory memory traces, potentially leading to compromised perceptual experiences and cognitive dysfunction [21,24]. Unlike the evidence found with the auditory modality, respiratory sensory evoked beta and gamma oscillations were not reduced for S2 in our study. One potential explanation for the disparity is that repetitive stimuli associated with survival functions, such as respiration, may still undergo the “gating in” process for initial registration and salience detection as represented by the preserved beta and gamma and only undergo “gating out” for further perceptual and emotional processing as reflected by the attenuated theta and alpha bands.

In addition, in the current study, beta and gamma oscillations were found with the evoked power analysis, but these bands were not visible using the induced power analysis method. With a paired-click paradigm, Nguyen et al. discovered that the evoked and induced oscillations in beta and gamma frequencies displayed distinct patterns [24]. Specifically, only the evoked, not the induced, oscillations have demonstrated the capacity to differentiate between healthy individuals and those with schizophrenia. Another study by Kisley and Cornwell, examining cross-modal conditions, found that gamma-band power was only visible in the evoked and not induced activity, which further lends support to our current results in the respiratory domain [42]. Some studies have suggested that the absence of induced activity reflects the lack of top-down modulation and stimulus-related attention [[48], [49], [50]]. However, in the present study, participants were required to direct their attention to the respiratory occlusions and mentally count the number of stimuli they experienced, yet the induced gamma and beta power was still minimal. Additionally, another study by Cheng et al. also found attenuated induced power in the gamma band in response to S2 by using median nerve stimulation. Based on these and our current results, it is unlikely that these two bands exclusively reflect selective-attention driven or top-down derived neural activity [25,51]. Nevertheless, further research regarding induced beta and gamma oscillations in different sensory domains is required for clarification.

Finally, the correlational analysis revealed a significant relationship between the level of unpleasantness of breathlessness and induced theta power (p < 0.0134). However, we did not find a significant association between the level of unpleasantness and neural gating of respiratory sensation from time domain (i.e., N1 S2/S1 ratio). The lack of a direct relationship between individuals’ unpleasantness of breathlessness and their respiratory sensory gating performance is not surprising, as the past results in the field have been somewhat mixed [11,12,19,34]. For example, Herzog et al. found that reduced neural gating was associated with increased dyspnea intensity and unpleasantness [11]. Another two reports by Chan et al. found that anxious emotion or anxiety disease states were positively associated with respiratory gating ratios (hence decreased gating function) [19,34]. In contrast, Jelinčić et al. tested the relationships between dyspnea and both respiratory sensory gating as well as somatosensory gating with a larger sample size of healthy participants [12]. While they did not find a significant relationship between the level of dyspnea unpleasantness/intensity and respiratory gating ratio, an inverse relationship was found between the paired occlusion unpleasantness level and somatosensory gating ratio [12]. Given the relevance of theta power band to chronic pain perception [52], our results implicate induced theta power in subjective dyspnea unpleasantness. It is speculated that, as a top-down controlling factor, subjective emotions towards dyspnea experience could further enhance or inhibit the theta power band expression to S2. If the above notion is true, it would assist with the explanation of top-down intervention approaches, such as mindfulness training for the subjective perception of dyspnea in respiratory or anxiety diseases. Future research is recommended to further investigate this potential top-down mechanism in patient populations.

A few limitations should be considered in the current study. Firstly, our study sample consisted of only healthy adults who were rather young. Therefore, the findings of our study cannot be generalized to other populations, such as elderly individuals or those with respiratory diseases or anxiety disorders. Secondly, an attention condition was utilized in the present study, where the participants were required to count the number of paired obstructed breaths during the experiment. This top-down modulation may have had different impacts on the observed oscillations as compared to ignore or passive attention conditions, which most other studies have utilized. Third, while the utilization of the peak-to-peak analysis method allowed for the retention of a maximum number of data points, thus enhancing statistical power, the sample size is still relatively small. Future studies with larger sample sizes are recommended to assess the effect of attention and explore the potential influences of top-down modulations.

Conclusions

In summary, the present study demonstrated that both evoked and induced theta neural oscillations are attenuated in response to repeated stimuli during the paired-occlusion paradigm. Moreover, the induced theta power to the repeated stimulus was significantly related to self-reported unpleasantness of occlusions. These results suggest that theta oscillations could be complementary to the ERP-based gating ratio to provide additional information on neural gating of respiratory sensation, especially in datasets with fewer trials or lesser quality of data (for example in patients with respiratory diseases). Despite the visible beta and gamma oscillations in the evoked activity, neither corresponding activations in the induced activity nor distinct gating phenomenon were observed. The discrepancies between the evoked and induced activities in neural gating of respiratory sensation have yet to be investigated.

Conflict of interest

No potential conflict of interest was reported by the author(s).

Acknowledgments

The project was supported by the Taiwan National Science & Technology Council (MOST-109-2320-B-182-008-MY3 & NSTC-112-2320-B-182-010-MY3), as well as the Linkou 10.13039/100012553 Chang Gung Memorial Hospital (BMRPB96) in Taiwan. We also wish to thank Tung-Han Huang for his assistance in data collection and organization. VJ and AvL were supported by grants from the 10.13039/501100003130 Research Foundation Flanders (10.13039/501100003130 FWO ), Belgium (G0C1921N), by an 10.13039/501100003130 FWO PhD fellowship (11G1320N), by an infrastructure grant from the 10.13039/501100003130 FWO and the Research Fund 10.13039/501100004040 KU Leuven , Belgium (AKUL/19/06; I011320N), and by a project grant of the Research Fund 10.13039/501100004040 KU Leuven , Belgium (C16/23/002).

Peer review under responsibility of Chang Gung University.
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