Respiration as a dynamic modulator of sensory sampling

Domain: neuroscience
Published: 2026-04-07
Editorial outcome: minor_revision

Abstract
Respiration dynamically modulates sensory perception by orchestrating transient states of the brain and the body. Using simultaneous recordings of high-density magneto-encephalography (MEG), respiration, and pupillometry, we show that human perceptual sensitivity to near-threshold visual stimuli was enhanced during inspiration, coinciding with respiration-modulated increases in arousal neuromodulation and cortical excitability. Participants adapted their breathing patterns to align with predictable stimulus onset, and this adaptive respiratory alignment correlated with improved performance. We further reveal that respiration-modulated changes in alpha and beta oscillations reflect distinct shifts in sensory and motor excitability, respectively. Crucially, respiration-resolved multivariate Granger causality analyses demonstrate that the breathing rhythm systematically shapes directed information flow within a widespread interoceptive network. This respiration-brain coupling was flexibly adjusted based on stimulus predictability, highlighting a mechanism for active sensing which integrates internal bodily rhythms with external sensory demands to optimise perception.

Introduction
Recent work continues to highlight the link between bodily and neural rhythms and its role in human behaviour1. At rest, for example, the breathing rhythm is systematically coupled to so-called respiration-modulated brain oscillations (RMBOs) across a widespread anatomical network2,3. Consequently, there is now a substantial body of evidence showcasing respiratory modulation of human perception, particularly in the visual domain4–6. From an interoceptive inference perspective7, the relationship between the breathing rhythm and human perception is by no means accidental: As respiration is uniquely under voluntary control, it may be used to actively align sampling of sensory information with neural and bodily states which facilitate performance. In humans, this application of active sensing8 is hypothesised to implicate two processes in particular in order to entrain brain, body, and behaviour.

First, transient states of arousal neuromodulation describe a global physiological preparedness to process sensory information9 and are therefore closely linked to human perception and cognition10. Under the control of the locus coeruleus (LC) and the nucleus basalis of Meynert, respectively, norepinephrine (NE) and acetylcholine (ACh) are released to widespread cortical regions11 to regulate transitions between, e.g., attentional and behavioural states governing perception12. As NE release also affects pupil size, pupillometry has been established as an effective readout of arousal13. Not only have fluctuations in pupil-linked arousal been shown to influence cortical activity14 and cognitive function15, but recent work has rekindled the field’s interest in respiratory modulations of arousal dynamics: Synaptic connections within the brain stem link the pre-Bötzinger complex-the central pattern generator of the respiration rhythm-to LC, suggesting that respiratory dynamics may influence arousal states16. Building on seminal rodent work demonstrating that interrupting the connection between both cores causes chronic hypoarousal and lethargic behavior in animals17, it is now becoming increasingly clear that human respiration and pupil-linked arousal are systematically coupled18. Functionally, this link has predominantly been interpreted as serving the synchronisation of attentional states during perception, particularly in the visual domain19.

A second vital determinant of perceptual processing is cortical excitability, i.e., the continuous balance between excitatory and inhibitory currents in the brain. In general, endogenous shifts in excitability states are a core characteristic of the brain as a dynamic system. Excitation-inhibition (E:I) balance within primary sensory cortices immediately affects behaviour, as e.g. excitability of the visual system determines whether a near-threshold stimulus would be perceived or not. The respiratory rhythm has been demonstrated as a modulator of both aperiodic20,21 as well as oscillatory signatures of transient excitability states2,4,22. In behavioural paradigms, there is extensive evidence for an intricate connection between neural oscillations, excitability states, and behavioural performance: During visual perception, excitatory input to the visual cortex is regulated by functional inhibition in a feed-forward mechanism based on alpha oscillations (7-13Hz), effectively controlling the excitability of the neural system per se23. This leads to an inverse relationship between prestimulus alpha power and excitability, corroborated by cross-modal evidence of strong ongoing alpha oscillations coinciding with reduced single-unit firing rates24 and local field potentials25. In this context, and in line with the active sampling framework outlined above, the proposed role for the respiratory rhythm concerns the temporal alignment of information sampling with states of particularly high excitability4. In the motor domain-i.e., when responding to rather than perceiving sensory stimuli - respiration has similarly been linked to beta oscillations (15–40Hz5,26), a prominent signature of predictive timing during perceptual processing27.

To summarise, previous work has put the respiratory rhythm at the forefront of potential physiological modulators of behaviour. At present, converging evidence from different lines of research strongly suggests respiratory involvement in active sensing, including adaptation of respiratory behaviour to task timing28, modulation of excitability and arousal states4, and behavioural facilitation29,30. In the animal literature, active sensing has been suggested to comprise both a homeoactive and an alloactive component31. In this framework, alloactive sensing posits the use of mechanical energy to alter the parameters of the sensory apparatus, including voluntary changes in the respiratory rhythm driven by internal forward models of sensory predictions. What has so far been critically missing, particularly in humans, is a direct test of previous correlational findings within a dedicated predictive processing paradigm to answer fundamental open research questions: To what extent does respiration orchestrate excitability and arousal states across different contexts of stimulus predictability? What are the behavioural benefits of this respiration-brain coupling? And finally, how is the facilitative coupling of brain and body mechanistically achieved on the neural network level?

In the present study, we addressed these questions using simultaneous high-resolution magnetoencephalography (MEG) and respiratory data recorded during a cued visual detection paradigm. Specifically, we presented lateralised, near-threshold visual stimuli whose predictability was systematically manipulated with temporal and spatial cues. We report increased perceptual sensitivity for stimuli presented during the inspiration phase, coinciding with increased cortical excitability. This behavioural facilitation was achieved through adaptive adjustment of individual breathing patterns to task demands, with stronger adjustments linked to better performance. Pupil-linked arousal states depended on stimulus predictability and respiration phase, with a larger pupil diameter during predictable trials associated with improved performance during inspiration. Both alpha-band activity (indicating excitability states during visual perception) and beta-band activity (indicating predictive timing during motor preparation) were modulated by respiration phase and stimulus predictability. Multivariate source-level and Granger causality analyses further showed that respiration phase systematically orchestrated information flow within the RMBO network, directly influencing perceptual sensitivity. Collectively, these results underscore respiration as a highly adaptive modulator of neural dynamics and provide evidence for the neural mechanisms by which it facilitates behaviour.

Results
Behavioural performance
Participants performed a simple near-threshold visual detection task in which Gabor patches were briefly presented either left or right of a central fixation point. Critically, stimuli could be preceded by spatial and/or temporal cues which systematically manipulated stimulus predictability: During fixation, the neutral fixation cross could either be replaced by an arrowhead (cued trials, C) and/or be surrounded by a circular countdown or not (timed trials, T; Fig. 1a). These manipulations allowed participants to predict the location and/or the temporal onset of the target stimulus. Upon button press, participants reported whether they had seen the target on the left, the right, or none at all. An adaptive QUEST staircase was used to adjust target contrast in a way that performance would settle at an overall hit rate (HR) of around μ = 0.60. On the group level, we observed an empirical mean hit rate of HR = 0.55 ± 0.02 (M ± SD) across conditions.

The detection of near-threshold stimuli was facilitated when cue information was available during the fixation period. A linear mixed effect model (LMEM) revealed significantly higher hit rates for spatially cued (HR = 0.57 ± 0.05; M ± SD) vs uncued trials (HR = 0.52 ± 0.05; t(116) = 4.28, p < 0.001) and for timed (HR = 0.56 ± 0.05) vs untimed trials (HR = 0.54 ± 0.06; t(116) = 2.00, p = 0.047). The interaction term cueing X timing remained insignificant (t(116) = −0.12, p = 0.901); individual hit rates for each condition are shown in Fig. 1b.

Respiratory phase information explains behavioural variance
Aiming first to replicate reports of general respiratory involvement in perception, we tested whether single-trial performance could be predicted from the respiratory phase angle at which a given stimulus was presented. As in a previous near-threshold perception study4, we first setup a mutual information model to predict stimulus detection (hit, miss) from a combination of single-trial stimulus contrast, spatial cues (yes/no), and temporal cues (yes/no). Confirming the performance effects shown in Fig. 1b, including spatial and temporal cues significantly improved the binary prediction of correct vs. incorrect responses compared to a base model that only used single-trial contrast information (t(29) = 2.64, p = 0.013). In a second step, we also included sine and cosine of the respiratory phase at stimulus onset, which once again significantly improved the prediction of individual performance (t(29) = 7.94, p < 0.001). In sum, these results demonstrate that both stimulus predictability (spatial, temporal) and respiration phase-fully described by its sine and cosine components-had a significant effect on behavioural performance.

In light of these findings, we next aimed to characterise which periods of the respiratory cycle were driving these effects, i.e. at which respiratory phase behavioural performance was particularly modulated. Based on previous findings4,6, we hypothesised a performance increase during the inspiratory phase. We exploited single-trial information regarding stimulus contrast and detection performance to iteratively refit the psychometric function. In short, the psychometric function expresses the sigmoidal relationship between the intensity of a stimulus and the probability of a particular response. In our analysis, we first fitted the overall psychometric function to all target trials (irrespective of condition), quantifying detection probability as a function of stimulus contrast. The resulting parameters were used as priors for a moving window approach in which we iteratively refitted the psychometric function to a subset of trials presented at a certain range of respiration phase angles. To this end, respiratory phase at the onset of each target stimulus was extracted and assigned to one of k = 60 phase bins with all other trials presented at a similar respiratory phase (see4 and Methods for details). Refitting the psychometric function for the subsets of trials assigned to each of these overlapping phase angle bins, we thus obtained a threshold estimate characterising perceptual performance at that respiration phase: Since the QUEST response criterion was fixed at μ = 0.60, a lower threshold indicates higher sensitivity (i.e. lower stimulus intensity was sufficient to yield the same level of performance). In support of our hypothesis, we observed a significant lowering of the perceptual threshold around the midway point of inspiration (ranging from −76° to −46°, see Fig. 1c), indicating an inspiration-locked increase in perceptual sensitivity across all trials. A subsequent analysis of individual conditions revealed that the beneficial inspiratory effect was driven by spatially and temporally predictable targets (C+T+; see insert in Fig. 1c). It should be noted that phase-related changes in decision criterion could occur simultaneously with the sensitivity modulation, but their computation would require changes in the experimental paradigm (i.e. the introduction of catch trials).

The respiratory rhythm is adapted to task dynamics
Finally, since respiration is under voluntary control, we aimed to close the loop on respiration-locked performance modulations and investigated whether respiratory dynamics themselves would actively be adjusted to the temporal regime of the task in order to facilitate performance. Given the inspiratory facilitation for stimulus detection (Fig. 1c), the first question was whether this ‘preferred phase’ could be observed as phase-locking of single-trial data. To allow participants to actively adjust their breathing rhythm to predictable stimuli, we designed the perceptual task in such a way that the prestimulus period was kept consistent across conditions and long enough to potentially adjust the slow respiratory rhythm.

One way for participants to actively achieve stimulus occurrence at the phase of highest sensitivity (i.e., mid- to late inspiration) would be to adjust their breathing pattern to the perceived task timing. Hence, potential respiratory changes during the task would hint at a beneficial adaptation of breathing to the temporal regime of the task. To directly analyse the potential facilitative effects of respiratory adaptation, we assessed participants’ changes in respiratory rate (RR) from independent resting state recordings (nasal and oral breathing) to the task recording and related these RR changes to task performance. Nasal breathing during the task was significantly faster than nasal breathing at rest (z = 3.67, p < 0.001; Fig. 1d). Confirming the initial behavioural results, we did in fact observe clustering of single trials during the inspiratory phase for highly predictable C+T+ trials (Hodges-Ajne test against uniformity: p = 0.005), but not for C-T- trials (p = 0.560). A direct within-subject comparison between median angles revealed that this difference in preferred phases is indeed meaningful, as the mean circular difference between the two conditions was significantly different from zero (p < 0.001; Fig. 1e). Moreover, across all conditions, the more strongly participants adapted their respiratory frequency during the task (compared to rest), the better their performance (i.e. the lower the contrast necessary to achieve the targeted hit rate; see Fig. 1f). Critically, circular-linear correlation analysis revealed a robust positive correlation between rest-to-task change in respiration rate and preferred phase at stimulus onset for timed-only stimuli (C-T+; ρ(28) = 0.50, p = 0.023). This finding strongly suggests that respiratory dynamics were indeed adjusted in such a way that temporally predictable stimuli would be perceived at the preferred respiratory phase associated with the highest perceptual sensitivity (Fig. 1f).

Overall, behavioural analyses showed that perceptual sensitivity for near-threshold stimuli was greatest during mid- to late inspiration and that participants capitalised on stimulus predictability to align respiration to target onset. Here, participants who achieved the best temporal alignment between respiration and target onset gained the strongest perceptual benefit.

Arousal states reflect stimulus predictability and are linked to behaviour
Following up on these respiration-modulated behavioural facilitation effects, our next aim was to understand their neural signatures. Given that pupillary dynamics provide a well-established proxy of noradrenaline-driven changes in arousal states13, we investigated pupil responses during the prestimulus window, i.e., between cue onset and target onset. Controlling for lighting changes on the screen (i.e. the presence of an additional bright stimulus during C-T+ and C+T+ trials, see Fig. 1a), spatial cues significantly modulated arousal during prestimulus fixation leading up to target presentation, irrespective of temporal cues (FDR-corrected p < 0.05; Fig. 1g), most likely reflecting the pre-stimulus difference in target uncertainty. Pupil diameter at target onset (across all conditions) was systematically related to respiration phase at target onset (Rayleigh test of individual circular means: z(29) = 8.51, p < 0.001), with greater diameter indicating higher arousal during inspiration. In addition to the reported overall effect, this modulation was consistently observed within each condition as well. Moreover, larger pupil diameter changes as a response to cued (vs uncued) trials coincided with lower average stimulus contrast on the individual level (ρ(28) = −0.38, p = 0.019), indicating a systematic influence of predictability-dependent changes in arousal states on behavioural performance (Fig. 1h). As a control analysis, we quantified saccadic behaviour during the prestimulus time window across the four conditions. As expected, the number of saccades overall was low (with the group-level median ranging from M = 4 to M = 6 saccades over 180 trials across conditions) and a Kruskall–Wallis test showed no group-level differences in the number of saccades across conditions (p = 0.92).

Distinct oscillatory signatures of behaviourally relevant stimulus characteristics
Moving from arousal neuromodulation to excitability states, we next sought to confirm a well-established neural signature of perceptual performance, namely alpha suppression effects over parieto-occipital sensors for hits vs misses (i.e., perceived vs non-perceived trials; Fig. 2a). Replicating previous work from our lab4, we report long-lasting alpha suppression for perceived trials (Fig. 2b), starting as early as the cue onset at -1600 ms prestimulus. Critically, these alpha suppression effects were related to changes in pupil-linked arousal in two ways: Pooling all conditions, we first observed a consistent increase in pupil size (indicating higher arousal) for hits vs misses (FDR-corrected p < 0.05; Fig. 2a, upper inset). Second, the greater this diameter increase for hits vs misses on the individual level, the stronger the alpha suppression we observed (ρ(28) = -0.42, p = 0.011; Fig. 2a, lower inset). Alpha suppression effects temporally preceded changes in pupil diameter (Fig. 2c).

Complementing our behavioural findings outlined above, we next characterised the effects of spatial and temporal cueing on the neural level, particularly changes in prestimulus alpha and beta power. We hypothesised distinct effects of spatial cues on perceptual processing and of temporal cues on predictive timing during motor preparation, respectively. A conjunction analysis of spatially cued vs uncued trials revealed an occipital distribution of significant alpha power decrease around −1400 ms to −600 ms prior to stimulus onset (Fig. 2d). Conversely, beta suppression effects for timed vs untimed stimuli were observed later (around −1000 ms up to stimulus onset) and localised over central sensors (Fig. 2e). Descriptive statistics for cluster peaks are provided in Fig. 2f.

Respiration-driven spectral changes within the RMBO network mediate perception
Going beyond sensor-level analyses, we had previously identified the RMBO network as a set of nodes in which neural oscillations were systematically modulated by the respiratory rhythm2. Here, we capitalised on these anatomical priors to investigate if and how the effect of stimulus predictability on respiration and behavioural performance would be mediated through changes in the RMBO network. To this end, we conducted source-level analyses using an LCMV beamformer. Using the parcellation atlas by Glasser and colleagues32, we extracted source-level time series for specific cortical regions of interest (ROIs) taken from the RMBO network. Each of these ROIs consisted of 2-6 parcels and comprised bilateral insula (INS), anterior and posterior cingulate cortex (ACC, PCC), supplementary motor area (SMA), and temporoparietal junction (TPJ). In addition to these nodes of the RMBO network, we included primary and secondary visual cortex (V1/2) as well as primary motor cortex (M1) as domain-specific upstream nodes involved in visual processing and motor preparation, respectively (Fig. 3a). After correcting for head motion artefacts, we first computed prestimulus power spectra for each ROI (DPSS multitaper, frequency range 1-40 Hz, [−1600 0] ms) to quantify oscillatory activity leading up to target onset. In order to remove the aperiodic component from the frequency spectra of each trial, we considered the first-order derivative of each time series, which can be used as a time-domain filter of the aperiodic component32. Visual inspection of the resulting power spectra showed that this spectral whitening did in fact remove the 1/f dependency from our data (Fig. 3c; also see Supplementary Fig. S1 for replication of the results without spectral whitening). Using a moving-window approach, we computed power spectra for 30 respiration-phase bins per ROI and per participant. Based on these individual matrices of ROI x frequency x phase bin, we performed a circular-linear correlation33 of frequency-specific power with the corresponding phase angle. Across all trials, significant group-level correlations between oscillatory power and respiration phase were found for almost the entire frequency spectrum across ROIs (Fig. 3b; FDR-corrected p < 0.05). These circular-linear correlations quantify systematic covariation of oscillatory power across the respiratory phase. This relationship was particularly evident in alpha and beta frequency bands (Fig. 3c, top panels), in that e.g. alpha power within the visual cortex was systematically lowered during inspiration (compared to expiratory phase bins). This link demonstrates the fundamental modulatory influence of the respiratory rhythm on oscillatory neural network activity prior to the onset of near-threshold stimuli.

To better understand these baseline changes in oscillatory activity within the RMBO network, we next investigated target predictability as a potential modulating factor. To this end, we statistically compared prestimulus power spectra for maximally predictable (C+T+) and unpredictable targets (C-T-), irrespective of respiration phase at target onset and behavioural performance. We report a wide-range, consistent power decrease for predictable targets in almost all cortical parcels corresponding to our regions of interest (Fig. 3d, cluster-corrected p < 0.05). Although significant power modulations were observed across theta, alpha, and beta frequency bands, most ROIs showed the strongest suppression effects in beta frequencies between 18-22 Hz.

In a final step, we aimed to relate respiration phase-locked changes in neural oscillations to individual differences in perceptual sensitivity. For this purpose, our approach was twofold: First, we sought to confirm the overall effect of the respiratory phase on prestimulus spectral power within our network of interest. To this end, we performed a linear regression per participant, frequency, and ROI, predicting frequency-specific power from respiratory phase (as a linear combination of sine and cosine) at target onset. This first-level analysis yielded regression coefficients per participant, frequency, and ROI. For the perceptual component, we refitted the psychometric function (relating performance to target contrast) for each participant across all trials, irrespective of respiratory phase, and extracted the function’s threshold as a measure of perceptual sensitivity (see Figs. 1c and 3e). Linking respiratory modulations of brain oscillations (RMBOs2) to perception, we then correlated individual beta coefficient spectra from our regression analysis with individual thresholds extracted from the psychometric function. On the group level, we observed clusters of significant correlations between RMBOs and perceptual sensitivity within beta-band frequencies located in ACC, SMA, and V1/2 (Fig. 3f; cluster-corrected p < 0.05). In other words, stronger respiratory modulation of beta activity in these ROIs coincided with greater differences in perceptual sensitivity. The directionality of this effect becomes evident when we consider the respiration phase-locked beta modulations shown in Fig. 3c: Prestimulus beta power was lowest (i.e., suppressed most strongly) when targets were presented during mid- to late inspiration, which is precisely the respiratory phase for which we observed significantly increased perceptual sensitivity on the group level (Fig. 1c). Collectively, these findings demonstrate that respiration-related changes in neural oscillations immediately affect perception: Not only did we show suppressed alpha power over occipital sensors for hit vs miss trials (Fig. 2a-c), but then revealed a significant correlation between respiration phase and alpha power in V1/2 with lower alpha power during inspiration (compared to expiration, Fig. 3b, c). In a dedicated correlational analysis, we further established that perceptual processing was mediated by the link between respiration and beta-band activity within medial central (ACC, SMA) and visual cortices (V1/2), relating respiration-locked beta suppression to increased perceptual sensitivity.

Finally, we asked whether this relationship between respiration-modulated neural oscillations and perceptual sensitivity would be altered by the predictability of upcoming stimuli, in that highly predictable stimuli allow for coordination of brain and body in favor of behavioural facilitation. Hence, we repeated our previous analysis separately for maximally predictable targets preceded by both a spatial and a temporal cue (C+T+) and unpredictable targets which were not preceded by any cue (C-T-). We once again used linear regression to predict prestimulus spectral power from respiration phase at target onset and then correlated differences in the resulting regression coefficient spectra with the respective individual threshold differences (i.e., perceptual sensitivity for C+T+ minus C-T-). We found that the previously reported respiration-brain-behaviour correlation within ACC beta-band activity reliably distinguished between predictable and unpredictable targets (Fig. 3g; cluster-corrected p < 0.05). This interaction with target predictability demonstrates that respiratory involvement in perceptual processing was dynamically adjusted to momentary perceptual demands, as we observed a stronger respiration-brain-behavior relationship when cue information was available prior to target onset, allowing for respiratory adaptation. In sum, our results thus far indicate that the rhythmic act of breathing is systematically coupled with neural oscillations throughout the brain, particularly in the alpha and beta frequency bands. As these oscillations reflect excitation-inhibition dynamics within visual and motor cortices in our visual detection paradigm, respiration is voluntarily adapted to facilitate perceptual processing by temporally aligning sensory sampling with high-excitability states. This respiration-driven brain-body coordination is particularly effective when upcoming sensory information is highly predictable in both location and timing, underscoring the system’s remarkable ability to flexibly adapt to short-term contextual changes. Naturally, our final aim then was to elucidate how this facilitative coupling of brain and body would be mechanistically achieved on the RMBO network level.

Respiration shapes perception through changes in directed network connectivity
From multivariate source-localised time series, we computed power and cross-spectral densities (DPSS multitaper, 0–150 Hz) for our set of RMBO nodes plus V1/2 and M1 (see Methods for details). Based on these frequency representations, pairwise multivariate Granger causality (mGC) was computed for all pairs of ROIs during the prestimulus period (Fig. 4a). By considering the combined influence of multivariate source-level time series, multivariate Granger causality allows for superior forecasts compared to conventional approaches. To quantify the extent to which respiration phase modulated directed functional connectivity within the RMBO network, we once again sorted all trials (collapsed across conditions) according to respiratory phase at target onset and then used a moving window approach to compute respiration phase-resolved mGC spectra (Fig. 4b). In keeping with our earlier approach (see above), we conducted a first-level linear regression predicting mGC per frequency and pairs of nodes from respiratory phase. We then computed the directed asymmetry index (DAI34,35) for the resulting regression weights as a measure of frequency-specific directionality between any two nodes (Fig. 4c). As positive DAI values indicate a stronger A→B connectivity and vice versa, we finally tested the DAI of respiration phase-resolved mGC spectra against zero using cluster-permutation correction. On the group level, this analysis revealed that respiration phase modulated broadband directed connectivity within the RMBO network, particularly involving insula, ACC/PCC, TPJ, and primary as well as supplementary motor areas (Fig. 4d). All pairwise DAI spectra are shown in Supplementary Fig. S2.

Exemplary grand-average spectra of GC regression weights for the connection between insular cortex and TPJ are shown in Fig. 4e along with the resulting asymmetry spectrum (Fig. 4f; see Supplementary Fig. S3 for all pairwise interactions). Similarly to the GC spectra, we observe variable peaks in the delta, alpha, and beta frequency bands, reflecting respiration-modulated interactions between INS and TPJ. With regard to the directionality of this connection, INS-TPJ connectivity was significantly modulated by respiration in frequencies up to 30 Hz (Fig. 4d), in that the dominance of INS→TPJ information flow was systematically higher during expiration (Fig. 4c, red traces) compared to inspiration (blue).

In addition to the overall influence of respiration on directed RMBO network connectivity-i.e., respiration-phase-dependent modulations shared across all experimental conditions-we were particularly interested in whether these connectivity changes would vary with target predictability and whether they were systematically related to perceptual processing. To this end, we repeated the mGC regression analysis separately for maximally predictable targets (C+T+) and unpredictable targets (C-T-). On the group level, we found a significant influence of respiration phase on broadband connectivity for a wide range of pairwise connections within the RMBO network which were markedly similar for predictable and unpredictable targets (Fig. 4g; cluster-corrected p < 0.05). In fact, the sole connection for which we observed a condition X respiration phase interaction on directed connectivity was the link between TPJ and visual cortices (V1/2). As shown in Fig. 4h, connectivity asymmetry between these two nodes in the alpha band (around 9-15 Hz) was differentially related to target predictability: When both temporal and spatial information about an upcoming target was available (C+T+), we observed a stronger respiratory modulation of prestimulus alpha-band information flow from TPJ to V1/2, compared to trials for which no prior information was available (C-T-). In a final analysis, we therefore investigated whether the functional link between respiratory phase, RMBO network connectivity, and perceptual sensitivity depended on the amount of sensory information available. Based on individual differences in respiration-modulated DAI spectra (C+T+ vs C-T-), we conducted a correlation analysis with the respective condition difference in individual perceptual thresholds (see Figs. 1c and 3e). Using non-parametric cluster permutation statistics, we observe a significant circular-linear correlation between alpha-band connectivity in the INS-SMA connection and perceptual sensitivity during C+T+ trials (Fig. 4i; cluster-corrected p < 0.05). This correlation was strongest at around 8 Hz, at which point group-level DAI indicated balanced information flow between the insular and supplementary motor cortex. On the individual level, the difference between INS-SMA (a)symmetry for predictable (vs unpredictable) trials was negatively correlated with the difference in perceptual threshold between the two conditions (Fig. 4j). In other words, for predictable targets only, the increase in alpha-frequency SMA→INS connectivity prior to target presentation was associated with higher perceptual sensitivity in perceiving these targets (i.e., lower threshold; Fig. 4k). Collectively, our analyses of directed RMBO network connectivity revealed that respiration phase significantly modulated broadband connectivity across RMBO nodes, particularly connections involving insula, anterior and posterior cingulate, TPJ, and (supplementary) motor areas. Of these pairwise links, stronger respiratory modulation of low-frequency SMA→INS connectivity coincided with greater behavioural facilitation in perceiving predictable (vs unpredictable) targets, suggesting a specific functional pathway by which respiration adjusts perceptual processing to momentary contextual demands and facilitates behaviour.

Methods
Participants
Thirty right-handed volunteers (15 female, age 25.9 ± 3.3 y [mean ± SD]) participated in the study. Self-reported sex or gender of participants did not play a role in the experimental design of the study. All participants reported having no respiratory or neurological disease and gave written informed consent prior to all experimental procedures. The study was approved by the local ethics committee of the University of Münster (approval ID 2018-068-f-S).

Procedure
Participants were seated upright in a magnetically shielded room while we simultaneously recorded respiration and MEG data. MEG data were acquired using a 275-channel whole-head system (OMEGA 275, VSM Medtech Ltd., Vancouver, Canada) and continuously recorded at a sampling frequency of 600 Hz. To minimise head movement, the participant’s head was stabilised with cotton pads inside the MEG helmet. Data were acquired across six runs with intermediate self-paced breaks. The length of each run was dependent on individual response speed (group-level mean ± SD: 452 ± 28 s). Participants were to breathe automatically through their nose while respiration was recorded as thoracic circumference by means of a respiration belt transducer (BIOPAC Systems, Inc., Goleta, United States) placed around their chest. Participants were continuously monitored via video to ensure they kept their mouths closed and were breathing through their noses.

Task
Participants performed a spatial detection task (3-choice, single interval) in which they were to detect peripheral visual stimuli presented on a black computer screen. Specifically, each trial comprised a fixation period (1600 ms) during which participants were to fixate on the centre of the screen. After this fixation period, a small Gabor patch (0.3 degrees in diameter) was presented for 50 ms in one of two marked circular areas (3.5 degrees in diameter) located 10 degrees to the left or the right side of the fixation cross. Following a delay of 500 ms, a question mark in the centre of the screen prompted participants to give their response: Using a 3-button response box in their right hand, participants reported whether they saw a target on the left side (index finger), on the right side (middle finger), or no target at all (thumb). Once the report was registered, a new trial started (again with a fixation period). Critically, the predictability of target stimuli was manipulated along two dimensions (Fig. 1): During the fixation phase, participants were shown either a neutral fixation cross (uncued trials, C-) or an arrowhead indicating the spatial location of the upcoming target (cued trials, C+). In addition to this spatial cueing, trials could be timed (T+) by a visual countdown surrounding the central fixation point which-in contrast to untimed (T-) trials-allowed participants to temporally predict the onset of the target stimulus. Independent manipulation of cueing and timing resulted in a full 2×2 design as shown in Fig. 1a. Each combination (C+T+, C+T-, C-T+, C-T-) was presented 30 times per experimental run (n=120 trials per run), yielding 180 trials per condition over the entire experiment. Target locations (left, right) were balanced within each condition and experimental run. For each trial, target contrast was adapted by a QUEST staircase64 aimed at individual overall hit rates of about 60%.

Prior to the first run, participants were instructed to keep their eyes on the centre cross at all times and encouraged to report left or right targets even when they were not entirely certain. Participants were told that, in addition to potential left and right targets, there were trials where no target was presented at all. In fact, no such catch trials occurred. Note that, while this was an important and deliberated decision in designing our task, it comes at the cost of certain SDT metrics (e.g., false alarm rate) not being able to be computed. Finally, participants were instructed that the spatial cue indicated the target location “with a certain probability” so that the target could still appear at the opposite location or be omitted altogether. Again, no such trials occurred and the spatial cues were in fact deterministic. Participants were debriefed at the end of the MEG session and reported not to have noticed the deterministic nature of the spatial cues.

Resting state
For each participant, two sessions of resting state data were collected (six minutes each). In one session, participants breathed naturally through their noses while being video-monitored to confirm that their mouths remained closed. During the other session, participants were instructed to breathe through their mouth while a nose clip was applied to block nasal airflow. The order of nasal and oral breathing sessions was counterbalanced across participants.

MRI acquisition and co-registration
For MEG source localisation, we obtained high-resolution structural magnetic resonance imaging (MRI) scans in a 3T Magnetom Prisma scanner (Siemens, Erlangen, Germany). Anatomical images were acquired using a standard Siemens 3D T1-weighted whole-brain MPRAGE imaging sequence (1×1×1 mm voxel size, TR=2,130 ms, TE=3.51 ms, 256 × 256 mm field of view, 192 sagittal slices). MRI measurement was conducted in the supine position to reduce head movements, and gadolinium markers were placed at the nasion as well as left and right distal outer ear canal positions for landmark-based co-registration of MEG and MRI coordinate systems. Co-registration of structural T1 MRIs to the MEG coordinate system was done for each participant by initial identification of three anatomical landmarks (nasion, left and right pre-auricular points) in their individual MRI. Using the implemented segmentation algorithms in Fieldtrip and SPM12, individual head models were constructed from anatomical MRIs. A solution of the forward model was computed using the realistically-shaped single-shell volume conductor model65 with a 5 mm grid defined in the Human Connectome Project (HCP) template brain31 after linear transformation to the individual MRI.

Behavioural analyses
Behavioural data were preprocessed and analysed using Matlab (The Mathworks, Inc., Natick, United States). To account for the initiation of the QUEST procedure, the first 10 trials of each run were discarded. Trials were classified into hits (i.e., detected targets) and misses (undetected targets). Individual hit rates (HR) were computed as n_hits / (n_hits + n_misses). For respiration phase-locked behavioural analyses, we used the respiration phase vectors described above to obtain the respiration phase angle corresponding to fixation onset and target onset of each trial.

Respiration phase-dependent fitting of the psychometric function
To quantify respiration phase-locked changes in perceptual accuracy, we employed a robust Bayesian inference analysis for the psychometric function. For each participant, we first fitted the psychometric function (with a cumulative Gaussian distribution as the sigmoid) to all target trials using a three-alternative forced choice model from the Psignifit toolbox66 for Matlab. We then used the identified threshold and slope from the overall fit as priors for an iterative refitting of the psychometric function on subsets of trials obtained from a moving window across respiratory phase: Moving along the respiration cycle in increments of Δω = π/30, we fitted the psychometric function to the trials presented at a respiration angle of ω ± π/10. This way, we extracted respiration phase-dependent threshold estimates for each participant (Fig. 3e) which were subsequently z-scored. Finally, the normalised threshold variations were averaged across participants to obtain the grand average phase-dependent threshold modulation at the population level.

MEG preprocessing and segmentation
All MEG, respiratory, and pupil data preprocessing was done in Fieldtrip67 for Matlab. As a first step in preprocessing, we adjusted the synthetic gradiometer order to third order to improve MEG noise balancing (ft_denoise_synthetic). Power line artefacts were removed using a discrete Fourier transform (DFT) filter on the line frequency of 50 Hz plus harmonics (including spectrum interpolation; dftfilter argument in ft_preprocessing). Next, bad channels were identified semi-automatically by means of amplitude and kurtosis criteria and subsequently repaired with cubic spline interpolation. For kurtosis, we applied a general threshold of ω > 10 and outliers in amplitude were judged relative to all other sensors by an experienced analyst (DSK). We then used independent component analysis (ICA) to capture eye blinks and cardiac artefacts (ft_componentanalysis with 20 extracted components). On average, artefacts were manually identified in 1.50 ± 0.42 components (M ± SD) per participant and removed from the data. Finally, continuous MEG data were segmented to a [−1.85 0.75] s interval around target onset and resampled to 300 Hz.

Respiration preprocessing and phase extraction
Raw respiration time courses were resampled to 300 Hz and z-scored to yield normalised respiration traces for each participant and each experimental run. To account for occasional, unusually high-amplitude breaths (e.g., sighs), segments that exceeded a normalised amplitude of z = ± 2.5 were linearly interpolated (i.e., clipped). This way, such artefacts did not bias the peak detection algorithm we subsequently applied to identify time points of peak inspiration (peaks) and peak expiration (troughs; findpeaks function in Matlab with minimal peak prominence set to 1). In addition, individual respiratory time series were visually inspected for noticeable breath holds, but none were detected. Continuous respiratory phase angles were linearly interpolated from trough to peak (−π to 0) and peak to trough (0 to π) in order to yield respiration cycles centered around peak inspiration (i.e., phase 0). Individual breathing rates were extracted as the mean distance between inspiratory peaks as defined by the peak detection algorithm described above.

Pupil preprocessing
Pupil data were preprocessed following procedures of previous work18. In short, area traces were converted to pupil diameter to linearize our measure of pupil size. Blinks were identified and visually validated by an automatic procedure (available from https://github.com/anne-urai/pupil_preprocessing_tutorial) and linearly interpolated. Blink-interpolated pupil time series were subjected to the procedure again, using a relaxed criterion of z = 6 SD to capture remaining artefacts. Next, canonical responses to blinks were estimated and removed from the pupil time series. To that end, pupil time series were band-pass filtered (pass band: 0.01–10 Hz, second-order Butterworth, forward-reverse two-pass). For subsequent analyses, pupil diameter time series were converted to z scores using a robust procedure (MATLAB function ‘normalise’ with options set to ‘zscore’ and ‘robust’).

Extraction of MEG time series in source space
For source-space MEG analyses, we extracted neural time series from a total of n = 230 cortical parcels from the HCP atlas31. Source reconstruction was performed using the linearly constrained minimum variance beamformer approach (LCMV68), where the lambda regularisation parameter was set to 5%. This approach estimates a spatial filter for each location of the 5-mm grid along the direction yielding maximum power. A single LCMV beamformer68 was used to estimate the parcel-level time series across all trials and conditions. Parcels of interest were selected from the original publication in which we had established the RMBO network2 based on the same atlas.

Head movement correction
In order to rule out head movement as a potential confound in our analyses, we used a GLM-based compensation procedure established by Stolk and colleagues69. This method uses the accurate online head movement tracking that is performed by our acquisition system during MEG recordings. This leads to six continuous signals (temporally aligned with the MEG signal) that represent the x, y, and z coordinates of the head centre (H_x, H_y, H_z) and the three rotation angles (H_ψ, H_θ, H_φ) that together fully describe head movement. We constructed a regression model comprising these six ‘raw’ signals as well as their derivatives and, from these 12 signals, the first-, second-, and third-order non-linear regressors to compute a total of 36 head movement-related regression weights (using a third-order polynomial fit to remove slow drifts). This regression analysis was performed on single-parcel time courses after source reconstruction (see above), removing signal components attributable to head translation or rotation relative to the MEG sensors.

Spectral decomposition and connectivity analysis
We used a non-parametric approach to estimate pairwise multivariate Granger causality between each pair of ROIs. The spectral representation of the source-localised time series was estimated using the fast Fourier transform along with multitapers (using 2-Hz smoothing) on the time domain data (separately for each condition) for the time interval of [−1600 0] ms relative to target onset. For each pair of ROIs we computed the cross-spectral density matrix and used it to compute multivariate Granger Causality (mGC) using a nonparametric spectral matrix factorisation in a blockwise approach70. For this, we considered the first three principal components of each ROI’s source-localised, trial-based time series. After obtaining the mGC spectra, we computed the directed asymmetry index (DAI34) as follows:

DAI = (mGC(A→B) - mGC(B→A)) / (mGC(A→B) + mGC(B→A))

This procedure was repeated iteratively for a subset of trials corresponding to separate bins of respiration phase angle at the target onset (30 non-overlapping bins covering a full cycle of inspiration and expiration).

Statistical analysis
Group-level significance was evaluated using cluster-based permutation tests implemented via Fieldtrip’s ft_freqstatistics function. Initially, two-tailed t-tests were performed for each data point (representing power, mGC, DAI, or regression coefficient values) within every sensor or ROI; either comparing experimental conditions, or comparing an experimental condition against zero (this was applicable only for the DAI values). Resulting t-values were thresholded at p = 0.05. Spatially (for sensor space) and spectrally adjacent significant data points were grouped into clusters. We defined each cluster’s summary statistic as the sum of absolute t-values and we randomly permuted condition labels 5000 times to generate a null distribution (note that for each permutation we recomputed initial t-values, redetected clusters, and recalculated cluster-level statistics). Then, original cluster statistics were compared against the permuted null distribution. Clusters exceeding the 97.5th percentile of this distribution (p < 0.05) were deemed statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents work in which the link was tested between respiration, arousal, neural oscillations, connectivity and how together they impact behaviour as measured in a perceptual sensitivity task. The experiment is carefully set up to answer the research questions and all analyses are solid and supported by the underlying hypotheses. The work demonstrates on the behavioural level how respiratory phase and alignment of breathing can change detection thresholds and how these effects are directly linked to changes in pupil size. Furthermore, these results are then extended to show that in both the visual and motor domain, respiration couples to alpha and beta oscillations in a behaviorally meaningful way. Lastly, using Granger causality analyses, the authors show how respiration influences directed information flow within a set of ROIs of the RMBO. The work is well done and the manuscript clearly written, so I have few comments to mostly improve the accessibility of the paper.\n\nOverall, the results section could benefit from explaining the implemented analyses in a way that is more accessible to a broad readership, given that there is no dedicated analysis section in the manuscript. I give some examples below on the first part concerning the behavioural results, but the logic can be extended to the other sections as well:\n- Page 4: please clarify “.. we also included the sine and cosine of the prestimulus respiratory phase”. Respiratory phase at which moment in the task exactly? Why were sin and cos taken used?\n- The description of refitting the psychometric curve of the behavioural data using resp. phase bins (i.e. moving window approach) could do with some rewriting, to make the logic/implementation of the analysis overall more clear.\nIn the methods section:\n- There is no description of the acquisition of resting state data, however RR rates were compared to rest in the analyses. It would be good to describe the acquisition of the resting state data in terms of duration, general instructions and breathing mode.\n- For the MEG preprocessing: were components identified manually or automatically?\n- Was there a specific criterium for bad channel identification? The current description of using amplitude and kurtosis is difficult to replicate based on the text alone.\nTypos:\n- Page 4: ‘peristimulus’", "reviewer_2": "Chalas and colleagues provide valuable new insights into the interplay between brain dynamics, perception, and respiration. Building on their 2021 study, the authors use a near-threshold visual detection task to probe perceptual sensitivity across the respiratory cycle, while recording arousal via pupillometry and brain activity with MEG. The task manipulates stimulus predictability (very nice) with spatial and temporal cues.\n\nMethods are sound and reproducible; the use of robust statistics (e.g., cluster-based permutation tests) is appropriate. Results are reported carefully and target mechanisms rather than correlations. Beyond confirming higher sensitivity during mid- to late inspiration, a clear novelty is shown: directional connectivity indicates increased low-frequency influence from temporoparietal junction (TPJ) to insula prior to target onset is associated with higher perceptual sensitivity. The Discussion situates these findings within an interoceptive predictive-processing account in which greater predictability enables tighter coupling across systems (respiration, beta suppression, pupil dilation). Within this view, TPJ-to-insula influence is interpreted as contextual updating that informs interoceptive control to optimise perceptual sensitivity. Overall, this is a careful and innovative manuscript with a substantive contribution.\n\nI recommend revisions aimed at clarifying scope, improving mechanistic transparency and the framing.\n\nGeneral framing and theory:\nSituate respiration within active sensing taxonomies. Add two sentences in the Introduction to acknowledge prior distinctions between alloactive sensing (sensor reconfiguration; e.g., eye movements, posture, respiration pacing) and homeoactive sensing (energy injection; e.g., palpation, echolocation). Note that respiration is a partly volitional, predominantly alloactive control that can reshape the internal milieu supporting intake. Provide at least one representative citation to this literature and clarify how your task engages alloactive control. See Zweifel, N. O., & Hartmann, M. J. (2020)\n\nTemper generalisation to everyday sensing:\nAdd a brief statement that near-threshold, brief, externally timed Gabor stimuli capture a specific, informative regime, but are not representative of naturalistic, sustained, symbolic, or self-paced sensing. You already emphasise predictability manipulations; explicitly note that effects may be smaller or policy-expressed rather than sensitivity-expressed in ecological tasks. Place this either in Discussion or a short “Limitations and scope” note. In short, it would be nice to directly state that the current experiments represent one portion of natural sensing possibilities in daily experiences; so further researchers/media don't oversell the mechanism.\n\nAcknowledge cardio-respiratory coupling as a complementary pathway:\nAdd some sentences noting that respiration can modulate cardiac timing via respiratory sinus arrhythmia, thereby co-shaping permissive windows for perception, and that concurrent ECG recording would allow testing whether part of the observed effects are mediated by changes in cardiac phase or heart–breath coupling. At present ECG does not appear to be recorded; stating this as a limitation clarifies mechanisms. See Menuet et al., 2025\n\nMotor-related confounds for beta modulation:\nBecause responses are manual each trial and beta power indexes motor readiness, please clarify how you separated sensory-related beta effects from motor preparation.\n\nCriterion vs sensitivity under a 3-choice task without catch trials:\nYou state no catch trials occurred although participants had a “no-target” button. This design prevents standard SDT estimation of false alarms and decision criterion. Please justify this choice, clarify how misses and “no-target” presses were treated, and consider reporting complementary bias-robust metrics. A brief note in Methods and maybe a sentence in Limitations would suffice.\n\nConsistency with mixed respiratory-phase findings:\nYou report inspiratory facilitation. Some studies report exhalation-related benefits depending on modality, task, and sampling constraints. Add two cautious sentences in the Discussion outlining boundary conditions under which inspiration versus exhalation may be optimal, and relate this to predictability and the ability to prolong sampling. This will preempt over-generalisation.\n\nPresentation:\nGiven the many pairwise relations reported (pupil, phase, threshold, alpha, beta), a compact diagram indicating putative facilitatory or inhibitory links and approximate magnitudes would help readers follow the mechanistic story. This is a suggestion, not a requirement.\n\nBreath holds and respiratory phase mapping in circular statistics:\nPlease clarify how respiratory phase was derived and mapped. In many pipelines inhalation and exhalation are each assigned 180°, yet inspiratory and expiratory durations typically differ within and between participants, and brief breath holds are common. This asymmetry can bias phase-resolved effects if time spent in each phase is not represented accurately. Plus, State how holds and pauses were detected and handled (e.g., excluded, merged with exhalation, or treated as a distinct category). Were these even present?\n\nOverall, We offer a favourable evaluation. The study is careful, the methodology is innovative, and the TPJ-to-insula connectivity result adds a novel piece to the respiration–brain–perception puzzle. With the minor clarifications and additions above, the manuscript will provide a stronger bridge between near-threshold laboratory paradigms and broader active-sensing accounts of respiration, brain dynamics and vision.", "reviewer_3": "1. What are the noteworthy results?\nThis study provides compelling evidence that respiration actively shapes human perception rather than serving as a passive, automatic background rhythm. Chalas et al. show that individuals fine-tune their breathing patterns to optimize sensory performance, aligning inhalation phases with moments of heightened cortical excitability and arousal preceding predictable stimulus onset. Corresponding shifts in alpha- and beta-band activity, measured with MEG, indicate that breathing modulates both visual processing and motor readiness to near-threshold visual stimuli. At the network level, directed connectivity from the temporoparietal junction to the insula reveals a key interoceptive pathway through which respiration influences perceptual context. Overall, the work positions breathing as a fundamental element of active sensing in humans and provides a framework for understanding - and ultimately treating - disorders of perception, attention, and interoception.\n\n2. Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nIt adds on the existent knowledge about respiratory-related perceptual modulation by more precisely specifying how respiration modulates neural excitability and arousal to aid performance on a perceptual sensory task. Previous papers (e.g., Perl et al 2019) have shown similar effects but this works takes it a step further.\n\n3. Does the work support the conclusions and claims, or is additional evidence needed?\nIt supports the conclusions and claims but see below.\nAnswers to the questions below is required.\n\n4. Are there any flaws in the data analysis, interpretation and conclusions?\nSee below\nDo these prohibit publication or require revision?\nThey do not prohibit publication but consideration. See below\n\n5. Is the methodology sound? Does the work meet the expected standards in your field?\nYes\n\n6. Is there enough detail provided in the methods for the work to be reproduced?\nYes\n\nRemarks to the authors related to points 3 and 4 above.\nThis study employs a variation of the classic Posner task, a well-established paradigm for probing visuospatial attention. This is a strong choice to demonstrate the role of respiration in perceptual processing, especially through the use of both spatial and temporal cues and well-controlled stimuli (Gabor patches). The fixed 1.6-s interval before target onset was intentionally designed to allow participants to align their respiratory phase with the upcoming stimulus. However, this design also introduces potential limitations.\n\nIf respiratory modulation were such a strong enhancer of perceptual sensitivity, one might expect participants—particularly those with better temporal alignment—to start adjusting their breathing even before cue presentation. Because the 1.6 s interval was constant, predictable, and relatively short in respiratory terms, an optimal strategy could have been to reset breathing as soon as the fixation cross appeared, ensuring that a favorable phase angle recurred 1.6 s later regardless of cue appearance. In this scenario, cues could serve to fine-tune performance rather than drive large respiratory phase shifts, which occur on a slower timescale.\n\nAlthough many conclusions rely on within-subject analyses (e.g., trials with stronger phase alignment showing lower detection thresholds), this fixed-interval predictability remains a concern. A less predictable design—or inclusion of a control condition with jittered pre-target intervals (e.g., 1–6 s)—would have provided a stronger test of the hypothesis. Alternatively, an entrainment paradigm presenting streams of visual stimuli with variable inter-stimulus intervals and target probabilities (e.g., Lakatos et al., 2008) would be well suited to examining respiratory entrainment in an inherently rhythmic active-sensing context. While the use of temporal cues partly addresses this issue, the effect was modest (t = 2.0, p = 0.047) compared with the spatial cue benefit (t = 4.28, p < 0.001), suggesting that participants derived limited advantage from temporal information under the fixed 1.6 s schedule. The result on pre-target pupil modulation (fig 1h) also supports the claim that respiration adjusts in the absence of cues (larger pupil changes in C-T- than C+T-). In addition, the lack of condition X respiration phase interaction on directed RMBO network connectivity (fig 4g) also supports this claim.\n\nRegarding hit rates, figure 1b shows that C+T+ is higher than C-T- and that C-T+ is higher than C-T-. However, C+T- looks higher than C-T- and maybe even higher than C-T+ also. Are these effects significant?\n\nFig 1g:\nWhat are the units in the y-axis (pupil diameter)? Why they start between .2 and .3? If deflections downwards mean pupil constriction, then temporal cues (T+) constrict the pupil during the pre-target period up to target onset. This effect is even larger when temporal cues are presented alone (larger constriction in C-T+ compared to C+T+). Not sure this is clearly explained. Lighting was controlled across conditions discarding luminance changes. Were there fixation differences across conditions (e.g., more saccades to temporal cues in C-T+ than C+T+) or is the effect explained by arousal differences? Why C-T- shows more pre-target pupil constriction than C+T-?\n\nPrevious studies have shown that during spontaneous ventilation (no stimuli) pupil size expands during inhalation and contracts during exhalation. Namely it starts expanding 1-1.5s after inhalation onset. Are the present arousal-related pupil changes correlated to the subsequent inhalation-related pupil changes after target onset? Given that in this study, most subjects align their mid/late inhalations to target onset to improve detection, were there larger inhalation-related pupil changes in resp aligned trials?\n\n“Controlling for lighting changes on the screen, spatial cues significantly modulated arousal during prestimulus fixation leading up to target presentation, irrespective of temporal cues (FDR-corrected p < .05; Fig. 1g).” Does that mean that spatial cues prevent pupil constriction from happening during the pre-target period?\n\n“Pupil diameter at target onset was systematically related to respiration phase at target onset (Rayleigh test of individual circular means: z(29) = 8.51, p < .001), with greater diameter indicating higher arousal during inspiration”. Is the greater pupil constriction in C-T+ compared to C+T+ at pre-target and target onset related to higher arousal due to higher uncertainty about the target?\n\nUsing a mutual information model, the authors showed that near-threshold visual detection can be predicted not only by spatial-temporal cues, but also by the respiratory phase at stimulus onset. Incorporating the sine and cosine of the peristimulus breathing cycle significantly improved single-trial prediction accuracy, demonstrating that both stimulus predictability and ongoing respiratory state jointly shape perceptual outcomes. This analysis robustly replicates and extends prior reports of respiration-linked perceptual modulation.\n\nIt is hard to grasp some of the aspects in Fig 1f. Black dots seem quite widely distributed in the y-axis. Does this mean that preferred resp phase is different in each subject? This interpretation contrast with fig 1c – in which mid inhalation is the ideal timing across all subjects (e.g., lower contrast gabor patches are perceived if presented during mid inhalation) and fig 1e. Maybe showing the raw or averaged respiration waveforms overlaid would be helpful.\n\nThe result that respiratory rate during task is higher than rest could be linked to the fact that it is easier to make phase adjustments in short resp cycles compared to longer ones.\n\nRegarding the analyses of the neural data, I believe the authors used state-of-the-art analyses and the results are strong with impressive correlations between neural activity in the RMBO network, performance and respiratory phase.\n\nGiven the activation of temporal site (TPJ)– did the participants heard their breathing sounds modulating auditory cortex activity? did they wear headphones?\n\nIt is known that hyperventilation can modulate neural excitability. Given that participants breathed faster during the task compared to rest, could this have influence some of their results? Was tidal volume measured or controlled in anyway?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewers\nWe would like to thank all four reviewers for their insightful comments on our manuscript. We\nfirmly believe to have included all their feedback in a revised version which, in doing so, has\nundoubtedly gained transparency and accessibility.\nBelow, we provide a point-by-point response to all reviewers with reviewer comments in grey and\nour responses in blue. Quoted changes to the manuscript are shown in red.\nFor the revised version of the manuscript we included two dedicated control analyses: one\nregarding the saccadic behavior prior to target onset and another regarding respiration modulation\non pupil dilation. Both analyses strengthen our initial observation. Additionally, while revising our\nmanuscript in accordance with the reviewers’ comments, we found a bug in our code, related to\nundocumented functionalities in Matlab, which caused the Granger causality analysis to\ninadvertently account for one SVD component (instead of three) for half of the nodes (use of curly\ninstead of regular brackets in one line). We recomputed all analyses reported in Fig. 4 and have\nrevised the figure as well as the main text accordingly (marked in red). As a result of the new\ncomputations, we increased statistical power and now report a previously non-significant\npredictability condition effect between C+T+ and C-T- trials (see new Fig. 4h below). This new\nfinding allows us to characterize consistent patterns of respiration phase-locked information flow\nwithin the RMBO network as a function of current statistical task context. Thanks to the increased\nsensitivity of the corrected analysis, this pattern unfolds much more clearly than before and\nreveals distinct sensory and motor-related subnetworks related to target predictability and\nperceptual accuracy, respectively.\n\nReviewer #1 (Remarks to the Author):\nThis manuscript presents work in which the link was tested between respiration, arousal, neural\noscillations, connectivity and how together they impact behaviour as measured in a perceptual\nsensitivity task. The experiment is carefully set up to answer the research questions and all\nanalyses are solid and supported by the underlying hypotheses. The work demonstrates on the\nbehavioural level how respiratory phase and alignment of breathing can change detection\nthresholds and how these effects are directly linked to changes in pupil size. Furthermore, these\nresults are then extended to show that in both the visual and motor domain, respiration couples\nto alpha and beta oscillations in a behaviorally meaningful way. Lastly, using Granger causality\nanalyses, the authors show how respiration influences directed information flow within a set\nof ROIs of the RMBO. The work is well done and the manuscript clearly written, so I have few\ncomments to mostly improve the accessibility of the paper.\nWe thank the reviewer for their positive evaluation of our manuscript; point-by-point responses to\nthe reviewer’s remarks are listed below.\nOverall, the results section could benefit from explaining the implemented analyses in a way that\nis more accessible to a broad readership, given that there is no dedicated analysis section in the\nmanuscript. I give some examples below on the first part concerning the behavioural results, but\nthe logic can be extended to the other sections as well:\n- Page 4: please clarify “.. we also included the sine and cosine of the prestimulus respiratory\nphase”. Respiratory phase at which moment in the task exactly? Why were sin and cos taken\nused?\nWe appreciate the reviewer’s suggestion to provide more accessible explanations of our analysis\npipeline and have rephrased the manuscript in several instances to include the proposed\nadditions. While changes in the revised manuscript are listed below, we want to briefly elaborate\non the inclusion on sine and cosine of respiration phase in our analysis: This approach is referred\nto as a circular GLM or harmonic regression and is a commonly used tool for quantifying the\namount of variance (in a GLM) or shared information (in a mutual information analysis) explained\nby circular signals. In short, respiratory phase is made up of a sine and a cosine component, both\nof which are included separately as data vectors in the analysis (see Cremers and Klugkist et al.,\n2018). In a regression analysis, this yields beta weights for sine and cosine components of\nrespiratory phase. Since the interpretation of sine or cosine alone is not meaningful, the vector\nnorm of both beta weights is computed and tested against a surrogate distribution. Changes made\nto the manuscript are as follows:\nIn a second step, we also included sine and cosine of the respiratory phase at stimulus\nonset, which once again significantly improved the prediction of individual performance\n(t(29) = 7.94, p < .001). In sum, these results demonstrate that both stimulus predictability\n(spatial, temporal) and respiration phase - fully described by its sine and cosine components\n- had a significant effect on behavioural performance. (p. 4)\n- The description of refitting the psychometric curve of the behavioural data using resp. phase\nbins (i.e. moving window approach) could do with some rewriting, to make the\nlogic/implementation of the analysis overall more clear.\nThank you, the corresponding paragraph has been rephrased as follows:\nWe exploited single-trial information regarding stimulus contrast and detection performance\nto iteratively refit the psychometric function. In short, the psychometric function expresses\nthe sigmoidal relationship between the intensity of a stimulus and the probability of a\nparticular response. In our analysis, we first fitted the overall psychometric function to all\ntarget trials (irrespective of condition), quantifying detection probability as a function of\nstimulus contrast. The resulting parameters were used as priors for a moving window\napproach in which we iteratively refitted the psychometric function to a subset of trials\npresented at a certain range of respiration phase angles. To this end, respiratory phase at\nthe onset of each target stimulus was extracted and assigned to one of k = 60 phase bins\nwith all other trials presented at a similar respiratory phase (see 4 and Methods for details).\nRefitting the psychometric function for the subsets of trials assigned to each of these\noverlapping phase angle bins, we thus obtained a threshold estimate characterizing\nperceptual performance at that respiration phase. (p. 4)\nIn addition, a dedicated paragraph has been added to the Methods section to describe the refitting\napproach in greater detail (pp. 20-21):\nRespiration phase-dependent fitting of the psychometric function. To quantify\nrespiration phase-locked changes in perceptual accuracy, we employed a robust Bayesian\ninference analysis for the psychometric function. For each participant, we first fitted the\npsychometric function (with a cumulative Gaussian distribution as the sigmoid) to all target\ntrials using a three-alternative forced choice model from the Psignifit toolbox 64 for Matlab.\nWe then used the identified threshold and slope from the overall fit as priors for an iterative\nrefitting of the psychometric function on subsets of trials obtained from a moving window\nacross respiratory phase: Moving along the respiration cycle in increments of Δω = π/30,\nwe fitted the psychometric function to the trials presented at a respiration angle of ω ± π/10.\nThis way, we extracted respiration phase-dependent threshold estimates for each\nparticipant (see Fig. 3e) which were subsequently z-scored. Finally, the normalized\nthreshold variations were averaged across participants to obtain the grand average phase-\ndependent threshold modulation at the population level.\nIn the methods section:\n- There is no description of the acquisition of resting state data, however RR rates were compared\nto rest in the analyses. It would be good to describe the acquisition of the resting state data in\nterms of duration, general instructions and breathing mode.\nWe agree with the reviewer. We added a ‘resting state’ paragraph in the Methods of the revised\nversion of the manuscript. The paragraph now reads as follows (p. 20):\nResting state. For each participant, two sessions of resting state data were collected (six\nminutes each). In one session, participants breathed naturally through their nose while\nbeing video-monitored to confirm that their mouths remained closed. During the other\nsession, participants were instructed to breathe through their mouth while a nose clip was\napplied to block nasal airflow. The order of nasal and oral breathing sessions was\ncounterbalanced across participants.\n- For the MEG preprocessing: were components identified manually or automatically?\nThe components were identified manually. This is now stated in the Methods section of the revised\nmanuscript. The paragraph now reads as follows (p. 21):\nOn average, artefacts were manually identified in 1.50 ± 0.42 components (M ± SD) per\nparticipant and removed from the data.\n- Was there a specific criterium for bad channel identification? The current description of using\namplitude and kurtosis is difficult to replicate based on the text alone.\nBad channels were identified through visual inspection by an expert analyst (DSK). Regarding\namplitude, sensors were only interpolated when they were clear outliers relative to all other MEG\nsensors. For kurtosis, a general criterion of kurtosis > 10 was applied. The paragraph has been\nrephrased and now reads as follows (p. 21):\nNext, bad channels were identified semi-automatically by means of amplitude and kurtosis\ncriteria and subsequently repaired with cubic spline interpolation. For kurtosis, we applied\na general threshold of ω > 10 and outliers in amplitude were judged relative to all other\nsensors by an experienced analyst (DSK). We then used independent component analysis\n(ICA) to capture eye blinks and cardiac artefacts (ft_componentanalysis with 20 extracted\ncomponents). On average, artefacts were manually identified in 1.50 ± 0.42 components (M\n± SD) per participant and removed from the data.\nTypos:\n- Page 4: ‘peristimulus’\nThank you for reading our manuscript with great care - in this instance, ‘peristimulus’ in fact\nreferred to the period during stimulus presentation, but has now been rephrased (see above).\n\nReviewer #2 (Remarks to the Author):\nChalas and colleagues provide valuable new insights into the interplay between brain dynamics,\nperception, and respiration. Building on their 2021 study, the authors use a near-threshold visual\ndetection task to probe perceptual sensitivity across the respiratory cycle, while recording arousal\nvia pupillometry and brain activity with MEG. The task manipulates stimulus predictability (very\nnice) with spatial and temporal cues.\nMethods are sound and reproducible; the use of robust statistics (e.g., cluster-based permutation\ntests) is appropriate. Results are reported carefully and target mechanisms rather than\ncorrelations. Beyond confirming higher sensitivity during mid- to late inspiration, a clear novelty is\nshown: directional connectivity indicates increased low-frequency influence from temporoparietal\njunction (TPJ) to insula prior to target onset is associated with higher perceptual sensitivity. The\nDiscussion situates these findings within an interoceptive predictive-processing account in which\ngreater predictability enables tighter coupling across systems (respiration, beta suppression, pupil\ndilation). Within this view, TPJ-to-insula influence is interpreted as contextual updating that\ninforms interoceptive control to optimise perceptual sensitivity. Overall, this is a careful and\ninnovative manuscript with a substantive contribution.\nThank you, we appreciate the reviewer’s favourable evaluation of our manuscript.\nI recommend revisions aimed at clarifying scope, improving mechanistic transparency and the\nframing.\nGeneral framing and theory:\nSituate respiration within active sensing taxonomies. Add two sentences in the Introduction to\nacknowledge prior distinctions between alloactive sensing (sensor reconfiguration; e.g., eye\nmovements, posture, respiration pacing) and homeoactive sensing (energy injection; e.g.,\npalpation, echolocation). Note that respiration is a partly volitional, predominantly alloactive\ncontrol that can reshape the internal milieu supporting intake. Provide at least one representative\ncitation to this literature and clarify how your task engages alloactive control. See Zweifel, N. O.,\n& Hartmann, M. J. (2020)\nWe appreciate the reviewer’s remarks regarding alloactive vs homeoactive sensing. As\nsuggested, we have rephrased the Introduction which now reads as follows (p. 3):\nAt present, converging evidence from different lines of research strongly suggests\nrespiratory involvement in active sensing, including adaptation of respiratory behaviour to\ntask timing 28, modulation of excitability and arousal states 4, and behavioural facilitation 29.\nIn the animal literature, active sensing has been suggested to comprise both a homeoactive\nand an alloactive component 30. In this framework, alloactive sensing postulates the use of\nmechanical energy to change parameters of the sensory apparatus, which includes\nvoluntary changes of the respiratory rhythm based on internal forward models of sensory\npredictions. What has so far been critically missing, particularly in humans, is a direct test\nof previous correlational findings in a dedicated predictive processing paradigm in order to\nanswer fundamental open research questions: To what extent does respiration orchestrate\nexcitability and arousal states across different contexts of stimulus predictability? What are\nthe behavioural benefits of this respiration-brain coupling? And finally, how is the facilitative\ncoupling of brain and body mechanistically achieved on the neural network level?\nAn additional reference has been added to the Discussion section (p. 15):\nLending from active sensing accounts proposed in the animal literature 8, these accounts\nposit that multiple streams of sensory information - e.g., internal and external signals - are\ncoordinated in a way that optimizes their integration and propagation 35. Since respiration is\nunder voluntary control, it is a prime example of an internal signal by which the system can\nexert alloactive control over sensory inputs, e.g. by temporal alignment through dynamic\nchanges in the pace of the respiratory rhythm we observed in the present study. This\npredictive timing has been shown to maximise the signal-to-noise balance between\ninteroceptive and exteroceptive processing 36 and synchronise sensory sampling with\nfavourable brain states of excitability 4,22 and arousal 18.\nTemper generalisation to everyday sensing:\nAdd a brief statement that near-threshold, brief, externally timed Gabor stimuli capture a specific,\ninformative regime, but are not representative of naturalistic, sustained, symbolic, or self-paced\nsensing. You already emphasise predictability manipulations; explicitly note that effects may be\nsmaller or policy-expressed rather than sensitivity-expressed in ecological tasks. Place this either\nin Discussion or a short “Limitations and scope” note. In short, it would be nice to directly state\nthat the current experiments represent one portion of natural sensing possibilities in daily\nexperiences; so further researchers/media don't oversell the mechanism.\nThis comment is particularly appreciated, as brain-body research continues to be overinterpreted\nin public discussions and ‘pop science’ or medial contexts. As suggested by the reviewer, and to\nprevent overselling of our mechanisms, we have added a brief ‘Limitations and scope’ section at\nthe end of the Discussion (pp. 18-19):\nSince brain-body neuroscience research (and the associated field of interoception)\ncontinues to be of great interest for public and medial discourse, it is important to state\nlimitations regarding the interpretability and ‘everyday’ applicability of our findings. In order\nto test our hypotheses, we used a great amount of experimental manipulation and very\nspecific near-threshold stimuli. Overall, this task context constitutes a highly controlled\nregime which does not immediately translate to the experience of daily, natural sensing. We\nhave recently argued the importance of fundamental research and mechanistic insight\nbefore applications or interventions can be derived from singular findings 60, and the very\nsame cautionary note applies here as well.\nAcknowledge cardio-respiratory coupling as a complementary pathway:\nAdd some sentences noting that respiration can modulate cardiac timing via respiratory sinus\narrhythmia, thereby co-shaping permissive windows for perception, and that concurrent ECG\nrecording would allow testing whether part of the observed effects are mediated by changes in\ncardiac phase or heart–breath coupling. At present ECG does not appear to be recorded; stating\nthis as a limitation clarifies mechanisms. See Menuet et al., 2025\nThank you for raising the point of complementary or cross-modal brain-body axes, which is the\nmain focus of an ongoing ERC project in our lab. Respiratory heart-rate variability (to comply with\nthe terminology of Menuet and colleagues) is primarily driven by rhythmic changes in\nparasympathetic activity which inhibits cardiac signals and is strongest during expiration, resulting\nin a decrease in heart rate. Consequently, absolute heart rate changes between inspiration and\nexpiration can again be voluntarily controlled through changes in the frequency (and, relatedly,\ndepth) of breathing (Sroufe, 1971). The present key findings of respiratory adaptation facilitating\nperception strongly suggest that HRV- or cardiac phase-related modulations reported by\ncolleagues in the field are at least partially explained by a respiratory influence. It is entirely\nunclear to what extent interactions between cross-frequency rhythmicities from different organ\nsystems modulate neural activity and whether the single-modality contributions are additive or\nmore complex in nature.\nWe acknowledge the reviewer’s suggestion and have added cardiorespiratory interactions to the\nnewly introduced Limitations section, which now reads as follows (pp. 18):\nFurther research is needed in order to unravel the complexities of the brain-body axis,\nparticularly with regard to interactions across organ systems. Respiratory and cardiac\ndynamics, for example, are tightly coupled through respiratory heart rate variability 61.\nHowever, this link by no means excludes the possibility of complementary pathways,\nparticularly because recent findings point towards independent modulations of corticospinal\nexcitability by respiratory and cardiac rhythms 62.\nMotor-related confounds for beta modulation:\nBecause responses are manual each trial and beta power indexes motor readiness, please clarify\nhow you separated sensory-related beta effects from motor preparation.\nWithin a predictive processing framework, there is no need for a strict separation of sensory and\nmotor-related components of respiration-related beta activity. Previous work from our lab and\nothers has demonstrated respiration phase-locked modulation of beta activity in motor cortices\n(Kluger et al., 2021; Kluger et al., 2023) and of motor function (Kluger & Gross, 2021; Park et al.,\n2020). Interestingly, this link is also present in speech perception (Chalas et al., 2022) and in\nmotor imagery, i.e. contexts in which no action has to be prepared because it is never executed\n(Park et al., 2022). Taken together, these findings make a strong argument for a more general\nrole of beta power in the motor system. As for a potential confound, even if one would clearly\ndistinguish motor readiness and sensory processing, the motor preparation component would be\ncancelled out in our statistical contrasts precisely because it is common across conditions.\nCriterion vs sensitivity under a 3-choice task without catch trials:\nYou state no catch trials occurred although participants had a “no-target” button. This design\nprevents standard SDT estimation of false alarms and decision criterion. Please justify this choice,\nclarify how misses and “no-target” presses were treated, and consider reporting complementary\nbias-robust metrics. A brief note in Methods and maybe a sentence in Limitations would suffice.\nThe reviewer is correct in stating that false alarms and criterion cannot be computed in the\nabsence of catch trials, which is why we refrain from doing so in the present manuscript. As a\ndevelopment from our initial eLife paper on a similar task (without the predicability manipulation),\ncatch trials were removed in order to increase statistical power through a 50% higher trial count\n(720 trials instead of 480). Precisely because there were no non-target trials, the ‘no target’\nresponse was counted as a miss. The Methods section as well as the newly added Limitations\nsection have been amended as follows:\nParticipants were told that, in addition to potential left and right targets, there were trials\nwhere no target was presented at all. In fact, no such catch trials occurred. Note that, while\nthis was an important and deliberate decision in designing our task, it comes at the cost of\ncertain SDT metrics (e.g., false alarm rate) not being able to be computed. (p. 20)\nMethodologically, further variations of perceptual tasks could explore the present effects not\nonly across sensory domains (e.g. in audition), but also with variable timings during the\nprestimulus fixation period for more precise readouts of individual respiratory alignment.\nMoreover, the benefit of catch trials (i.e. no stimuli presented) could be exploited to compute\nestablished signal detection theory measures like false alarm rate and decision criterion. (p.\n19)\nConsistency with mixed respiratory-phase findings:\nYou report inspiratory facilitation. Some studies report exhalation-related benefits depending on\nmodality, task, and sampling constraints. Add two cautious sentences in the Discussion outlining\nboundary conditions under which inspiration versus exhalation may be optimal, and relate this to\npredictability and the ability to prolong sampling. This will preempt over-generalisation.\nThis is closely related to the above comment regarding overgeneralisation (and equally\nappreciated); the Discussion has been amended and now reads as follows (p. 15):\nAs for brain-body involvement, the respiratory cycle has been shown to modulate perceptual\ndecision thresholds, particularly during inhalation 2,9. Note, however, that some studies\nreport expiratory facilitation of behaviour 6,40, suggesting that the distinction between\ninspiration- and expiration-locked modulation may depend on whether the events of interest\nprimarily concern sensory perception or an immediate behavioural response. In the present\nstudy, respiration-modulated changes in alpha activity temporally preceded shifts in pupil\ndilation, indicating that arousal neuromodulation is primed in neural circuits and, as a result,\naffects perceptual processing 2.\nPresentation:\nGiven the many pairwise relations reported (pupil, phase, threshold, alpha, beta), a compact\ndiagram indicating putative facilitatory or inhibitory links and approximate magnitudes would help\nreaders follow the mechanistic story. This is a suggestion, not a requirement.\nWe thank the reviewer for proposing a way to help make the admittedly rich pattern of findings\nmore accessible for readers. After careful consideration, we respectfully refrain from implementing\na ‘mechanistic diagram’, primarily due to the risk of oversimplification and/or overinterpretation of\nour results. The reviewer raised this point in one of their earlier comments and we wholeheartedly\nagree: No single study can provide sufficient mechanistic insight to explain the complexities at\nthe systems neuroscience level, and we fear that a necessarily oversimplified diagram would\nsuggest exactly the opposite. We did aim to structure the ‘story’ behind our findings more clearly\nin our revision, both regarding their reporting in the main text and the figures and we explicitly\nwelcome the reviewer’s feedback in this regard.\nBreath holds and respiratory phase mapping in circular statistics:\nPlease clarify how respiratory phase was derived and mapped. In many pipelines inhalation and\nexhalation are each assigned 180°, yet inspiratory and expiratory durations typically differ within\nand between participants, and brief breath holds are common. This asymmetry can bias phase-\nresolved effects if time spent in each phase is not represented accurately. Plus, State how holds\nand pauses were detected and handled (e.g., excluded, merged with exhalation, or treated as a\ndistinct category). Were these even present?\nThank you for raising the important methodological issue of respiratory phase extraction. As\ndescribed in the Methods section and in keeping with our previous work (Kluger & Gross, 2021;\nKluger et al., 2021; Kluger et al., 2023; Kluger et al., 2024; Kluger et al., 2025; Saltafossi et al.,\n2025), respiration phase was extracted using a two-point interpolation approach, meaning that\npeaks and troughs were detected in the respiratory time series and phase was interpolated from\npeak to trough and trough to peak, respectively. Although, as noted by the reviewer, this results\nin an equal distribution of respiratory wave length between inspiration and expiration, this\napproach is perfectly capable of accounting for intraindividual variation in inspiratory/expiratory\ndurations. In fact, we are currently preparing a tutorial paper on robust statistics for respiratory\ndata in which we compare different methods for extracting respiratory phase and point out the\nadvantages of two-point interpolation compared to the widely used Hilbert transform. To the\nreviewer’s point, during respiratory phase extraction, the analyst has to decide between two\nimperfect scenarios: They can either divide phase evenly between inspiration and expiration\ndespite knowing that - in units of time - expiration is longer than inspiration. This results in a slight\nmismatch between the time and phase domains, but keeps an even distribution of experimental\nevents within both inspiration and expiration. Alternatively, the analyst can assign more than 180°\nto the (slightly longer) expiratory phase. This approach more accurately represents the time-\nphase relationship but does not allow an intuitive interpretation of respiratory phase as\nrepresenting physiological processes (e.g. 180° no longer corresponds to the functionally\nmeaningful change from active inspiration to the passive release of air from the lungs during\nexpiration). Moreover, during the critical step of sorting events of interest into respiratory phase\nbins, the latter option results in an imbalance between inspiration and expiration, with fewer trials\nbeing assigned to inspiratory phase bins (compared to expiratory ones). While this does more\naccurately represent the slightly uneven durations of both respiratory states, it compromises the\ninterpretability of phase-locked neural or behavioural effects. Therefore, in our research, we have\nlong decided on the former approach - knowing that it does not accurately represent time, but\nphase.\nAs for the reviewer’s final comment regarding breath holds, all respiratory time series were visually\ninspected for breath holds and none were detected. This has now been added to the Methods\nsection (p. 21):\nTo account for occasional, unusually high-amplitude breaths (e.g., sighs), segments that\nexceeded a normalised amplitude of z = ± 2.5 were linearly interpolated (i.e., clipped). This\nway, such artefacts did not bias the peak detection algorithm we subsequently applied to\nidentify time points of peak inspiration (peaks) and peak expiration (troughs; findpeaks\nfunction in Matlab with minimal peak prominence set to 1). In addition, individual respiratory\ntime series were visually inspected for noticeable breath holds, but none were detected.\nOverall, We offer a favourable evaluation. The study is careful, the methodology is innovative,\nand the TPJ-to-insula connectivity result adds a novel piece to the respiration–brain–perception\npuzzle. With the minor clarifications and additions above, the manuscript will provide a stronger\nbridge between near-threshold laboratory paradigms and broader active-sensing accounts of\nrespiration, brain dynamics and vision.\nOnce again, the reviewer’s positive assessment and productive comments are highly appreciated.\n\nReviewer #3 (Remarks to the Author):\n1. What are the noteworthy results?\nThis study provides compelling evidence that respiration actively shapes human perception rather\nthan serving as a passive, automatic background rhythm. Chalas et al. show that individuals fine-\ntune their breathing patterns to optimize sensory performance, aligning inhalation phases with\nmoments of heightened cortical excitability and arousal preceding predictable stimulus onset.\nCorresponding shifts in alpha- and beta-band activity, measured with MEG, indicate that breathing\nmodulates both visual processing and motor readiness to near-threshold visual stimuli. At the\nnetwork level, directed connectivity from the temporoparietal junction to the insula reveals a key\ninteroceptive pathway through which respiration influences perceptual context. Overall, the work\npositions breathing as a fundamental element of active sensing in humans and provides a\nframework for understanding - and ultimately treating - disorders of perception, attention, and\ninteroception.\n2. Will the work be of significance to the field and related fields? How does it compare to the\nestablished literature? If the work is not original, please provide relevant references.\nIt adds on the existent knowledge about respiratory-related perceptual modulation by more\nprecisely specifying how respiration modulates neural excitability and arousal to aid performance\non a perceptual sensory task. Previous papers (e.g., Perl et al 2019) have shown similar effects\nbut this works takes it a step further.\n3. Does the work support the conclusions and claims, or is additional evidence needed?\nIt supports the conclusions and claims but see below.\nAnswers to the questions below is required.\n4. Are there any flaws in the data analysis, interpretation and conclusions?\nSee below\nDo these prohibit publication or require revision?\nThey do not prohibit publication but consideration. See below\n5. Is the methodology sound? Does the work meet the expected standards in your field?\nYes\n6. Is there enough detail provided in the methods for the work to be reproduced?\nYes\nWe thank the reviewer for their highly positive overall assessment of our manuscript.\nRemarks to the authors related to points 3 and 4 above.\nThis study employs a variation of the classic Posner task, a well-established paradigm for probing\nvisuospatial attention. This is a strong choice to demonstrate the role of respiration in perceptual\nprocessing, especially through the use of both spatial and temporal cues and well-controlled\nstimuli (Gabor patches). The fixed 1.6-s interval before target onset was intentionally designed to\nallow participants to align their respiratory phase with the upcoming stimulus. However, this\ndesign also introduces potential limitations.\nIf respiratory modulation were such a strong enhancer of perceptual sensitivity, one might expect\nparticipants—particularly those with better temporal alignment—to start adjusting their breathing\neven before cue presentation. Because the 1.6 s interval was constant, predictable, and relatively\nshort in respiratory terms, an optimal strategy could have been to reset breathing as soon as the\nfixation cross appeared, ensuring that a favorable phase angle recurred 1.6 s later regardless of\ncue appearance. In this scenario, cues could serve to fine-tune performance rather than drive\nlarge respiratory phase shifts, which occur on a slower timescale.\nAlthough many conclusions rely on within-subject analyses (e.g., trials with stronger phase alignment showing lower detection thresholds), this fixed-interval predictability remains a concern.\nA less predictable design—or inclusion of a control condition with jittered pre-target intervals (e.g.,\n1–6 s)—would have provided a stronger test of the hypothesis. Alternatively, an entrainment\nparadigm presenting streams of visual stimuli with variable inter-stimulus intervals and target\nprobabilities (e.g., Lakatos et al., 2008) would be well suited to examining respiratory entrainment\nin an inherently rhythmic active-sensing context. While the use of temporal cues partly addresses\nthis issue, the effect was modest (t = 2.0, p = 0.047) compared with the spatial cue benefit (t =\n4.28, p < 0.001), suggesting that participants derived limited advantage from temporal information\nunder the fixed 1.6 s schedule. The result on pre-target pupil modulation (fig 1h) also supports\nthe claim that respiration adjusts in the absence of cues (larger pupil changes in C-T- than C+T-\n). In addition, the lack of condition X respiration phase interaction on directed RMBO network\nconnectivity (fig 4g) also supports this claim.\nWe truly appreciate the reviewer’s in-depth comments regarding the implications and limitations\nof our experimental design. We fully agree with their suggestion that a variable prestimulus\ninterval will provide key insights in the interaction between the use of spatial and temporal\ninformation, we would briefly like to elaborate why the task had been purposefully designed in its\npresent form: Our main goal was to provide (much-needed) clear evidence in favour of or against\nrespiratory adaptation in a task with variable levels of predictability, i.e. to answer the rather\ngeneral question ‘Does the amount of available spatial temporal information overall modulate the\nrespiration-brain connection?’. To answer this question, we believe that the fixed-interval design\nwas the best choice - if there indeed is respiratory adaptation in response to different levels of\ninformation, this provides the easiest context for participants to adjust their breathing. As the\nreviewer correctly points out, however, our pattern of results suggests that the two domains of\ninformation are not used equally, and that the behavioural consequences suggest a differential\nprocessing of spatial vs temporal information. This is why we not only replicated the study with\nlateralized auditory stimuli (data collection ongoing) to investigate how our findings generalise\nacross sensory modalities, but we are also currently preparing a follow-up study with variable\nprestimulus time windows to better understand at which point of the task timeline respiratory\nadaptation is initiated. This includes blockwise within-subject manipulation of variable vs fixed\nintervals for both the pre-cue and pre-stimulus interval. We hope that these upcoming\ninvestigations will shed further light on how the respiration-brain-behaviour link dynamically\ninteracts with changing sensory contexts.\nAll that being said, we acknowledge the reviewer’s well-founded comments and have amended\nthe manuscript with a Limitations section to reflect these considerations. The corresponding\nparagraph reads as follows (p. 19):\nMethodologically, further variations of perceptual tasks could explore the present effects not\nonly across sensory domains (e.g. in audition), but also with variable timings during the\nprestimulus fixation period for more precise readouts of individual respiratory alignment.\nMoreover, the benefit of catch trials (i.e. no stimuli presented) could be exploited to compute\nestablished signal detection theory measures like false alarm rate and decision criterion.\nRegarding hit rates, figure 1b shows that C+T+ is higher than C-T- and that C-T+ is higher than\nC-T-. However, C+T- looks higher than C-T- and maybe even higher than C-T+ also. Are these\neffects significant?\nThank you for spotting this inconsistency: While (in line with the reviewer’s comment) the figure\ncaption correctly stated a higher hit rate for C+T- compared to C-T-, the significance marker was\nmisplaced in the figure itself. The figure now accurately indicates higher hit rates for C+T+ and\nC+T- compared to C-T-. Since C-T- was used as the reference condition, our report was restricted\nto comparisons involving C-T-. The only other significant difference in hit rate was found for C+T+\nvs C-T+, which has now been added for the sake of completeness.\nFig. 1. [...] b, Pairwise comparisons with Tukey-Cramer correction revealed significantly higher hit rates for C+T+ (p <\n.001) and C+T- (p = .01) compared to C-T-. In addition, we observed significantly higher hit rates for C+T+ compared\nto C+T- (p = .01). [...]\nFig 1g:\nWhat are the units in the y-axis (pupil diameter)? Why they start between .2 and .3? If deflections\ndownwards mean pupil constriction, then temporal cues (T+) constrict the pupil during the pre-\ntarget period up to target onset. This effect is even larger when temporal cues are presented\nalone (larger constriction in C-T+ compared to C+T+). Not sure this is clearly explained. Lighting\nwas controlled across conditions discarding luminance changes. Were there fixation differences\nacross conditions (e.g., more saccades to temporal cues in C-T+ than C+T+) or is the effect\nexplained by arousal differences? Why C-T- shows more pre-target pupil constriction than C+T-\n?\nPupillary responses in Fig. 1g are provided in z-scores, as our pupil preprocessing included\nnormalisation across the continuous time series prior to segmentation. Initial values reflected\narbitrary units corresponding to the amount of pixels detected by the EyeLink camera within the\npupil. In response to the reviewer’s comment, Fig. 1 has been updated (kindly see our response\nto the previous comment), the figure legend has been amended, and a dedicated paragraph for\nthe pupil preprocessing has been added as follows (p. 21):\nFig. 1. [...] g, Controlling for the presence or absence of temporal cues, spatial cues elicited robust increases in\npupil diameter. Pupil diameter at target onset was strongly correlated with respiration phase (see inset; dashed\nline shows group-level mean across phase bins). Independently of temporal cues, spatially cued trials on\naverage lead to significantly larger pupil diameter during the fixation period. Note that pupil diameter is given in\nrobust z-scores since pupil preprocessing involved normalization across the continuous time series. [...]\nPupil preprocessing. Pupil data were preprocessed following procedures of previous work\n18. In short, area traces were converted to pupil diameter to linearize our measure of pupil\nsize. Blinks were identified and visually validated by an automatic procedure (available from\nhttps://github.com/anne-urai/pupil_preprocessing_tutorial) and linearly interpolated. Blink-\ninterpolated pupil time series were subjected to the procedure again using a relaxed\ncriterion of z = 6 SD to capture remaining artifacts. Next, canonical responses to blinks were\nestimated and removed from pupil time series. To that end, pupil time series were band-\npass filtered (pass band: 0.01–10 Hz, second-order Butterworth, forward-reverse two-pass).\nFor subsequent analyses, pupil diameter time series were converted to z scores using a\nrobust procedure (MATLAB function ‘normalize’ with options set to ‘zscore’ and ‘robust’).\nRegarding the constriction effects following temporal cues, it is difficult to say with absolute\ncertainty that lighting conditions did not change between e.g. C-T- and C-T+. If one were picky\nfrom a psychophysical standpoint, one aspect to note is that the temporal cue (i.e. the circular\n‘countdown’ surrounding the central fixation marker) is an additional light stimulus appearing on\na dark background. Pupillary constrictions for C-T+ (vs C-T-) and C+T+ (vs C+T-) may at least in\npart be explained by the additional brightness of the temporal cue, which would fit our pattern of\nresults in Fig. 1g. This is why we refer to ‘constant lighting conditions’ in the strict sense only in\nthe analyses we report, meaning contrasting conditions in which temporal cues are either present\nor absent in both cases. Following the reviewer’s remarks, we have amended the manuscript to\nmake this reasoning more clear (p. 6):\nControlling for lighting changes on the screen (i.e. the presence of the temporal countdown\nas a small, but light stimulus during C-T+ and C+T+ trials), spatial cues significantly\nmodulated arousal during prestimulus fixation leading up to target presentation, irrespective\nof temporal cues (FDR-corrected p < .05; Fig. 1g), most likely reflecting the pre-stimulus\ndifference in target uncertainty.\nFinally, as for the reviewer’s question regarding arousal or fixation differences, we do indeed\ninterpret our results as a reflection of differential arousal states depending on target-related\nuncertainty and available spatiotemporal information. Since participants were not only instructed\nto fixate on the central marker, but we also had continuous real-time monitoring of both the pupil\nellipsoid (in the eyetracker) and the resulting time series (from the MEG), we have no reason to\nbelieve that saccade behaviour differed between conditions. As is routine in all our studies,\nparticipants are reminded of the instructions in case their ocular behaviour (fixations, blinks)\nsystematically disrupts the experiment.\nTo confirm, we conducted an analysis of saccadic patterns across conditions. Raw saccade data\nfrom the eyetracker was segmented according to the trial definition of our behavioural analysis,\ncropped to the [-1.6 0] s prestimulus window, and sorted into different conditions. Using Fieldtrip’s\nbuilt-in artifact detection functionality ft_artifact_zvalue, we extracted the number of saccades\nexceeding a magnitude threshold of z = 5. Upon visual inspection, one participant was left out of\nthis analysis due to an overly noisy signal in one of the experimental blocks. As expected, the\nnumber of saccades overall was low (with the group-level median ranging from 4-6 saccades over\n180 trials across conditions, see plot below) and a Kruskall-Wallis test showed no group-level\ndifferences in the number of saccades across conditions (p = .92).\nWe have added this control analysis to the Results section as follows (p. 7):\nAs a control analysis, we quantified saccadic behaviour during the prestimulus time window\nacross the four conditions. As expected, the number of saccades overall was low (with the\ngroup-level median ranging from M = 4 to M = 6 saccades over 180 trials across conditions)\nand a Kruskall-Wallis test showed no group-level differences in the number of saccades\nacross conditions (p = .92).\nPrevious studies have shown that during spontaneous ventilation (no stimuli) pupil size expands\nduring inhalation and contracts during exhalation. Namely it starts expanding 1-1.5s after\ninhalation onset. Are the present arousal-related pupil changes correlated to the subsequent\ninhalation-related pupil changes after target onset? Given that in this study, most subjects align\ntheir mid/late inhalations to target onset to improve detection, were there larger inhalation-related\npupil changes in resp aligned trials?\nThank you for pointing out the intriguing connection between arousal-related pupil responses and\nrespiration phase. As recently highlighted in a comprehensive review by Schaefer and colleagues\n(Eur J Neurosci 2022), the relationship between respiratory and pupillary time series is far from\ntrivial with very mixed findings overall. In our data, we indeed report greater pupil diameter\nindicating higher arousal during inspiration, so the reviewer asks an important question regarding\ndiameter change differences as a function of respiratory alignment. At this point one has to be\ncareful not to conduct a circular analysis on the single-trial level, since we did not register post-\nstimulus inhalation for each trial (which might lead to a biased selection of trials to include in such\nan analysis).\nIn order to approximate the suggested analysis, we recomputed respiration phase-locked pupil\ndiameter separately for each condition. To account for baseline differences in pupil diameter at\ntarget onset (see Fig. 1g), condition-specific vectors of pupil diameter ~ respiration phase were\nmean-centered within each condition. From Fig. 1c and 1e, it is evident that the C+T+ shows both\nthe clearest behavioural effect (i.e. lowered inspiratory threshold) and the most focal distribution\nof trial onsets in that beneficial phase (i.e. the strongest respiratory adaptation). If there was a\nsystematic link between preferred phase and inspiration-locked arousal changes, the increase in\npupil diameter should be most prominent in C+T+ trials. This was not what we observed, however\n- the inspiratory increase in pupil diameter was highly consistent across conditions (see plot\nbelow).\nThe observation of condition-general inspiratory increase in arousal has been added to the\nmanuscript as follows (p. 7):\nPupil diameter at target onset (across all conditions) was systematically related to\nrespiration phase at target onset (Rayleigh test of individual circular means: z(29) = 8.51, p\n< .001), with greater diameter indicating higher arousal during inspiration. In addition to the\nreported overall effect, this modulation was consistently observed within each condition as\nwell.\n“Controlling for lighting changes on the screen, spatial cues significantly modulated arousal during\nprestimulus fixation leading up to target presentation, irrespective of temporal cues (FDR-\ncorrected p < .05; Fig. 1g).” Does that mean that spatial cues prevent pupil constriction from\nhappening during the pre-target period?\nThank you for prompting us to clarify that interpretation. We understand the pupil diameter pattern\nshown in Fig. 1g as follows: When a spatial cue is added, either from C-T- to C+T- or C-T+ to\nC+T+, we indeed observe a reduced constriction of the pupil, no matter whether a temporal cue\nis present or not. In other words, pupillary responses to spatial cues appear to reflect an\n‘upholding’ of increased prestimulus arousal up to the point of stimulus presentation.\n“Pupil diameter at target onset was systematically related to respiration phase at target onset\n(Rayleigh test of individual circular means: z(29) = 8.51, p < .001), with greater diameter indicating\nhigher arousal during inspiration”. Is the greater pupil constriction in C-T+ compared to C+T+ at\npre-target and target onset related to higher arousal due to higher uncertainty about the target?\nThis is difficult to answer, and we were deliberately careful not to overinterpret these findings,\nbut we would in principle agree with this interpretation. We clearly observe the higher arousal level\nindicated by greater pupil dilation, but can - strictly speaking - only speculate regarding the\nunderlying mechanism. Since, as the reviewer points out, the uncertainty manipulation is the only\nexperimental difference between the two conditions, we are confident that it would be difficult to\ncome up with a convincing counterargument.\nIn response to the reviewer’s remarks, we have added a brief (and still cautious) statement to\nclarify how we interpret this finding (p. 7):\nControlling for lighting changes on the screen, spatial cues significantly modulated arousal\nduring prestimulus fixation leading up to target presentation, irrespective of temporal cues\n(FDR-corrected p < .05; Fig. 1g), most likely reflecting the pre-stimulus difference in target\nuncertainty.\nUsing a mutual information model, the authors showed that near-threshold visual detection can\nbe predicted not only by spatial-temporal cues, but also by the respiratory phase at stimulus onset.\nIncorporating the sine and cosine of the peristimulus breathing cycle significantly improved single-\ntrial prediction accuracy, demonstrating that both stimulus predictability and ongoing respiratory\nstate jointly shape perceptual outcomes. This analysis robustly replicates and extends prior\nreports of respiration-linked perceptual modulation.\nIt is hard to grasp some of the aspects in Fig 1f. Black dots seem quite widely distributed in the y-\naxis. Does this mean that preferred resp phase is different in each subject? This interpretation\ncontrast with fig 1c – in which mid inhalation is the ideal timing across all subjects (e.g., lower\ncontrast gabor patches are perceived if presented during mid inhalation) and fig 1e. Maybe\nshowing the raw or averaged respiration waveforms overlaid would be helpful.\nWe apologize for the perceived inconsistency between Fig. 1f and results shown in panels 1c+e.\nFrom our description, it was apparently not always clear which conditions we referred to. To\nreiterate, Fig. 1c shows the C+T+ condition driving the inspiratory facilitation effect, which is highly\nconsistent with Fig. 1e (where these trials are contrasted against C-T- trials). The grey arrow\npointing from Fig. 1e to 1f was admittedly distracting, as panel 1f shows different conditions (the\narrow has been removed accordingly): Dark blue dots show the overall relationship between\npreferred phase and respiration rate changes for all conditions combined, which is why the\ndistribution of preferred phase appears more scattered and cannot be compared to the one shown\nin panel 1e.\nTo remedy this potential misunderstanding of our analyses, we have adjusted both Fig. 1 as well\nas the main text. While the updated Fig. 1 with the grey arrow removed and clearer axis labels\ncan be seen above, the respective passages in the main text read as follows:\nFig. 1. [...] f, Group-level correlation of respiratory adaptation with task performance. The more strongly\nparticipants adapted their respiratory frequency during the task (compared to rest), the better their performance\nfor timed trials (light blue). A strong circular-linear correlation between rest-to-state changes in respiration rate\nand preferred phase across all conditions suggests respiratory adaptation in favour of behavioural facilitation\n(blue). [...]\nMoreover, across all conditions, the more strongly participants adapted their respiratory\nfrequency during the task (compared to rest), the better their performance (i.e. the lower the\ncontrast necessary to achieve the targeted hit rate; see Fig. 1f). Critically, circular-linear\ncorrelation analysis revealed a robust positive correlation between rest-to-task change in\nrespiration rate and preferred phase at stimulus onset for timed-only stimuli (C-T+; ρ (28) =\n.50, p = .023). (p. 6)\nThe result that respiratory rate during task is higher than rest could be linked to the fact that it is\neasier to make phase adjustments in short resp cycles compared to longer ones.\nRegarding the analyses of the neural data, I believe the authors used state-of-the-art analyses\nand the results are strong with impressive correlations between neural activity in the RMBO\nnetwork, performance and respiratory phase.\nThank you for this suggestion. Faster respiratory rate during tasks compared to rest is a finding\nthat has been replicated many times. The reviewer is entirely correct that one explanation could\nbe a ‘baseline readiness’ due to shorter overall cycles in order to even more flexibly adjust one’s\nrespiratory cycles to external task demands (which are absent during rest). Our finding of greater\ndifferences from rest to task being predictive of perceptual performance support such an\ninterpretation.\nGiven the activation of temporal site (TPJ)– did the participants heard their breathing sounds\nmodulating auditory cortex activity? did they wear headphones?\nParticipants were seated in a quiet environment inside the MEG chamber and were therefore not\nwearing headphones. Note that the source-localized activity within TPJ on the cortical surface\n(parcels # 61 & 167 in the HCP atlas) is located rather far away from primary or associative\nauditory cortices below the temporal lobe (parcels # 37 & 152).\nIt is known that hyperventilation can modulate neural excitability. Given that participants breathed\nfaster during the task compared to rest, could this have influence some of their results? Was tidal\nvolume measured or controlled in anyway?\nTidal volume was constantly monitored with a respiration belt and participants were monitored via\nvideo throughout the MEG recording. No participants showed any signs of hyperventilation.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank both the ECR reviewer as well as their mentor for their work in evaluating our manuscript\nand appreciate the journal’s approach in strengthening junior researchers’ contributions to the\nscientific process."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I would first like to thank the authors for their careful and thoughtful responses to the previous round of comments. The revised manuscript is substantially improved. The following points are offered as minor comments aimed at further strengthening clarity, scope, and interpretative precision. None of them detract from the overall quality or validity of the work.\n\nFraming of active sensing and voluntary respiratory control: While the evidence for task-dependent respiratory adjustment and respiration-locked behavioural facilitation is compelling, some passages still read as slightly stronger than strictly warranted in terms of voluntary control. The data clearly support adaptive coupling between respiration, neural dynamics, and perceptual performance, but direct causal evidence for strategic respiratory control remains inferential.\nIt may therefore be helpful to slightly temper the language in places, e.g., by emphasizing adaptive alignment or task-dependent adjustment rather than fully intentional control.\n\nBehavioural interpretation and perceptual sensitivity: Given the absence of catch trials, established signal detection theory measures such as false alarm rate and decision criterion cannot be computed. While this limitation is acknowledged later in the manuscript, it may be useful to briefly remind the reader earlier that respiration-related threshold effects could, in principle, reflect a combination of sensory sensitivity and response criterion shifts.\n\nRelated literature and completeness: For the sake of completeness, the authors may wish to consider citing a recent preprint that is closely related in scope and conceptual framing: https://osf.io/preprints/psyarxiv/zj23s_v1\n\nInterpretation of directed connectivity analyses: The multivariate Granger causality analyses are carefully implemented and constitute a clear strength of the manuscript. That said, as with any GC-based approach, directed connectivity estimates may still be influenced by source leakage, common drivers, and slow rhythmic modulation.\nA brief, explicit note acknowledging these general limitations, and clarifying that the reported directionality reflects statistical predictability rather than anatomical or causal direction, would further strengthen interpretative caution and transparency. This would be particularly useful in sections linking specific INS–SMA or TPJ–V1 interactions to functional roles.\n\nDensity and presentation: The manuscript is necessarily rich, but some sections, particularly in the Results and Discussion, remain quite dense and occasionally reiterate similar points across behavioural, oscillatory, and connectivity levels. A modest reduction of repetition or slightly clearer \"marking\" of which analyses are confirmatory versus novel could improve readability without loss of content.\n\nIn summary, this is a strong and carefully executed study that makes a meaningful contribution to our understanding of respiration as a modulator of perception and neural dynamics. The points above are intended solely as minor refinements. I commend the authors for the quality of the work and for the clear improvements introduced in the revised version.", "reviewer_3": "The authors have answered my questions and now provide an improved manuscript. I support its publication.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Chalas et al.: Respiration as a dynamic modulator of sensory sampling\nResponse to Reviewers\nWe would like to thank all reviewers for their positive feedback and for supporting publication of\nour manuscript. In what follows, we address the final minor points raised by Rev 2. Reviewer\ncomments are shown in grey, our responses in blue, and quoted changes to the manuscript in\nred.\n\nReviewer #2:\nI would first like to thank the authors for their careful and thoughtful responses to the previous\nround of comments. The revised manuscript is substantially improved. The following points are\noffered as minor comments aimed at further strengthening clarity, scope, and interpretative\nprecision. None of them detract from the overall quality or validity of the work.\nThank you, we appreciate the reviewer’s positive encouraging assessment of our revision.\n\nFraming of active sensing and voluntary respiratory control: While the evidence for task-\ndependent respiratory adjustment and respiration-locked behavioural facilitation is compelling,\nsome passages still read as slightly stronger than strictly warranted in terms of voluntary control.\nThe data clearly support adaptive coupling between respiration, neural dynamics, and perceptual\nperformance, but direct causal evidence for strategic respiratory control remains inferential.\nIt may therefore be helpful to slightly temper the language in places, e.g., by emphasizing adaptive\nalignment or task-dependent adjustment rather than fully intentional control.\nWe agree that even a compelling body of evidence should not be overinterpreted as a clear\nindication of voluntary control (excluding any other potential explanation). In line with the\nreviewer’s suggestion, we have therefore rephrased the manuscript in the following instances to\nemphasize alignment/adjustment:\nParticipants adapted their breathing patterns to align with predictable stimulus onset, and this\nadaptive respiratory alignment correlated with improved performance. (p. 1)\nTogether with the behavioural evidence outlined above, the tight link of these respiration-related\nexcitability modulations to sensory sampling as well as their flexible adaptation to predictability\nchanges strongly suggest that the respiratory rhythm is adaptively aligned with task dynamics in\norder to form an increasingly precise predictive model of upcoming sensory information and\noptimally prepare corresponding behavioural responses. (p. 16)\n\nBehavioural interpretation and perceptual sensitivity: Given the absence of catch trials,\nestablished signal detection theory measures such as false alarm rate and decision criterion\ncannot be computed. While this limitation is acknowledged later in the manuscript, it may be useful\nto briefly remind the reader earlier that respiration-related threshold effects could, in principle,\nreflect a combination of sensory sensitivity and response criterion shifts.\nThank you, we have now added an earlier statement regarding this limitation:\nIt should be noted that phase-related changes in decision criterion could occur simultaneously\nwith the sensitivity modulation, but their computation would require changes in the experimental\nparadigm (i.e. the introduction of catch trials). (p. 4)\n\nRelated literature and completeness: For the sake of completeness, the authors may wish to\nconsider citing a recent preprint that is closely related in scope and conceptual framing:\nhttps://osf.io/preprints/psyarxiv/zj23s_v1\nWe appreciate the suggestion and have gladly added the new preprint as reference #30 to the\nIntroduction section:\nAt present, converging evidence from different lines of research strongly suggests respiratory\ninvolvement in active sensing, including adaptation of respiratory behaviour to task timing 28,\nmodulation of excitability and arousal states 4, and behavioural facilitation 29-30 (p. 3)\n\nInterpretation of directed connectivity analyses: The multivariate Granger causality analyses are\ncarefully implemented and constitute a clear strength of the manuscript. That said, as with any\nGC-based approach, directed connectivity estimates may still be influenced by source leakage,\ncommon drivers, and slow rhythmic modulation.\nA brief, explicit note acknowledging these general limitations, and clarifying that the reported\ndirectionality reflects statistical predictability rather than anatomical or causal direction, would\nfurther strengthen interpretative caution and transparency. This would be particularly useful in\nsections linking specific INS–SMA or TPJ–V1 interactions to functional roles.\nThe question of causality closely relates to a previous comment regarding voluntary control. In\nkeeping with our previous response, we have added a cautionary statement with regard to the\nlimits of GC analyses and their interpretation in the Discussion section:\nNote that any directional connectivity analysis cannot strictly exclude influences like source\nleakage or infraslow modulation. Hence, even the compelling phase-related directionality\nchanges presented here reflect statistical predictability rather than immediate anatomical or\ncausal direction. (p. 18)\n\nDensity and presentation: The manuscript is necessarily rich, but some sections, particularly in\nthe Results and Discussion, remain quite dense and occasionally reiterate similar points across\nbehavioural, oscillatory, and connectivity levels. A modest reduction of repetition or slightly clearer\n\"marking\" of which analyses are confirmatory versus novel could improve readability without loss\nof content.\nIn response to the reviewer’s suggestion, we have rephrased the manuscript in a few instances\nin order to more clearly separate novel insights from corresponding (but probably confirmatory)\nresults in other modalities:\nConfirming the performance effects shown in Fig. 1b, including spatial and temporal cues\nsignificantly improved the binary prediction of correct vs incorrect responses compared to a base\nmodel that only used single-trial contrast information (t(29) = 2.64, p = .013). (p. 4)\nConfirming the initial behavioural results, we did in fact observe clustering of single trials during\nthe inspiratory phase for highly predictable C+T+ trials (Hodges-Ajne test against uniformity: p =\n.005), but not for C-T- trials (p = .560). (p. 6)\nMoving from arousal neuromodulation to excitability states, we next sought to confirm a well-\nestablished neural signature of perceptual performance, namely alpha suppression effects over\nparieto-occipital sensors for hits vs misses (i.e., perceived vs non-perceived trials; Fig. 2a).\nReplicating previous work from our lab 4, we report long-lasting alpha suppression for perceived\ntrials (Fig. 2b), starting as early as the cue onset at -1600 ms prestimulus. (p. 7)\nFirst, we sought to confirm the overall effect of respiratory phase on prestimulus spectral power\nwithin our network of interest. (p. 8)\n\nIn summary, this is a strong and carefully executed study that makes a meaningful contribution to\nour understanding of respiration as a modulator of perception and neural dynamics. The points\nabove are intended solely as minor refinements. I commend the authors for the quality of the work\nand for the clear improvements introduced in the revised version.\nThank you again for consistently helpful and productive comments, much appreciated."}]

Decision letter