Neural underpinning of a respiration-associated resting-state fMRI network

What does the brain do when we breathe? Humans and other animals with lungs depend on breathing to supply their cells with oxygen for energy production. Neurons in the brain are supplied oxygen through an intricate system of blood vessels. When active, neurons consume a lot of energy and require a steady supply of oxygen-rich blood. In fact, this relationship between blood vessels and activity of neurons in the brain is so tightly linked that to study neuron activity researchers and clinicians often use an approach called functional magnetic resonance imaging (fMRI) to analyze the flow of oxygenated blood in the brain.

This imaging technique allows scientists to map how active different parts of the brain are at any given time without the need for an invasive medical procedure. Unfortunately, fMRI results are affected by the cycles of inhalation and exhalation that take place while breathing, even when an individual is at rest. This is because the rate and depth of respiration can vary, resulting in the body moving unpredictably and in CO₂ levels fluctuating in the brain, which can lead to changes in fMRI signals that do not correlate with neuron activity. Such misleading measurements are called ‘artifacts’. The assumption that these fMRI results do not represent real brain activity has meant that the effects of breathing on neuron activity in different parts of the brain is poorly understood.

To solve this issue, Tu and Zhang performed fMRI on rats and combined the results with measurements of the depth and rate of respiration, and with electrophysiology, an approach that allowed them to directly record the electrical properties of neurons. This allowed them to map out the network of neurons that become active in response to breathing.

The results show that breathing leads to a specific fMRI signal that can be distinguished from the artifacts introduced by fluctuating CO₂ levels and body movements. The signal correlates with the activity of neurons measured using electrophysiology and with breathing patterns, and it disappears when the electrical activity of neurons in the brain is suppressed, even if the rats are still breathing. This suggests that breathing affects brain activity that is independent of the previously described artifacts.

Future studies may focus on how the brain responds to breathing or how respiration itself is controlled by the brain, with the methods developed allowing researchers to explore regions of the brain that increase their activity while breathing. This clears the path towards investigating the neural mechanisms underlying therapies and exercises that focus on breathing.

Resting-state fMRI (rsfMRI), which measures spontaneous blood-oxygen-level-dependent (BOLD) signal, is a powerful tool for non-invasively investigating brain-wide functional connectivity (Biswal et al., 1995; Fox and Raichle, 2007; Smith et al., 2009). Due to its hemodynamic nature, the rsfMRI signal is susceptible to systemic physiological changes such as respiration and cardiac pulsations (Murphy et al., 2013; Liu, 2016; Caballero-Gaudes and Reynolds, 2017; Chen et al., 2020), and these effects are usually treated as non-neuronal artifacts in rsfMRI data.

Respiration is a major physiological process that drives fluctuations in cerebral blood flow and oxygenation (Liu, 2016). Respiration can affect the BOLD signal (Thomason et al., 2005; Abbott et al., 2005; Birn et al., 2006) with two types of effects resulting from slow respiration variations and fast cyclic changes, respectively. Slowly varying changes (typically below 0.15 Hz) in breathing rate and depth, which can be quantified as respiration volume per time (RVT) (Birn et al., 2006), covary with arterial CO₂ (Wise et al., 2004; Chang and Glover, 2009; Hoiland et al., 2016; Liu et al., 2017) and affect the BOLD signal via vasodilatory effects and/or autonomic influences of the vessel tone (Duyn et al., 2020; Picchioni et al., 2022). On the other hand, faster respiratory cycles (i.e., periodic inspiration and expiration), accompanied by the corresponding chest and neck movement, can induce changes in the static magnetic field, which in turn lead to BOLD signal changes (Glover et al., 2000; Raj et al., 2000; Windischberger et al., 2002). Both effects can be effectively mitigated/removed by standard rsfMRI preprocessing methods (Birn et al., 2006; Glover et al., 2000).

In addition to the well-characterized respiration-related non-neural artifacts, there is evidence hinting a possible neural component in the respiration–rsfMRI interaction (Yuan et al., 2013; Shams et al., 2021). First, respiration can directly drive brain-wide neuronal oscillations (Tort et al., 2018a; Kay et al., 2009) not only in the olfactory bulb (Adrian, 1942) and piriform cortex (Fontanini et al., 2003)—brain regions directly related to breathing—but also in the medial prefrontal cortex (mPFC), somatosensory cortex, and hippocampus, and this effect has been consistently found across species (Ito et al., 2014; Yanovsky et al., 2014; Biskamp et al., 2017; Zelano et al., 2016). In addition, respiration changes can be associated with arousal and/or emotion-related brain state changes, which covary with cortical activity (Yackle et al., 2017; Shea, 1996; Homma and Masaoka, 2008; Folschweiller and Sauer, 2021). Therefore, in addition to the artifactual effects aforementioned, respiration may affect the rsfMRI signal by directly modulating the neural activity. However, this potential neural component in the respiration–fMRI relationship is largely unexplored.

To gain a comprehensive understanding of the relationships between respiration, neuronal activity, and rsfMRI signal, here we simultaneously acquired rsfMRI, electrophysiology, and respiration data in anesthetized rats. Anesthesia was used to ensure our results are not confounded by the animal’s motion, which affects all three signals. Based on these measures, an RVT-correlated rsfMRI network was identified. Importantly, regressing out gamma activity or silencing neural activity across the brain disrupted this respiration-related network, suggesting that this respiration–rsfMRI relationship is mediated by neural activity.

To determine the potential role of neural activity in the respiration–rsfMRI relationship, we simultaneously recorded the electrophysiology and respiration signals along with rsfMRI data in rats (Figure 1A). The respiration signal was recorded by a respiration sensor placed under the animal’s chest (Figure 1A). Representative raw respiration signal, the respiration rate distribution across all scans, and the averaged respiration power are shown in Figure 1B, C. Slow respiration variations are quantified by RVT, calculated as the difference of consecutive peaks of inspiration and expiration divided by the time interval between the two adjacent signal maxima (or minima) (Figure 1D; Birn et al., 2006). The electrophysiology signal was recorded using an MR-compatible electrode implanted in the right side of the anterior cingulate cortex (ACC). The ACC was selected given its critical role in respiratory modulation, evidenced by early studies showing that ACC was activated during breathlessness (Liotti et al., 2001; Evans et al., 2002). More recent research further demonstrates the role of ACC in respiratory control (Evans et al., 2009; Holton et al., 2021; Tort et al., 2018b). For example, electrophysiology recordings in humans showed that ACC exhibited different neural responses to separate respirational tasks including breath-holding, voluntary deep breathing, and hypercapnia (Holton et al., 2021). In addition, the ACC is a key region in the rodent default-mode network (DMN) (Lu et al., 2012; Tu et al., 2021a), which has been linked to respiration-related fMRI signal changes (Birn et al., 2006). The location of the electrode was confirmed by T2-weighted structural images (Figure 1—figure supplement 1). MRI artifacts in the electrophysiology signal were removed using a template regression method (Logothetis et al., 2001; Pan et al., 2011) (see Materials and methods and Figure 1—figure supplement 2), and local field potential (LFP) and spectrogram were obtained using denoised electrophysiologic data (Figure 1E).

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Gamma-band neural oscillations are, respectively, associated with respiration and rsfMRI signals

We first asked whether the respiration is linked to neural activity changes (Figure 2A) in lightly sedated animals (combined low-dose dexmedetomidine and isoflurane, see Methods and materials). Prominent coherence between the respiration signal and LFP is observed with the dominant peak at ~1.1 Hz (Figure 2B, C), in line with the respiration frequency in animals (Figure 1C). This result is consistent with the finding of respiration-entrained LFP oscillations previously reported (Tort et al., 2018a; Kay et al., 2009).

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To further dissect the respiration–LFP relationship, the LFP was separated into five conventionally defined frequency bands including gamma (40–100 Hz), delta (1–4 Hz), theta (4–7 Hz), alpha (7–13 Hz), and beta band (13–30 Hz) (Lu et al., 2016; Zhang et al., 2020). Gamma-band power displays significant coherence with the RVT at ~0.035 Hz with a zero phase lag (Figure 2D, E, phase [mean ± ste] = −0.08 ± 0.5 pi, with the range of [−pi, pi]), suggesting a phase-locking relationship between the two measures. In contrast, this coherence is not observed in any other frequency bands (Figure 2F–I). These results remain the same when the LFP data were analyzed using the local subtraction method (Figure 2—figure supplement 1A–C), ruling out the potential artifact resulting from the volume conduction of signals between the cortex and olfactory bulb (Parabucki and Lampl, 2017). The RVT–gamma power coherence in individual lightly sedated animals is shown in Figure 2—figure supplement 2. Taken together, our data demonstrate that respiration is associated with gamma-band neural oscillations in the ACC.

We next examined the relationship between the gamma-band LFP and rsfMRI signals (Figure 3A; Winder et al., 2017). The ACC gamma power was first convolved with a hemodynamic response function (HRF, defined by a single gamma probability distribution function (a = 3, b = 0.8) Liang et al., 2017, Figure 3B), which was then voxel-wise correlated to brain-wide rsfMRI signals. The r value between the ACC rsfMRI signal and HRF-convolved gamma power was 0.086 (1200 time points, p = 0.003). Our data show that the gamma power-derived rsfMRI correlation map (Figure 3C) is highly consistent with the ACC resting-state functional connectivity (RSFC) seedmap with the right ACC (i.e., the electrode implanted side) defined as the seed (Figure 3D), evidenced by a strong voxel-to-voxel spatial correlation between these two maps (Figure 3E, R = 0.775, p < 10⁻¹⁵). Again, the same gamma power-derived correlation pattern is observed when the LFP data were analyzed using the local subtraction method (Figure 2—figure supplement 1D). The gamma power-derived rsfMRI correlation maps in individual animals are shown in Figure 3—figure supplement 1. These data demonstrate that the gamma-band LFP is tightly linked to the rsfMRI signal.

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Respiration is associated with a characteristic rsfMRI network, mediated by gamma-band neural activity

Given that the gamma-band power is, respectively, associated with the RVT and rsfMRI signals, we specifically asked how RVT is related to the rsfMRI signal in lightly sedated animals by calculating voxel-wise correlations between the RVT and rsfMRI signals (Figure 4A). This analysis generates a respiration-related rsfMRI network (Figure 4B), involving key brain regions controlling respiration such as the piriform cortex. It is known that the piriform cortex receives inputs from the olfactory bulb, and can be directly activated when the animal breathes. In addition, this network includes regions involved in the rodent DMN such as the ACC, mPFC, orbital, retrosplenial, and primary somatosensory cortices, as well as the hippocampus (Lu et al., 2012; Figure 4C, one-sample t-test, p < 0.05, false discovery rate [FDR] corrected). The resemblance of this respiration-related network and DMN well agrees with the human literature that the physiological effects on rsfMRI data are colocalized with the DMN (Birn et al., 2006). The respiration-related rsfMRI network in individual animals is shown Figure 4—figure supplement 1. Taken together, slow respiration variations exhibit a characteristic correlation pattern with brain-wide rsfMRI signals, representing a respiration-related rsfMRI network.

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To test whether this respiration-related rsfMRI network is mediated by gamma-band activity, the gamma power was, respectively, regressed out from the RVT and voxel-wise rsfMRI signals, and the same correlational analysis was repeated on the residual signals. In this case, the respiration-related network is diminished (Figure 4D). This result is confirmed by the contrast of the correlation maps before (Figure 4B) and after (Figure 4D) gamma power regression, with essentially all brain regions involved in the respiration-related network exhibiting reduced RVT–rsfMRI correlation after the gamma power is regressed out (Figure 4E, paired t-test, p < 0.05, FDR corrected). These results demonstrate that the respiration-related rsfMRI network is mediated by gamma-band neural activity.

The respiration-related rsfMRI network is absent at the isoelectric state

To further confirm the necessary role of neural activity in the respiration-related rsfMRI network, we experimentally silenced the neural activity in the whole brain by inducing an isoelectric brain state using high-dose sodium pentobarbital, while maintained the respiration in the rat (Figure 5A; Du et al., 2008). At the isoelectric state, the LFP amplitude and power are close to zero, in remarkable contrast to the electrophysiology data recorded before the drug infusion (Figure 5B). In addition, rsfMRI data recorded at the isoelectric state exhibit a flat, noise-like power distribution, unlike the characteristic 1/f pattern during light sedation (Figure 5I; Zhang et al., 2021). Furthermore, there is no meaningful ACC RSFC in the ACC seedmap at the isoelectric state (Figure 5H), which is distinct from the ACC seedmap during light sedation (Figure 3D). The brain-wide ROI-based RSFC matrix also reveals a global suppression of RSFC at the isoelectric state (Figure 5K), compared to the RSFC matrix observed during light sedation. These data collectively confirm that brain-wide neural activity is silenced in the isoelectric state.

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To examine whether the temporal variation of respiration in the isoelectric state and light sedation is consistent, we calculated the respiration rate (Figure 5D) as well as the standard deviation (SD) and power spectrum of RVT across all scans. We did not observe any significant difference in the SD of the respiration rate (Figure 5E, two-sample t-test, t = −1.0, p = 0.32) or the SD of RVT (Figure 5F, two-sample t-test, t = −0.87, p = 0.39) between the light sedation and isoelectric states, suggesting that the temporal variance of respiration is similar between the two states. Interestingly, we observed a characteristic 1/f pattern in the RVT power during light sedation, but this pattern was not present at the isoelectric state (Figure 5G). This difference is consistent with the ACC BOLD signal power spectra in the light sedation and isoelectric states (Figure 5I).

After confirming the global neural silencing effect, we calculated voxel-wise correlations between the RVT and rsfMRI signals in the isoelectric state. The respiration-related network observed during light sedation is absent when brain-wide neural activity is silenced (Figure 5J), despite similar breathing patterns (Figure 5C–F) at both conditions. The RVT–rsfMRI correlation maps in individual animals in the isoelectric state are shown in Figure 5—figure supplement 1. These data demonstrate that the neural activity plays a necessary role in the respiration-related rsfMRI network, corroborating the notion that the respiration-related rsfMR network we observed is mediated by neural activity.

In this study, we discovered a slow respiration variation-related functional network in rodents. Importantly, we confirmed the neural underpinning of this network, indicated by the phase-locking relationship between the RVT and gamma-band power in the ACC—a node in this network. Regressing out the gamma-band power disrupted the respiration network, suggesting a mediating role of neural activity in this network. To further validate this finding, we experimentally silenced the neural activity across the brain but maintained the respiration in the animal. In this condition, we again failed to observe the respiration-related brain network, confirming the neural underpinning of the network. Lastly, we showed that the respiration-related neural network is distinct from respiration-related rsfMRI artifacts. Overall, our study maps brain-wide neural responses related to respiration. These data provide new insight into understanding the neural activity-mediated respiratory effects on resting-state functional networks. Importantly, this breathing-related brain network might be altered in brain disorders, and thus our findings might potentially provide important clinical value.

All data for this study have been deposited to NITRIC repository.

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Author details

1. Wenyu Tu

   1. The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, United States
   2. Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3480-2098

2. Nanyin Zhang

   1. The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, United States
   2. Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park, United States
   3. Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   nuz2@psu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5824-9058

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