Rhythmogenic Networks: The ins and outs of breathing
- Seattle Children's Hospital, United States
- Seattle Children's Research Institute, United States
From our first breath to our last we continuously maintain and adjust our breathing to meet our metabolic, behavioural and environmental needs. For example, we breathe faster when we are anxious or while we are working out; we gasp when we run out of air, and we sigh when we are relieved. Yet, despite all of its complexity, breathing is controlled by a very small network of neurons within the brainstem (Richter and Smith, 2014).
Our breathing follows a rhythm that can have up to three phases. The first phase, which is called ‘inspiration’, begins when we breathe in: our diaphragm and some of the muscles between our ribs contract to increase the volume of our chest, which causes air to enter our lungs. During the second phase, which is called ‘post-inspiration’, we begin to exhale by relaxing the diaphragm and rib muscles, which causes air to slowly leave the lungs. Post-inspiration is particularly important for generating speech. During the third phase, which is called ‘active expiration’, other muscles contract to actively push air out of the lungs. Active expiration is important during exercise, but is not necessary under resting conditions.
Within the brainstem, neurons working in a network called the preBötzinger complex generate the basis of our breathing rhythm (Gray et al., 2010). These neurons, which are identified as Dbx1 neurons, continue to generate rhythmic nerve impulses, even when they are isolated from the rest of the brain. These nerve impulses normally travel through the brain stem, to cranial nerves, and down the spinal cord (via motor neurons) to the diaphragm and rib muscles. However, this network is primarily active in the inspiration phase (Carroll et al., 2013), so there is considerable interest in efforts to identify and understand the neurons and areas of the brain that are responsible for generating all three phases of the breathing cycle. Now, in eLife, Paul Gray and co-workers at the Washington University School of Medicine, Baylor College of Medicine and the College of William and Mary have used elegant genetic approaches to provide new insights into the generation of the breathing rhythm and its different phases.
Gray and colleagues—including Srinivasan Tupal as first author—have built on recent research on mice that identified a second rhythm-generating region in the brainstem. This rhythmogenic region is formed from two overlapping groups of neurons: the retrotrapezoid nucleus (RTN for short) and the parafacial respiratory group (pFRG; Pagliardini et al., 2011). Deleting a gene to block the development of neurons called the Atoh1 neurons in the RTN/pFRG region caused mice to die shortly after birth because they were unable to establish a normal respiratory pattern (Rose et al., 2009). However, this discovery did not identify whether these neurons are directly involved in the generation of active expiration or through another role.
Now, by simultaneously measuring different respiratory motor neurons, Tupal et al. demonstrated that elimination of Atoh1 neurons throughout the ventral respiratory column (a larger region within the brainstem which includes the RTN/pFRG) causes the inspiratory and active expiratory phases of the breathing rhythm to become out of sync with one another (Tupal et al., 2014). However, there was still rhythmic breathing activity. And thus, unlike the loss of the Dbx1 neurons in the preBötzinger complex, losing the Atoh1 neurons in the RTN/pFRG region did not cause the breathing rhythm to be lost.
Tupal et al. conclude that two distinct populations of neurons are responsible for two different functions: some are responsible for generating the different rhythms of breathing, whilst others ensure that these different rhythms remain coordinated. Identifying which genes help to make these neurons, and the roles that these neurons play, may help us to understand medical conditions where breathing rhythms become uncoordinated, such as obstructive sleep apnea (Ramirez et al., 2013). However, the current model still leaves some questions unanswered. For example, how do neurons interacting in the brainstem give rise to the post-inspiration phase of breathing?
The findings by Tupal et al. also offer the thought-provoking possibility that there exists a third rhythmogenic region that generates the rhythm behind post-inspiratory activity. In addition to the preBötzinger complex and RTN/pFRG region, Dbx1 and Atoh1 neurons are also found throughout the ventral respiratory column (Gray, 2013). Thus, one may hypothesize that additional unidentified rhythmogenic networks of neurons may be present in this larger region and that these networks could generate the rhythm of post-inspiration.
If so, and much like what happens for active expiration, these networks would also need to be coordinated with the other networks that control the rhythms of the other phases of breathing. However, the neurons that might coordinate these rhythms are also currently unknown.
Tupal et al. propose that rhythmogenic networks could be organized in segments throughout the ventral respiratory column. This idea has been proposed for the spinal cord (Grillner, 2011) and is supported by experimental evidence (Wiggin et al., 2012). However the suggestion that a similar system in the brainstem exists in the brainstem remains to be validated.
While many questions remain unanswered, the work of Tupal et al. has expanded what we know about the neuronal activity and rhythms that control our breathing—a process that is vital to all of our lives.
References
-
Patterns of inspiratory phase-dependent activity in the in vitro respiratory networkJournal of Neurophysiology 109:285–295.https://doi.org/10.1152/jn.00619.2012
-
Developmental origin of preBotzinger complex respiratory neuronsJournal of Neuroscience 30:14883–14895.https://doi.org/10.1523/JNEUROSCI.4031-10.2010
-
Active expiration induced by excitation of ventral medulla in adult anesthetized ratsJournal of Neuroscience 31:2895–2905.https://doi.org/10.1523/JNEUROSCI.5338-10.2011
-
Central and peripheral factors contributing to obstructive sleep apneasRespiratory Physiology & Neurobiology 189:344–353.https://doi.org/10.1016/j.resp.2013.06.004
-
Respiratory rhythm generation in vivoNephron Physiology 29:58–71.https://doi.org/10.1152/physiol.00035.2013
Article and author information
Author details
Publication history
Copyright
© 2014, Ramirez et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,286
- views
-
- 49
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Rhythmogenic Networks: The ins and outs of breathing
eLife 3:e03375.
https://doi.org/10.7554/eLife.03375
Further reading
-
- Neuroscience
Time estimation is an essential prerequisite underlying various cognitive functions. Previous studies identified ‘sequential firing’ and ‘activity ramps’ as the primary neuron activity patterns in the medial frontal cortex (mPFC) that could convey information regarding time. However, the relationship between these patterns and the timing behavior has not been fully understood. In this study, we utilized in vivo calcium imaging of mPFC in rats performing a timing task. We observed cells that showed selective activation at trial start, end, or during the timing interval. By aligning long-term time-lapse datasets, we discovered that sequential patterns of time coding were stable over weeks, while cells coding for trial start or end showed constant dynamism. Furthermore, with a novel behavior design that allowed the animal to determine individual trial interval, we were able to demonstrate that real-time adjustment in the sequence procession speed closely tracked the trial-to-trial interval variations. And errors in the rats’ timing behavior can be primarily attributed to the premature ending of the time sequence. Together, our data suggest that sequential activity maybe a stable neural substrate that represents time under physiological conditions. Furthermore, our results imply the existence of a unique cell type in the mPFC that participates in the time-related sequences. Future characterization of this cell type could provide important insights in the neural mechanism of timing and related cognitive functions.
-
- Neuroscience
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran’s method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method’s power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran’s method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran’s method is a rapid, simple image segmentation method optimal for variable and high noise conditions.
