Brain Networks: Modeling breathing rhythms
Computational models are helping researchers to understand how certain properties of neurons contribute to respiratory rhythms.
- Seattle Children's Research Institute, United States
- University of Washington, United States
Orchestral music is beautiful, rich and complex, yet analyzing the contribution of any one instrument can be difficult. In many ways, neuroscientists face similar challenges when they try to understand how the networks in the brain work. These circuits are made of many types of neurons, each of which has different properties. Moreover, the properties of a neuron can change because of its interactions with other cells in the network.
Researchers have started to unravel this complexity by building computational models of both neurons and networks of neurons, and by focusing on pairs of interacting properties. This is similar to how one might study how the left and right hand of a violinist work together to produce melody and rhythm, and then use this knowledge to better understand other stringed instruments and their roles in the orchestra.
Within the ‘rhythmogenic’ networks of the brain, different types of neurons work together to create the body rhythms that are essential for life. For example, a complex network generates breathing rhythms, and it is often used to understand rhythmogenic circuits in general. The balance between excitatory and inhibitory connections between neurons is critical to shape network activity (Ramirez and Baertsch, 2018). Equally important, but less well understood, are the interactions between intrinsic properties that are built into individual neurons.
In particular, two intrinsic properties are thought to play a role in controlling breathing rhythms and network activity in general. The first is a persistent sodium current (INaP), which is slowly activated and inactivated by changes in the voltage across the neuronal membrane. The second does not depend on voltage: rather, the calcium-activated non-selective cation current (ICAN) is triggered when the level of calcium ions inside the cell increases. The source of these calcium ions is unknown, but there is experimental evidence that they could be provided by mechanisms at the synapses between neurons (Del Negro et al., 2010; Del Negro et al., 2011). Both INaP and ICAN allow some neurons to generate rhythms on their own, without being stimulated by their neighbors. Yet, in the actual network, these properties also enhance the signals transmitted by excitatory synapses (Ramirez et al., 2004). Because INaP and ICAN interact with synaptic properties, as well as with each other, it is difficult to isolate their relative contributions.
Pharmacological and genetic manipulations have shed light on how INaP and ICAN work in the respiratory network (see, for example, Koizumi et al., 2018; Picardo et al., 2019), but there is still no consensus on how they contribute to rhythmogenesis (Feldman and Del Negro, 2006; Ramirez et al., 2004). Now, in eLife, Jeffrey Smith of the National Institute Neurological Disorders Stroke (NINDS) and colleagues – including Ryan Phillips as first author – report results from a computational modeling approach that re-examines how INaP and ICAN control breathing (Phillips et al., 2019).
The researchers, who are based at NINDS, the University of New Hampshire and Georgia State University, established a simplified model of the breathing network, which only takes into account excitatory synaptic interactions. Then, they tuned the contributions of three variables, INaP, ICAN, and the source of intracellular calcium ions, and explored how this affected the frequency and amplitude of breathing. First, they examined how changes in the source of calcium ions influenced the contribution of ICAN to the network. If calcium came from within neurons, ICAN controlled the frequency, but not the strength, of breathing. On the other hand, if calcium depended on synaptic activity, ICAN acted as a synaptic amplifier to control the strength of the rhythm, but it had little effect on its frequency. This scenario best matched experimental data and prior conclusions (Del Negro et al., 2011; Koizumi et al., 2018), prompting Phillips et al. to conclude that ICAN is activated by calcium ions that are primarily of synaptic origin.
The group then went on to demonstrate that ICAN does not establish the rhythm in their computational model, but that it increases the strength of breathing by recruiting more neurons to participate in the network. Indeed, when ICAN was eliminated from the model network, a weak breathing rhythm remained, which was generated by a small number of neurons with high levels of INaP activity (Figure 1). Eliminating this ‘rhythmogenic kernel’ from the model stopped the rhythm altogether, in agreement with some (Peña et al., 2004), but not all (Pace et al., 2007) experimental data.
Figure 1
How the intrinsic properties of neurons contribute to the breathing rhythm.
Phillips et al. established a model of the brain network that controls breathing, and used it to deduce how two built-in properties of neurons, the INaP sodium current (blue) and the ICAN current (green), control the breathing rhythm. Without INaP, the network is silent, with the neurons exhibiting non-rhythmic activity (grey). However, a small number of neurons with high levels of INaP activity can produce a weak rhythm, even in the absence of ICAN. In turn, the model suggests that ICAN is activated by calcium ions coming from synapses; the current would then amplify excitatory interactions between neurons. This amplification leads to additional neurons participating in the rhythm (red), producing a robust functional network.
Unlike the model built by Phillips et al., the actual network that controls breathing is not exclusively excitatory, but is subjected to important inhibitory and neuromodulatory control. Further, alternative mechanisms of rhythmogenesis that do not depend on INaP have also been proposed (Del Negro et al., 2010; Rubin et al., 2009). Could different rhythmogenic mechanisms therefore contribute to breathing, depending on the demands of the network? In particular, could the contributions of INaP and ICAN change based on the neuromodulatory state of the network? Addressing such questions will require further back-and-forth between experiments and increasingly complex models.
Overall, the results of Phillips et al. allow us to understand how intrinsic neuronal properties independently control the strength and frequency of the breathing rhythm. Their model is also a useful framework in which to explore how changes in the way INaP and ICAN interact can dynamically impact rhythmogenic properties. For example, it could shed light on the way how the network reconfigures when the body lacks oxygen. Ultimately, describing the duet between INaP and ICAN in the respiratory network may help dissect how rhythmic activity is controlled in other regions of the brain (Penn et al., 2016; Riquelme et al., 2018).
References
-
Dendritic calcium activity precedes inspiratory bursts in preBötzinger complex neuronsJournal of Neuroscience 31:1017–1022.https://doi.org/10.1523/JNEUROSCI.4731-10.2011
-
Looking for inspiration: new perspectives on respiratory rhythmNature Reviews Neuroscience 7:232–241.https://doi.org/10.1038/nrn1871
-
Role of persistent sodium current in mouse preBötzinger complex neurons and respiratory rhythm generationThe Journal of Physiology 580:485–496.https://doi.org/10.1113/jphysiol.2006.124602
-
Pacemaker neurons and neuronal networks: an integrative viewCurrent Opinion in Neurobiology 14:665–674.https://doi.org/10.1016/j.conb.2004.10.011
-
Subcellular localization and activity of TRPM4 in medial prefrontal cortex layer 2/3Frontiers in Cellular Neuroscience 12:12.https://doi.org/10.3389/fncel.2018.00012
Article and author information
Author details
Jan-Marino Ramirez
Publication history
- Version of Record published: March 25, 2019 (version 1)
Copyright
© 2019, Ramirez and Baertsch
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
-
- 1,162
- Page views
-
- 174
- Downloads
-
- 1
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
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)
Brain Networks: Modeling breathing rhythms
eLife 8:e46033.
https://doi.org/10.7554/eLife.46033
Further reading
-
- Cancer Biology
- Computational and Systems Biology
Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell–cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.
-
- Computational and Systems Biology
The mouse brain is by far the most intensively studied among mammalian brains, yet basic measures of its cytoarchitecture remain obscure. For example, quantifying cell numbers, and the interplay of sex, strain, and individual variability in cell density and volume is out of reach for many regions. The Allen Mouse Brain Connectivity project produces high-resolution full brain images of hundreds of brains. Although these were created for a different purpose, they reveal details of neuroanatomy and cytoarchitecture. Here, we used this population to systematically characterize cell density and volume for each anatomical unit in the mouse brain. We developed a DNN-based segmentation pipeline that uses the autofluorescence intensities of images to segment cell nuclei even within the densest regions, such as the dentate gyrus. We applied our pipeline to 507 brains of males and females from C57BL/6J and FVB.CD1 strains. Globally, we found that increased overall brain volume does not result in uniform expansion across all regions. Moreover, region-specific density changes are often negatively correlated with the volume of the region; therefore, cell count does not scale linearly with volume. Many regions, including layer 2/3 across several cortical areas, showed distinct lateral bias. We identified strain-specific or sex-specific differences. For example, males tended to have more cells in extended amygdala and hypothalamic regions (MEA, BST, BLA, BMA, and LPO, AHN) while females had more cells in the orbital cortex (ORB). Yet, inter-individual variability was always greater than the effect size of a single qualifier. We provide the results of this analysis as an accessible resource for the community.
