Spinal Cord Injury: Is the vagus nerve our neural connectome?

  1. V Reggie Edgerton  Is a corresponding author
  2. Parag Gad  Is a corresponding author
  1. University of California, Los Angeles, United States

Abstract

What are the implications of the vagus nerve being able to mediate the time-dependent plasticity of an array of sensorimotor networks?

Main text

The vagus nerve reports on the state of many of the organs in our body, including the heart, the lungs and the gut, and it relays this information to various neural control networks that unconsciously regulate internal organs. It has also been shown that artificial electric stimulation of the vagus nerve helps with recovery in animal models of stroke, tinnitus and spinal cord injury (De Ridder et al., 2014; Hays, 2016). In particular, stimulation of the vagus nerve promotes the recuperation of motor skills and, maybe, autonomic functions (such as breathing), even when the injuries took place years before the intervention. However, we do not fully understand how stimulating this single nerve can lead to such results.

Now, in eLife, Patrick Ganzer, Robert Rennaker at the University of Texas at Dallas and the Texas Biomedical Device Center, and colleagues, report that stimulating the vagus nerve of a rat with spinal injuries helps it to recover mobility of an affected limb – in this case, its front paw (Ganzer et al., 2018). The stimulation has to be applied during a short time window after the rat manages to perform a specific movement with this paw, such as grasping a lever with a specific level of strength.

In this scenario, grasping the lever activates a network of neurons. The connections between these neurons will then be reinforced if the vagus nerve is stimulated within seconds of this task being completed (Figure 1). Classical long-term potentiation experiments show that simultaneous activation leaves ‘tags’ in neurons, which help to strengthen any connections between these neurons (He et al., 2015). Yet, during vagus nerve stimulation, there is no direct synaptic link between the circuits that perform the motor task and the synapses that are excited by the vagus nerve (Alvarez-Dieppa et al., 2016; Hulsey et al., 2017).

The effects of vagus nerve and spinal cord stimulations on neural networks.

The schematic shows the effect of two types of artificial stimulations on a network of neurons (circles), which is activated when a rat with spinal cord injury pulls a lever with its affected limb. Column A shows the effect of vagus nerve stimulation on neural networks that are activated when the rat pulls that lever. In A1, when the rat performs the task, it activates neurons. Some of these are specific to this task (encircled by solid black lines), some of which are not (dashed black line). In A2, the vagus nerve (not represented) is stimulated (pink cloud and lightning bolts). The neurons previously activated are also modulated by this stimulation, and certain new neurons are activated (dashed red line). This combination of activation and modulation strengthens the connections between the neurons that previously fired together, which leads to a transformation of the network. Ultimately, this more robust network supports better physical performance by the rats. Column B shows the effect of spinal cord stimulation on the same neural networks. In B1, spinal cord stimulation is applied (blue cloud and lighting bolt) before the task takes place. When the task is performed (B2), the synapses between the groups of neurons that are involved are further reinforced, a result that is comparable to what is obtained with vagus nerve stimulation. Similar results may also be obtained by performing the stimulation during the task itself. Both vagus nerve and spinal cord stimulations depend on the subject performing a certain task that activates the networks which need reinforcing. However, how these two types of stimulations differ, and how they modulate neural connections, remains unclear.

IMAGE CREDITS: Figure courtesy of V Reggie Edgerton.

Instead, the effects of vagus nerve stimulation could be mediated by a wide range of neuromodulatory mechanisms, which are non-specific to a given synapse. Because the vagus nerve is connected to so many different organs, both in sensorimotor and autonomic ways, it provides the entry point to various neuroendocrine and neurotransmitter systems. Essentially, it seems that the vagus nerve can form a 'connectome' for many functions, which means that interventions via the vagus nerve have the potential to help with the recovery of multiple functions.

Remarkably, similar effects can be obtained by stimulating the spinal cord before the action is performed (Figure 1; Gad et al., 2013). In both cases, there are activity-dependent mechanisms that identify the synapses that have been activated, and the stimulation triggers a series of time-dependent events that reinforce the connections between the relevant neurons.

More broadly, there are at least four fundamental biological concepts relevant to the findings by Ganzer et al. First, the vagus nerve mediates physiological systems (including sensorimotor and autonomic systems) which are extensively and comprehensively integrated together. Second, these systems are dynamic and can reorganize and interact in ways that can dramatically change our behavior. Third, time-dependence has a central role in defining activity-dependent plasticity. Fourth, the networks under the influence of the vagus nerve may be constantly reshaped by neural and biochemical signals via activity-dependent mechanisms.

These four points are consistent with the theory of neural group selection put forward by Gerald Edelman, the American biologist who shared the Nobel Prize in Physiology or Medicine in 1972 (Edelman, 1987). According to this theory, during early and neonatal development neurons form somewhat malleable connections based on genetic (i.e., internal) signals. Thereafter, throughout life, these networks dynamically and continuously create different combinations of functional neuronal connections – called neural networks, or somatically formed groups – by functionally pruning or reinforcing the strength of their connections. This remodeling is determined by the level of activity of the neural connections as they are constantly responding to internal and external stimuli. Finally, which neural groups fire together determines the overall shape of these networks. In terms of behavior, the basic functional unit of the brain is therefore not a neuron, but instead a group of connections among neurons that tend to fire together. Thus, fully understanding the mechanisms at play during vagus nerve stimulation requires thinking at the level of systems, as well as of subcellular components.

References

  1. Book
    1. Edelman GM
    (1987)
    Neural Darwinism: The Theory of Neuronal Group Selection
    New York: Basic Books.

Article and author information

Author details

  1. V Reggie Edgerton

    V Reggie Edgerton is in the Department of Integrative Biology and Physiology at the University of California, Los Angeles, United States

    For correspondence
    vre@ucla.edu
    Competing interests
    V Reggie Edgerton holds shareholder interest in NeuroRecovery Technologies and holds certain inventorship rights on intellectual property licensed by The Regents of the University of California to NeuroRecovery Technologies and its subsidiaries.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6534-1875
  2. Parag Gad

    Parag Gad is in the Department of Integrative Biology and Physiology at the University of California, Los Angeles, United States

    For correspondence
    paraggad@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8352-7614

Publication history

  1. Version of Record published: March 16, 2018 (version 1)

Copyright

© 2018, Edgerton 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

  • 3,990
    Page views
  • 202
    Downloads
  • 8
    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)

  1. V Reggie Edgerton
  2. Parag Gad
(2018)
Spinal Cord Injury: Is the vagus nerve our neural connectome?
eLife 7:e35592.
https://doi.org/10.7554/eLife.35592

Further reading

    1. Neuroscience
    Nataliia Kozhemiako et al.
    Research Article

    Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key non-rapid eye movement (NREM) sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.

    1. Medicine
    2. Neuroscience
    Guido I Guberman et al.
    Research Article

    Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.

    Methods: Using cross-sectional data from 306 previously-concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.

    Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.

    Conclusions: Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity.

    Funding: financial support for this work from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (GIG), an Ontario Graduate Scholarship (SS), a Restracomp Research Fellowship provided by the Hospital for Sick Children (SS), an Institutional Research Chair in Neuroinformatics (MD), as well as a Natural Sciences and Engineering Research Council CREATE grant (MD).