The spine houses a network of neurons that relays electric signals from the brain cells to the muscles. When the spine is injured, some of these neurons may be damaged and their connections to the muscles broken. As a result, the muscles they command become weak, and movement is impaired. It is possible to strengthen the remaining neural connections with physical rehabilitation, but the results are limited.

Vagus nerve stimulation, VNS for short, is a new technique that could help people recuperate better after their spine is injured. The vagus nerve controls the heart, lungs and guts, and it reports the state of the body to the brain. When this nerve is electrically stimulated, it releases chemicals that can strengthen the neural connections between brain, spine and muscles, and even create new ones. This rewiring process is essential to repair or bypass the broken neural connections caused by a spine injury. However, it is still not clear how best to use VNS to optimize recovery.

Here, Ganzer et al. study how VNS helps rats whose forelimbs are weakened after a spine injury. Three groups of rats go through physical rehabilitation, using their affected front paws to pull a handle and feed themselves. Two of these groups also receive VNS: their vagus nerve is stimulated either after the best trials (strongest pulls) or worst trials (weakest pulls).

Compared to the rehab-only and the worst trials-VNS animals, the rats that receive VNS on the best trials while using their affected paw have many more neuronal connections between their brain and the muscles in this limb. These muscles also become much stronger. VNS during the movement improves recovery whether the rodents have one or two front limbs injured, and the benefits are long lasting.

As the rats pull the handle, the neurons involved in the movement get activated: they then carry a molecular ‘signature’ that lasts for a short time. When VNS is applied during that window, it appears to help these neurons form new connections with each other, as well as strengthen existing ones. These improved connections mean the brain can communicate better with the muscles: movement is enhanced, which results in greater functional recovery compared to rehabilitation alone.

VNS is already trialed in stroke patients, who have weakened muscles because their brain neurons are damaged. The work by Ganzer et al. provides crucial information on how VNS could ultimately improve the recovery and quality of life of people with spine injuries.

Recovery from serious neurological injury requires substantial rewiring of neural circuits. Many methods have been developed to enhance synaptic plasticity in hopes of enhancing recovery. Unfortunately, these methods have largely failed in the clinic likely due to the challenge of precisely targeting specific synapses and absence of testing in clinically-relevant models (Gladstone et al., 2006; Levy et al., 2016). Real-time control of neural activity provides a new avenue to promote synaptic plasticity in specific networks and restore function after injury (Engineer et al., 2011; Nishimura et al., 2013; McPherson et al., 2015; Guggenmos et al., 2013).

Human and animal studies demonstrate that precisely timed vagus nerve stimulation (VNS) can improve recovery of sensory and motor function. VNS engages neuromodulatory networks and triggers release of pro-plasticity factors including norepinephrine, acetylcholine, serotonin, brain-derived neurotrophic factor, and fibroblast growth factor (Hays, 2016; Hulsey et al., 2017; Hulsey et al., 2016). This in turn influences expression and phosphorylation of proteins associated with structural and synaptic plasticity, including Arc, CaMKII, TrkB, and glutamate receptors (Alvarez-Dieppa et al., 2016; Furmaga et al., 2012). Engagement of neuromodulatory networks activates a transient synaptic eligibility trace to support spike-timing-dependent plasticity (STDP) (He et al., 2015), thus raising the prospect that closed-loop neuromodulatory strategies may provide a means to direct specific, long-lasting plasticity to enhance recovery after neurological injury. Indeed, in the absence of neurological damage, repeatedly pairing sensory or motor events with brief bursts of VNS yields robust plasticity in sensory or motor cortex that is specific to the paired experience (Engineer et al., 2011; Hulsey et al., 2016). Moreover, the addition of VNS to rehabilitative training improves recovery in rodent models of unilateral brain injury and in chronic stroke patients, highlighting the clinical potential of closed-loop neuromodulatory strategies (Hays, 2016; Khodaparast et al., 2016; Pruitt et al., 2016; Hays et al., 2014a; Dawson et al., 2016).

We tested the hypothesis that closed-loop VNS (CLV) could be harnessed to enhance recovery after spinal cord injury (SCI). To do so, we developed a real-time closed-loop neuromodulation paradigm based on the synaptic eligibility trace to deliver VNS immediately after the most successful forelimb movements during motor rehabilitation. The strategy uses a control algorithm that adaptively scales stimulation threshold to trigger a brief 0.5 s train of VNS on trials in which pull forces fall within the top quintile of previous trials (Top 20% CLV; Figure 1a,b, and Figure 1—figure supplement 1a). To test the hypothesis that temporal precision is required for VNS-dependent effects, we employed a similar algorithm in which stimulation was delivered on the weakest quintile of trials (Bottom 20% CLV; Figure 1b and Figure 1—figure supplement 1g). Both algorithms deliver the same amount of VNS during rehabilitative training, but Bottom 20% CLV results in a significant delay between VNS and the most successful trials.

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To test whether CLV could improve recovery of motor function after SCI, rats were trained to perform an automated reach-and-grasp task measuring volitional forelimb strength (Figure 1b, Video 1) (Hays et al., 2013). Once proficient, rats received a right unilateral impact at spinal level C6 to impair function of the trained forelimb and underwent implantation of a bipolar cuff electrode on the left cervical vagus nerve (Ganzer et al., 2016a). SCI resulted in a 77% reduction in volitional forelimb strength, consistent with paresis observed in many cervical SCI patients (Figure 1c, PRE v. Wk 8, Paired t-test, t(29) = 37.34, p=4.4×10⁻²⁶, Video 2). Top 20% CLV substantially boosted recovery of volitional forelimb strength compared to equivalent rehabilitative training without CLV (Rehab alone), demonstrating that CLV enhances recovery of motor function after SCI (Figure 1c; Two-way repeated measures ANOVA, Interaction; F[6,120]=3.88, p=1.43×10⁻³; Videos 3–4). CLV resulted in lasting recovery after the cessation of stimulation after week 11, consistent with the notion that CLV restores function in critical motor networks (Top 20% CLV; Wk 11 v. Wk 12; Paired t-test, t(12) = −0.89, p=0.38). Despite equivalent rehabilitation and a comparable number of stimulations delivered during task performance (Figure 1d,e), Bottom 20% CLV resulted in substantially diminished recovery compared to Top 20% CLV (Figure 1c, Two-way repeated measures ANOVA, Interaction; F[6,114]=2.40, p=0.03, Video 5) and failed to improve forelimb strength compared to Rehab alone. Together, these findings demonstrate that closed-loop neuromodulation paired with the most successful movements during rehabilitation improves recovery of motor function after cervical SCI.

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The synaptic eligibility trace theory posits that neuromodulatory reinforcement must occur within seconds after neural activity to drive plasticity (He et al., 2015). To clarify how temporally precise CLV must be, a subset of rats received VNS delayed approximately 1.5 s after the top 50% most successful trials (Delayed Top 20% CLV, Figure 1—figure supplement 1d). This short delay resulted in comparable recovery to stimulation delivered immediately after a successful trial in the Top 20% CLV group (Figure 1f). Stimulation in the Bottom 20% CLV group was separated by 25 ± 5 s from the most successful trials and failed to drive substantial benefits (Figure 1f, Figure 1—figure supplement 1g). This absence of enhanced recovery despite delivery of CLV may be attributed to either the long delay or greater variance in the timing between stimulation and the most successful trials. These findings support a temporal precision limit for CLV near 10 s, consistent with the synaptic eligibility trace hypothesis (He et al., 2015).

To determine whether more pairings of VNS with successful trials would improve recovery, we utilized an adaptive algorithm in which VNS was delivered on at least the top 50% most successful trials, resulting in 2.5 times more stimulation pairings (Top 50% CLV, Figure 1—figure supplement 1j). Top 50% CLV substantially improved recovery of forelimb function compared to Rehab alone, which provides an independent confirmation that CLV enhances recovery after SCI (Figure 2a, Two-way repeated measures ANOVA, Interaction; F[6,174]=3.56, p=2.38×10⁻³). The rate and degree of recovery were comparable in the Top 50% CLV and the Top 20% CLV groups (Figure 2—figure supplement 1), suggesting that timing is more important than quantity of stimulation.

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Plasticity in remaining networks could be harnessed to support recovery after SCI (Fink and Cafferty, 2016; Manohar et al., 2017). Unilateral SCI resulted in extensive damage to gray matter, rubrospinal pathways, and propriospinal pathways in the right hemicord while largely sparing the right dorsal corticospinal tract (CST) (Figure 2b and Figure 2—figure supplement 2). Thus, we used intracortical microstimulation to test the hypothesis that CLV enhances output from the corticospinal circuits to the impaired forelimb. CLV resulted in eight times more motor cortex sites that generated grasp movements in the impaired forelimb compared to Rehab alone (Figure 2c and Figure 2—figure supplement 4, Unpaired t-test, t(10) = 2.28, p=0.04), providing the first evidence that CLV induces large-scale plasticity in corticospinal networks after neurological injury.

We next tested the hypothesis that CLV improves recovery by increasing synaptic connections within the motor network controlling grasping muscles of the forelimb. We injected the retrograde transsynaptic tracer pseudorabies virus (PRV-152) into flexor digitorum profundus and palmaris longus and counted labeled neurons six days later. CLV resulted in a five-fold increase in labeled neurons in motor cortex compared to Rehab alone (Figure 2d and Figure 2—figure supplement 5, Unpaired t-test, t(9) = 7.63, p=3.2×10⁻⁵). The magnitude of this increase in synaptic connectivity is comparable to the seven-fold increase in the number of motor cortex sites that produce grasp. CLV failed to increase neuronal labeling of spinal motor neurons, red nucleus neurons or propriospinal neurons (Figure 2d). Additionally, CLV did not influence lesion extent (Figure 2e, Figure 2—figure supplement 2). Together, these results are consistent with anatomical plasticity in the spared corticospinal network contributing to enhanced recovery when CLV is added to rehabilitative training after SCI (Figure 3).

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The observation that CLV improves recovery and enhances functional and anatomical plasticity in corticospinal networks suggests that CLV may prove ineffective if the CST is destroyed. Given the severity and anatomical heterogeneity of damage observed in SCI patients (Sekhon and Fehlings, 2001), such a finding would limit the clinical utility of CLV. We therefore evaluated motor recovery in a bilateral injury model that virtually eliminates the CST on both sides of the cord (Figure 4b). Despite profound damage, CLV more than doubled the degree of forelimb motor recovery compared to Rehab alone (Figure 4a, Two-way repeated measures ANOVA, Interaction; F[6,144]=5.29, p=7.62×10⁻⁵). The observation that CLV can improve recovery following bilateral SCI suggests CLV could be clinically useful. We hypothesized that CLV enhances recovery by promoting plasticity in the rubrospinal and propriospinal pathways, which were damaged, but not eliminated, by this injury (Figure 4b and Figure 4—figure supplement 1). Indeed, CLV doubled the number of labeled red nucleus neurons and C3/4 propriospinal neurons compared to Rehab alone (Figure 3d and Figure 4—figure supplement 3, Unpaired t-test, Red Nucleus: t(4) = 3.89, p=0.018; Propriospinal: t(4) = 2.77, p=0.05). Consistent with the extensive damage to the corticospinal pathway, CLV had no effect on reorganization of motor cortex (Figure 4c and Figure 4—figure supplement 4, Unpaired t-test, t(12) = −0.13, p=0.90) and failed to increase the number of labeled neurons in the motor cortex (Figure 3d, Unpaired t-test, t(4) = 0.83, p=0.45). These results suggest that CLV is capable of supporting recovery following SCI by strengthening anatomical connectivity within remaining pathways (Figure 4—figure supplement 5).

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In this study, we developed a novel closed-loop neuromodulation strategy to make use of the high temporal precision of the synaptic eligibility trace. We demonstrate that activation of the vagus nerve improves recovery when reliably delivered within seconds of a successful movement, and we provide the first evidence that CLV enhances reorganization of synaptic connectivity in remaining networks in two non-overlapping models of SCI. The flexibility to promote reorganization in a range of pathways is a critical benefit of CLV, given the great heterogeneity in the etiology, location, and extent of damage present in SCI patients.

Classical studies by Skinner demonstrate that adaptive reinforcement of successive approximations, or shaping, drives behavior toward a desired response (Skinner, 1953). This principle has been adopted for use in rehabilitation, with the intention to reinforce successively better movements (Wood, 1990). We made use of this concept by applying an adaptively-scaled stimulation threshold to deliver CLV with the most successful forelimb movements during rehabilitation. Enhanced recovery was observed only when CLV was paired with trials that approximated the desired outcome, highlight the importance of timing for closed-loop stimulation to shape behavioral outcomes and maximize recovery.

The magnitude of neuromodulatory activation elicited by an event is directly proportional to the surprise, or unpredictability, of the event (Hangya et al., 2015; Hollerman and Schultz, 1998; Sara and Segal, 1991). This phenomenon is ascribed to reward prediction error (Schultz, 2002). Unsurprising events fail to activate neuromodulatory systems, and even rewarding events fail to trigger neuromodulator release if they are expected. We posit the predictability and accompanying tedium of long, frustrating rehabilitation and the minimal reinforcement of practicing a previously simple motor task blunts plasticity and limits recovery after SCI. The closed-loop neuromodulation strategy developed here circumvents this by artificially engaging neuromodulatory networks and providing a repeated, non-adapting reinforcing signal typically associated with surprising consequences (Hays, 2016; Hulsey et al., 2017; Hulsey et al., 2016). CLV drives temporally-precise neuromodulatory release to convert the synaptic eligibility trace in neuronal networks that generate optimal motor control to long-lasting plasticity (He et al., 2015).

CLV is a minimally-invasive, safe strategy to provide precisely-timed engagement of multiple neuromodulatory networks to boost plasticity during rehabilitation (Hays, 2016). Preliminary results in chronic stroke and tinnitus patients highlight the clinical potential of CLV, while delivering less than 1% of the total FDA-approved amount of stimulation (Dawson et al., 2016; De Ridder et al., 2014; Ben-Menachem, 2001). Moreover, the flexibility to deliver stimulation with a variety of rehabilitative exercises raises the possibility to design CLV-based to target motor dysfunction of the lower limbs, somatosensory loss, and bowel and bladder issues, all of which are prevalent in SCI patients. Delineation of the timing requirements and documentation of neuronal changes driven by CLV in this study provide a framework for development of this strategy for a range of neurological conditions, including stroke, peripheral nerve injury, and post-traumatic stress disorder (Hays, 2016; Lozano, 2011).

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Author details

1. Patrick D Ganzer

   1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
   2. Texas Biomedical Device Center, Richardson, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration

   For correspondence

   patrick.ganzer.neuro@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4260-1624

2. Michael J Darrow

   Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Eric C Meyers

   1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
   2. Texas Biomedical Device Center, Richardson, United States

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. Bleyda R Solorzano

   Texas Biomedical Device Center, Richardson, United States

   Contribution

   Supervision, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Andrea D Ruiz

   Texas Biomedical Device Center, Richardson, United States

   Contribution

   Supervision, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Nicole M Robertson

   Texas Biomedical Device Center, Richardson, United States

   Contribution

   Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

7. Katherine S Adcock

   School of Behavioral Brain Sciences, The University of Texas at Dallas, Richardson, United States

   Contribution

   Supervision, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

8. Justin T James

   Texas Biomedical Device Center, Richardson, United States

   Contribution

   Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

9. Han S Jeong

   Texas Biomedical Device Center, Richardson, United States

   Contribution

   Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

10. April M Becker

    Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, United States

    Contribution

    Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2706-1711

11. Mark P Goldberg

    Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, United States

    Contribution

    Supervision, Funding acquisition

    Competing interests

    No competing interests declared

12. David T Pruitt

    1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
    2. Texas Biomedical Device Center, Richardson, United States

    Contribution

    Software, Visualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8356-0887

13. Seth A Hays

    1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
    2. Texas Biomedical Device Center, Richardson, United States
    3. School of Behavioral Brain Sciences, The University of Texas at Dallas, Richardson, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Writing—original draft, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4225-241X

14. Michael P Kilgard

    1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
    2. Texas Biomedical Device Center, Richardson, United States
    3. School of Behavioral Brain Sciences, The University of Texas at Dallas, Richardson, United States

    Contribution

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

    Competing interests

    has a financial interesting in MicroTransponder, Inc., which is developing CLV for stroke and tinnitus

15. Robert L Rennaker II

    1. Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, United States
    2. Texas Biomedical Device Center, Richardson, United States
    3. School of Behavioral Brain Sciences, The University of Texas at Dallas, Richardson, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    renn@utdallas.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1260-1973

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