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Epidural stimulation of the cervical spinal cord for post-stroke upper-limb paresis

Nature Medicine volume 29, pages 689–699 (2023)Cite this article

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Abstract

Cerebral strokes can disrupt descending commands from motor cortical areas to the spinal cord, which can result in permanent motor deficits of the arm and hand. However, below the lesion, the spinal circuits that control movement remain intact and could be targeted by neurotechnologies to restore movement. Here we report results from two participants in a first-in-human study using electrical stimulation of cervical spinal circuits to facilitate arm and hand motor control in chronic post-stroke hemiparesis (NCT04512690). Participants were implanted for 29 d with two linear leads in the dorsolateral epidural space targeting spinal roots C3 to T1 to increase excitation of arm and hand motoneurons. We found that continuous stimulation through selected contacts improved strength (for example, grip force +40% SCS01; +108% SCS02), kinematics (for example, +30% to +40% speed) and functional movements, thereby enabling participants to perform movements that they could not perform without spinal cord stimulation. Both participants retained some of these improvements even without stimulation and no serious adverse events were reported. While we cannot conclusively evaluate safety and efficacy from two participants, our data provide promising, albeit preliminary, evidence that spinal cord stimulation could be an assistive as well as a restorative approach for upper-limb recovery after stroke.

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Fig. 1: Experimental setup and stimulation arrangement.
Fig. 2: Optimized continuous stimulation protocols.
Fig. 3: SCS immediately improved strength.
Fig. 4: SCS immediately improved arm kinematics.
Fig. 5: Muscle activation pattern during planar movement.
Fig. 6: SCS improved function.

Data availability

Anonymized data will be uploaded to the Database Archive of the BRAIN Initiative (DABI, https://dabi.loni.usc.edu/home). Source data are provided with this paper.

Code availability

All software used to produce the figures in this manuscript will be available upon reasonable request to the corresponding author.

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Acknowledgements

We thank J. Ruby for the design of figure elements in Figs. 1, 3 and 4. We thank T. Simpson for engineering support. The study was executed through the support of National Institutes of Health Brain Initiative grant no. UG3NS123135-01A1 to M.C. and D.J.W. and internal funding from the Department of Neurological Surgery at the University of Pittsburgh to M.C., the Department of Mechanical Engineering and the Neuroscience Institute at Carnegie Mellon University to D.J.W. and the Department of Physical Medicine and Rehabilitation at the University of Pittsburgh to E.P.

Author information

Author notes

  1. These authors contributed equally: Marc P. Powell, Nikhil Verma, Erynn Sorensen.

  2. These authors jointly supervised this work: Elvira Pirondini, Douglas J. Weber, Marco Capogrosso.

Authors and Affiliations

  1. Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Marc P. Powell, Erynn Sorensen, Daryl P. Fields, Souvik Roy, Jeffrey Balzer, Robert M. Friedlander, Peter C. Gerszten & Marco Capogrosso

  2. Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA

    Marc P. Powell, Erynn Sorensen, Erick Carranza, Amy Boos, Daryl P. Fields, Souvik Roy, Scott Ensel, Beatrice Barra, George F. Wittenberg, Lee E. Fisher, Elvira Pirondini & Marco Capogrosso

  3. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Nikhil Verma & Douglas J. Weber

  4. NeuroMechatronics Lab, Carnegie Mellon University, Pittsburgh, PA, USA

    Nikhil Verma & Douglas J. Weber

  5. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Erynn Sorensen, Erick Carranza, Scott Ensel, Lee E. Fisher, Elvira Pirondini & Marco Capogrosso

  6. Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA

    Erick Carranza, Scott Ensel, Lee E. Fisher & Elvira Pirondini

  7. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA

    Amy Boos & George F. Wittenberg

  8. Department of Biostatistics, Columbia University, New York, NY, USA

    Jeff Goldsmith

  9. Veterans Affairs HS, Pittsburgh, PA, USA

    George F. Wittenberg

  10. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Lee E. Fisher

  11. Department of Neurology, Johns Hopkins University, Baltimore, MD, USA

    John W. Krakauer

  12. Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA

    John W. Krakauer

  13. Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA

    John W. Krakauer

  14. The Santa Fe Institute, Santa Fe, NM, USA

    John W. Krakauer

  15. The Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Douglas J. Weber

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Contributions

M.C., D.J.W. and E.P. conceived the study. M.C., D.J.W. and E.P. secured the funding. M.C., E.P., J.W.K. and D.J.W. designed the experiments and assessments. M.P.P., D.J.W., N.V. and E.S. designed and implemented the stimulation control system, and the hardware and software framework for the experiments. E.C. and E.P. designed and implemented the KINARM motor tasks. S.E. and E.P. designed and performed the MRI-based analyses. A.B. and G.F.W. implemented patient recruitment, eligibility and monitoring and coordinated management of the study. M.C., L.E.F. and P.C.G. designed the neurosurgical approach. P.C.G., D.P.F. and R.M.F. implemented and evaluated the neurosurgical procedures and participants’ clinical management protocols. J.B., M.P.P. and M.C. designed and performed the intraoperative monitoring protocols. M.P.P., N.V., E.S., E.C., M.C., E.P., S.R., B.B., A.B. and J.W.K. performed the experiments. J.G. designed the statistical data analyses. M.P.P., N.V., E.S., E.C. and S.R. analyzed the data. M.P.P., E.S., E.C. and N.V. created the figures. M.C., M.P.P., E.P., D.J.W., E.S. and N.V. wrote the paper and all authors contributed to its editing.

Corresponding author

Correspondence to Marco Capogrosso.

Ethics declarations

Competing interests

M.P.P., D.J.W., M.C. and P.C.G. are founders and shareholders of Reach Neuro, a company developing spinal cord stimulation technologies for stroke. E.P. has interest in Reach Neuro due to personal relationship with M.C.; M.P.P., D.J.W., M.C., P.C.G., E.P., E.S., N.V. and E.C. are inventors on US patent application number PCT/US2022/043128 related to this work. All other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks D. Sayenko, D. Reinkensmeyer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Lesion characterization and Lead position over time.

(a) sagittal, coronal, and axial T1- weighted MRI 2D projections for SCS01 and SCS02. The segmented lesion is shown in red for both participants. R indicates the Right hemisphere. (b) High-definition fiber tracking of the corticospinal tract (CST) for SCS01 and SCS2. Colored fibers represent estimated CTS axons from the affected (right) and unaffected (left) hemisphere. Significant reduction in number of tracked fibers in the right hemisphere is clear in both participants in consequence of the stroke. (c) Repeated X-rays for SCS01 (left) and SCS02 (right) showing the position of the spinal leads. The red lines mark the same anatomical location across the X-rays to facilitate interpretation. Minimal displacement occurred after initial implantation.

Source data

Extended Data Fig. 2 SCS parameters set using a custom-built controller.

(a) An image of the stimulator (DS8R, left) and 1-to-8 channel multiplexer (D188, right) used to deliver stimulation pulses. (b) An overview of the control scheme used to deliver patterns of stimulation. A PC running a (c) MATLAB based GUI communicated with a microcontroller using a custom (d) communication protocol over a virtual serial port. The microcontroller’s firmware delivered pulse triggers and amplitude control signals to the stimulator as well as an 8 bit parallel channel selection signal to the multiplexer in order to control pulse timing, amplitude, and output channel. Current was delivered from the stimulator through the multiplexer and ultimately to the selected electrode on the implanted spinal array. (c) The GUI interface allowed for configuring all stimulation parameters including active channels, stimulation frequency, pulse train duration (or continuous), pulse train latency, and stimulation amplitude for each active channel. Once configured, stimulation was initiated or terminated via the software interface. The software also allowed for rapid changes in either global stimulation frequency (nudge frequency) or channel amplitude (nudge amplitude). (d) A custom command protocol layer was developed on top of a UART serial interface to enable communication between the GUI and microcontroller. Each packet from the master (PC) to the slave (microcontroller) comprised a 1 byte packet length, 1 byte command, and 0–6 bytes of payload. A payload comprised a 1 byte parameter (to be read or written), a 1 byte channel number (when appropriate), and the value to be written (when ‘write’ command was used). Microcontroller response packets comprised a 1 byte packet length, 1 byte command echo, 0–32 bytes of payload (used to return parameter values during ‘read’ command), and a 1 byte success flag. (e) The microcontroller firmware allowed for pseudo-synchronous stimulation across multiple channels by interleaving pulses on all active channels. A delay of at least 1 ms between each pulse allowed enough time for the multiplexer to fully switch channels. The same pattern of pulses was delivered every period as defined by the stimulation frequency. Each channel could also be configured to deliver a single pulse, a pulse train with finite duration and/or latency, continuous stimulation, or a ‘recruitment curve’ in which the amplitude was gradually increased for successive pulse trains of specified length.

Extended Data Fig. 3 Muscle recruitment curves.

In each panel we show the recruitment curves obtained with stimulation at 1 Hz at increasing current amplitude for 11 arm and hand muscles: TRAP: trapezius, A, P, M DEL: anterior, posterior and medial deltoid respectively, BIC: biceps, TRI: triceps, EXT: Extensor carpi, FLX: flexor carpi, PRO: pronator teres, ABP: abductor pollicis and ADM: abductor digiti minimi. Below each set of recruitment curves we report the graphical representation of the muscle activation obtained at the amplitude indicated on the left of each human figurine. Interpretation of human figurines is reported in the bottom right. Each muscle is colored with a color scale (on the left) representing the normalized peak-to-peak amplitude of EMG reflex responses obtained at the stimulation amplitude indicated on the left. Peak-to-peak values for each muscle are normalized to the maximum value obtained for that muscle across all contacts and all current amplitudes.

Source data

Extended Data Fig. 4 Frequency dependent suppression.

To demonstrate that SCS recruits arm and hand muscles via direct activation of the primary afferents we performed stimulation at multiple frequencies. The figure reports the spinal reflexes obtained when stimulating at 1, 5, 10 and 20 Hz from multiple contacts and multiple muscles. Each plot on the top shows the normalized peak-to-peak reflex amplitude as a function of frequency showing in the muscles that respond to the specific contact substantial frequency dependent suppression at stimulation frequencies greater than 10 Hz. On the bottom, we report raw EMG traces that show the classic phenomenon. At 5 Hz each pulse of stimulation corresponds to a clear evoked potential in the EMG albeit amplitude slightly diminishes at each pulse. At 10 Hz, modulation of peak-to-peak amplitudes becomes more evident, at 20 Hz almost complete suppression of EMG evoked responses subsequent to the first is shown. Example is taken from Pronator muscles, contact 1 C, (highlighted in darker grey in the top panel).

Source data

Extended Data Fig. 5 SCS improves arm kinematics supplementary metrics.

(a) Effect of stimulation frequency shown for SCS01 and SCS02. In SCS01, quantification of isometric torques during single joint flexion and extension is shown for the elbow during no stim (dark grey), 20 Hz (blue), 40 Hz (blue), and 60 Hz (blue). In SCS02, maximum reached distance and elbow angle excursion (max-min) are reported during reach and pull of the reach-out task for no stim (dark grey), 20 Hz (blue), 40 Hz (blue), and 60 Hz (blue). Raw endpoint trajectories for SCS02 are shown in the reach out task during no stim (dark grey), 20 Hz (blue), 40 Hz (blue), and 60 Hz (blue) where SCS02 was tasked to reach beyond the third horizontal line to complete the task. Reach (solid line) and pull (dashed line) trajectories are represented in separate plots. Darker lines represent average trajectories, shaded lines represent single trajectories. (b) Quantification of kinematic features for SCS01, path length for completed reach and pull of three targets in cm and variance of the path between trials are reported for no-stim (dark grey) and stim condition (blue). Center target could not be calculated for no-stim condition because SCS01 did not complete the task. (c) Quantification of kinematic features for SCS02, movement smoothness (velocity peaks) and path length in cm for reach and pull separately are reported for no-stim (dark grey) and stim condition (blue). The distribution of deviations from the mean path trajectory is shown in cm (equivalent to variance in SCS01). Statistics Distributions of deviations were compared using a two-sample Kolmogorov-Smirnov non-parametric test with alpha=0.05 where p~=0 (where the value was smaller than able to be stored in a double precision variable). All other quantifications are reported using box-plots. For each box, the central circle indicates the median while the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the minima and maxima data points, not considering outliers. Any outliers are plotted individually with additional circles. Inference on mean differences is performed by bootstrapping the n = 5 repetitions obtained for each measurement, with n = 10,000 bootstrap samples, and by using a Bonferroni correction when performing multiple comparisons; * indicates statistical significance and rejection of the null hypothesis of no difference with a 95% confidence interval.

Source data

Extended Data Fig. 6 Optimized SCS leads to best improvement.

(a) Quantification of isometric torques during single joint flexion and extension of the elbow during no stim (dark grey), non-optimal stim (light blue), and optimal stim (blue) for SCS01. (b) Quantification of performance for three targets of the center-out task during no stim (dark grey), non-optimal stim (light blue), and optimal stim (blue) normalized from 0 (SCS02 never reached target) and 1 (SCS02 reached target in all trials). n = 3 (c-e) Raw endpoint trajectories by SCS02 for three targets of the center-out task during no stim (dark grey), non-optimal stim (light blue), and optimal stim (blue). Darker lines represent average trajectories, shaded lines represent single trajectories. Statistics For quantifications reported using box-plots, the central circle indicates the median while the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the minima and maxima data points, not considering outliers. Any outliers are plotted individually with additional circles. Inference on mean differences for (a) were performed by bootstrapping the n = 5 repetitions obtained for each measurement, with n = 10,000 bootstrap samples, and by using a Bonferroni correction when performing multiple comparisons; * indicates statistical significance and rejection of the null hypothesis of no difference with a 95% confidence interval.

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Extended Data Table 1 Modified Ashworth Scale longitudinal breakdown
Full size table
Extended Data Table 2 Fugl-Meyer assessment longitudinal breakdown
Full size table

Supplementary information

Supplementary Information

Supplementary Methods (surgical procedure, custom stimulation controller, EMG acquisition, figure generation) and Table 1.

Reporting Summary

Supplementary Video 1

SCS01 opening her fingers and abducting her shoulder with and without stimulation. The video demonstrates that SCS immediately improved range of motion for these tasks.

Supplementary Video 2

SCS01 performing the box and blocks task with and without stimulation demonstrating that SCS immediately improved task performance.

Supplementary Video 3

SCS01 lifting a soup can with and without stimulation demonstrating that SCS immediately improved her ability to supinate her wrist and grasp the can.

Supplementary Video 4

SCS01 feeding herself with a fork with and without stimulation demonstrating that SCS immediately and functionally improved her ability to perform this task of daily living.

Supplementary Video 5

SCS02 moving a cylinder from one wooden dowel to another demonstrating her ability to successfully complete the task with SCS active.

Source data

Source Data Fig. 1

Statistical source data.

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Source Data Fig. 5

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Source Data Fig. 6

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Source Data Extended Data Fig. 1

Statistical source data (DTI measurements).

Source Data Extended Data Fig. 3

Statistical source data (muscle recruitment curve data).

Source Data Extended Data Fig. 4

Statistical source data (plot data points).

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

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Powell, M.P., Verma, N., Sorensen, E. et al. Epidural stimulation of the cervical spinal cord for post-stroke upper-limb paresis. Nat Med 29, 689–699 (2023). https://doi.org/10.1038/s41591-022-02202-6

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