Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Oct;10(5):056008.
doi: 10.1088/1741-2560/10/5/056008. Epub 2013 Aug 8.

Real-time control of walking using recordings from dorsal root ganglia

B J Holinski  1 , D G Everaert, V K Mushahwar, R B Stein
Affiliations

Real-time control of walking using recordings from dorsal root ganglia

B J Holinski et al. J Neural Eng. 2013 Oct.

Abstract

Objective: The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components.

Approach: In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback.

Main results: Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt.

Significance: Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Experimental overview. Sensory signals recorded from video recordings of the hind limb and from gyroscopes and force plates are recorded by the Cerebus system. Within the spinal cord, Utah arrays are implanted in the dorsal root ganglia and an ISMS array is implanted. Sensory recordings from the Utah arrays are sorted and recorded by the Cerebus. Training and real-time prediction was done in Matlab from streaming recordings from the Cerebus. The Matlab algorithm controlled an ISMS stimulator to produce unilateral stepping-like movements.
Figure 2
Figure 2
Single trial predictions during passive stepping-like movement in Cat E. Recorded signals are in blue and real-time predictions of the signal are in red. A) Motion capture of the right hind limb every 120 ms. B) Distance from the hip to the limb endpoint. C) Angle of the vector connecting hip to limb endpoint (measured clockwise from horizontal forward). D) Integrated gyroscope signal. E) Supportive force in % body weight. Predictions of external sensor signals are possible using recordings from the dorsal root ganglia.
Figure 3
Figure 3
Single neuron recordings without and with artifact rejection in Cat D. Without artifact rejection: A) Multiple waveforms identified as a single unit are overlaid with the average waveform shown in black. Large amplitude stimulation artifacts are also erroneously identified as the single unit. B) A histogram of interspike intervals (ISI) shows large numbers of events occurring at harmonics of the stimulation frequency (16 ms). C) A raster plot of all channels with neural data. Each channel is plotted on a separate horizontal line. Each small vertical line represents detection of an action potential on an electrode. Gray lines are unsorted units while colored lines represented signals accepted for processing. Due to stimulation artifacts so many events are accepted that it appears as a blue block. The channel of the single unit shown is A) and B) is highlighted with an arrow. D), E), and F) are similar except they show the same single unit with artifact rejection. Artifacts have been successfully removed from the waveform, ISI histogram and the raster plot and are not accepted into the prediction algorithm.
Figure 4
Figure 4
Single trial predictions during ISMS stepping in Cat E. Recorded signals are in blue and real-time predictions of the signal are in red (similar format to Figure 2). All external sensor signals can be predicted in the presence of ISMS.
Figure 5
Figure 5
The individual single units used in a prediction of limb tilt in Cat E. The training column (panel A) shows the recorded tilt during the training trial (blue) and the training fit (green). Below are the firing rates of the five most correlated units used in the prediction. The testing column (panel B) shows the recorded tilt (blue) and the real-time prediction (red) from the same trial as Figure 4. Again, firing rates for the same units are shown for the prediction trial. The waveforms during training and prediction of the selected units are shown in panel C, respectively for each unit during training and testing. Functionality of each unit is listed under the waveform and was determined during a separate identification process. From training to prediction waveforms and firing rates are stable.
Figure 6
Figure 6
The average VAF when predicting each external sensor signal during open loop trials. Error bars represent the standard deviation of the predictions. Significance differences in means between sensor types are shown with a star (P<0.001). N: distance, tilt=47; gyro, force=34.
Figure 7
Figure 7
The prediction quality (VAF%) of each external sensor signal over time after initial training. Each sensor signal is shown in a different shade of gray (from 1 to 8 trials after training). Linear trend lines are dashed and matched to their respective sensor signal.
Figure 8
Figure 8
The effect of stimulation frequency on prediction quality (VAF%) for each sensory signal during open loop trials. Bars represent the standard deviation of the predictions. Significant differences in mean VAF% between stimulation frequencies for each sensor are shown with a star (P<0.001). 0 Hz is passive movement of the hind limb, 31 Hz is the lower stimulation frequency tested and 62 Hz is the higher stimulation frequency. Passive distance and tilt: N=16, gyro: 7, force: 11; 31 Hz distance and tilt: N=13, gyro and force= 15; 62 Hz distance and tilt: N=47, gyro and force= 34.
Figure 9
Figure 9
Activation based on predicted sensory input of a closed loop rule to limit backward hyperextension in Cat E. Recorded hind limb tilt is shown in blue and the real-time predicted tilt is shown in red. The activation threshold of the rule is shown with a black horizontal dotted line. Gray vertical regions represent times within the step cycle (corresponding to the underlying state system of the controller) when a rule can activate if the threshold is exceeded (activation windows). In panel A, the prediction (red) did not exceed the threshold during the activation windows. Panel B shows that a lower threshold causes many activations of the rule. The solid vertical line marks a rule activation and subsequent state transition to swing. Due to the underlying state transition, the activation window is truncated based on sensory feedback and appears irregular across steps.
Figure 10
Figure 10
The effect of treadmill speed on activating the rule for backward hyperextension in Cat D (format similar to Figure 9). Each panel shows a trial at a different belt speed: 0.1 m/s (panel A, slow), 0.15 m/s (panel B, medium), 0.20 m/s (panel C, fast). The faster the belt speed the more the limb hyperextends and the more likely the rule will activate and begin the swing phase (thus truncating the activation window with the state transition). The threshold levels (horizontal black lines) were constant between belt speeds.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Aoyagi Y, Stein RB, Branner A, Pearson KG, Normann RA. Capabilities of a penetrating microelectrode array for recording single units in dorsal root ganglia of the cat. J Neurosci Methods. 2003;128:9–20. - PubMed
    1. Bamford JA, Putman CT, Mushahwar VK. Intraspinal microstimulation preferentially recruits fatigue-resistant muscle fibres and generates gradual force in rat. J Physiol. 2005;569:873–84. - PMC - PubMed
    1. Bauman MJ, Bruns TM, Wagenaar JB, Gaunt RA, Weber DJ. Online feedback control of functional electrical stimulation using dorsal root ganglia recordings. Conf Proc IEEE Eng Med Biol Soc. 20112011:7246–9. - PMC - PubMed
    1. Branner A, R A Normann. A multielectrode array for intrafascicular recording and stimulation in sciatic nerve of cats. Brain Res Bull. 2000;51:293–306. - PubMed
    1. Brown-Triolo DL, Roach MJ, Nelson K, Triolo RJ. Consumer perspectives on mobility: implications for neuroprosthesis design. J Rehabil Res Dev. 2002;39:659–69. - PubMed

Publication types