Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

Gaurav Sharma, David A Friedenberg, Nicholas Annetta, Bradley Glenn, Marcie Bockbrader, Connor Majstorovic, Stephanie Domas, W Jerry Mysiw, Ali Rezai, Chad Bouton, Gaurav Sharma, David A Friedenberg, Nicholas Annetta, Bradley Glenn, Marcie Bockbrader, Connor Majstorovic, Stephanie Domas, W Jerry Mysiw, Ali Rezai, Chad Bouton

Abstract

Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

Conflict of interest statement

Authors declare competing interests as they are employed by institutions that provided funding for this work and/or have filed associated patents.

Figures

Figure 1
Figure 1
(a) Experimental neural bypass system in use with the participant (seated in a wheelchair) in front of a table with a computer monitor. The neuromuscular electrical stimulation sleeve is wrapped around the participant’s forearm; (b) Close up view of the participant’s hand showing the colored cots on the fingers. An additional cot was placed on a plastic cylinder extending out past the participant’s thumb (referred to as the “sixth finger”). A stereo camera was positioned above the participant’s hand to track movement in 3 dimensions. The color of the cot on the thumb was used to identify thumb movement and locate it in three-dimensional space with the “sixth finger” serving as the reference point; (c) Examples of rasters histograms from one discriminated unit (channel 7 unit 1) with response to attempted thumb movements. The participant was presented with cues to attempt thumb flexion, thumb extension, and rhythmic thumb wiggle. Each cue was presented for a random duration of 2.5–4.5 s followed by a random 2.5–4.5 s rest period. We presented 6 trials of each in random order. Each dot in the raster represents a spike, and each row of spikes represents data from one trial. All trials were aligned on cue presentation (time 1 s, red dashed line). (d) Wavelet processed neural data from channel 7 corresponding to the same movements in panel c showing the mean wavelet power (MWP) averaged over the trials in dark blue with a confidence interval of ±1 S.D. around the mean shown in grey.
Figure 2
Figure 2
(a) A two-neuron oscillator based on the Matsuoka neural model. Neural decoder output corresponding to the rhythmic thumb wiggle was fed as an input to the neuron oscillator model. The output from the oscillator is linked to NMES for stimulating the extensor and flexor muscles controlling the thumb movement; (b) Output of the neural oscillator, yout, showing oscillatory behavior with a frequency of oscillation at 2.5 Hz.
Figure 3. Neural activity is distinct for…
Figure 3. Neural activity is distinct for imagined rhythmic movement.
(a) Neural firing pattern (as represented by the average normalized MWP power for each move) overlaid on the physical layout of the electrode array shows changes in neural activity while imagining rhythmic/CPG wiggle compared to discrete flexion and extension. The normalized mean wavelet power is in units of standard deviations away from a baseline non-movement period of rest.
Figure 4. Correlation between neural activity and…
Figure 4. Correlation between neural activity and cue.
(a,b) Correlation coefficient between neural activity and cue vector was calculated for each motion for each array channel and shows the change in modulation levels on certain channels when imagining wiggle compared to flexion and extension. (c) Modulation in neural activity on a few representative channels clearly shows distinct neural modulation on these channels when imagining rhythmic wiggle compared to discrete flexion and extension of the thumb and wrist. Data shown ranges from 0.3 seconds before the cue to 2.5 s after the cue ended.
Figure 5. CPG task performance.
Figure 5. CPG task performance.
(a) Subfigures show heat maps of MWP representing processed signals from the microelectrode array, neural decoder output scores (dashed line), and physical thumb movements (solid line), as detected by a computer-based video classification algorithm. Of the three simultaneously running decoders, the output score from the one with the highest amplitude greater than zero was used to turn on/off the stimulation. The vertical white line marks when the decoder crosses zero and the stimulation is turned on. MWP increases during stimulation due to residual stimulation artifact (see Methods). The MWP plot is therefore split into two areas with two different color scales (one each for pre- and post-stimulation patterns) to facilitate better visualization of these patterns. The neural decoders are robust to any stimulation induced effects as they not only recognize the correct imagined movement to initiate stimulation but also continue to recognize the participant’s desire to sustain and subsequently terminate the stimulation-induced movement. Trial data shown ranges from 0.3 s before the cue to 2.5 s after the cue ended. A 1 s boxcar filter is applied to the MWP data causing a delay of ~0.5 s. The MWP is in units of standard deviations away from a baseline non-movement period. Only decoder outputs greater than zero are shown for visual clarity; (b) shows the results from finger motion tracking for each motion: thumb flexion (F), thumb extension (E), thumb wiggle (W) and a rest (R) in between each motion obtained from the 3D stereo camera mounted overhead. The output is measured as the distance of the finger from a reference point (“sixth finger”, see Methods).

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