Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury

Abstract

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited the therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here we developed stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real-time control software that modulate extensor and flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight-bearing capacity, endurance and skilled locomotion in several rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.

Figure 1. Spatiotemporal activation of hindlimb motoneuron during locomotion

(a) Diagram illustrating tracer injections into the tibialis anterior (TA) muscle to label motoneurons. Top–down and coronal snapshots of 3D lumbosacral reconstructions; each TA motoneuron is represented by a single dot. The same procedure was applied to gluteus medius (GM), illiopsoas (IL), vastus lateralis (VL), semi–tendinosus (St), biceps femoris (BF), gastrocnemius medialis (MG), gastrocnemius lateralis (LG), extensor digitorum longus (EDL), and flexor hallucis longus (FHL) muscles. The rostrocaudal location and center (white square) of each reconstructed motoneuron column is indicated in red and blue for muscles acting functionally for extension versus flexion, respectively. (b) Muscle activity during locomotion in an intact rat. (c) The muscle activity was projected onto the motoneuron location matrix to elaborate the spatiotemporal map of motoneuron activation. The spatially restricted hotspots emerging during gait were extracted by applying a Gaussian cluster algorithm onto the map. (d) Mean (thick lines) and individual (thin lines, n = 7 rats) temporal activation profiles of muscle synergies (Syn 1–4), and histogram plots reporting muscle weighting for each rat (vertical bars) and muscle on each muscle synergy. (e) Spatiotemporal maps of muscle synergy activation, elaborated by representing the temporal activation profiles onto the weighted motoneuron matrix. (f) Model of spinal segments depicting the temporal sequence underlying the recruitment of muscle synergies, and the corresponding activation of extensor and flexor hotspots. (g) Illustration of the timing underlying muscle synergy activation, as captured in the spatiotemporal trajectory of the hindlimb endpoint.

Figure 2. Design, fabrication and validation of spatially selective spinal implants

(a) An optimization algorithm identified the cost to preferentially activate dorsal roots projecting to extensor versus flexor hotspots. The Gaussian curves display the spatial distribution of each hotspot. The dorsal roots were reconstructed in 3D (n = 3 rats). Computer simulations were iterated over a grid of electrodes covering the targeted neural structures. The black circles indicate the optimal locations of electrodes. (b) Computer simulations showing isopotential 1 V surface following a 150 μA stimulation at each identified electrode location, including the resulting dorsal root activation. (c) Spatial maps of spinal segment activation for the optimal electrodes predicted by computer simulations and (d) experimental validation in anesthetized rats (n = 4). The map is computed from motor responses recorded from eight hindlimb muscles, as described previously (Fig. 1). (e) Photographs, including zooms on electrodes and connector, showing spinal implants. The 3D rendering was reconstructed from high–resolution micro–computed tomography (μCT) scans performed after 5 weeks of implantation. (f) Under suspended conditions, a series of four bursts (40 Hz) was delivered through the electrodes targeting each hotpot. The resulting stick diagram decomposition of hindlimb movements is shown for the ipsilateral (color) and contralateral (black) side to the stimulation, together with histogram plots reporting the vertical displacement of the hindlimb endpoint (ANOVA one–way, n = 6, mean and SEM. *, P < 0.05; **, P < 0.01; ***, P < 0.001)

Figure 3. Software to adjust spatiotemporal neuromodulation in real–time during locomotion

Computational platform to trigger adjustments of the temporal structure, spatial configuration and stimulation parameters of the neuromodulation therapies. Rats were supported bipedally in a robotic system provided vertical support during locomotion onto a motorized treadmill belt. A high–resolution video system allowed real–time monitoring of the left and right hindlimb endpoints (feet). The angular displacements of hindlimb endpoints around a calculated center of rotation were converted into angular coordinates, as indicated with the dotted grey lines. The on and off states of electrodes targeting extensor– and flexor–related hotspots were triggered when the angular coordinates crossed user–defined thresholds, personalized for each rat. The stimulation profile module enabled tuning the amplitude and frequency of stimulation based on the therapist or control policies. The diagram represents the relationship between the vertical displacement of the foot and the activation of extensor and flexor hotspot, and how the spatially selective electrodes were turned on and off to replicate this activation pattern.

Figure 4. Spatiotemporal neuromodulation reproduces the natural pattern of motoneuron activation

(a) Rats receive a complete SCI at the thoracic level (T8) and a spinal implant with conventional midline electrodes (black) and spatially selective lateral electrodes (blue and red). (b) Locomotion was recorded on a treadmill without stimulation, with continuous neuromodulation applied over the midline of lumbar and sacral segments, and during spatiotemporal neuromodulation. For each condition (same rat, n = 5) and intact rats (n = 3), a stick diagram decomposition of left hindlimb movement is shown together with successive trajectories of the hindlimb endpoint, the velocity and orientation of the foot trajectory at toe off (vector with arrowheads), the stance (dark gray), drag (dark red) and swing phases (light gray) of both hindlimbs, and vertical ground reaction forces during a continuous sequence of steps. The horizontal bars (blue, red, black) indicate the current state of the electrodes. The corresponding spatiotemporal maps of motoneuron activation were calculated over ten consecutive steps. Conventions are the same as previously (Fig. 1). (c) Left, PC analysis applied on 137 gait parameters with all the gait cycles recorded in rats (n = 5) under the different conditions of neuromodulation are represented in a PC space. Right, histogram plots report mean peak amplitude of vertical ground reaction forces and the mean peak velocity of the foot during swing for the different neuromodulation conditions and for intact rats. (Kurskal Wallis, mean and SEM. *, P < 0.05; **, P < 0.01)

Figure 5. Selective and gradual modulation of extension and flexion components

(a) Locomotor sequences recorded during spatiotemporal neuromodulation in an injured rat with different levels of stimulation amplitudes for the electrode targeting the extensor (left) versus flexor (right) hotspot. A representative stick diagram decomposition of hindlimb movement is shown for each condition. The colors correspond to the period during which the modulated electrode is turned on. Below, the electromyographic activity of extensor and flexor ankle muscles is displayed together with the changes in hindlimb length for a series of steps. The upper right diagram explains the calculation of the hindlimb length, which combines changes over multiple joints of the hindlimb. The spatiotemporal pattern of stimulation is shown at the bottom. The height of the bars is proportional to the stimulation amplitude. (b) Histogram plots reporting the mean vertical ground reaction forces measured during stance while modulating the amplitude of the extension electrode, and the mean step height measured during swing while modulating the flexion electrode (One–way ANOVA, n = 5, mean and SEM. *, P < 0.05.). (c) Vertical foot displacements during locomotion under different stimulation frequency adjusted over both extension and flexion electrodes. The dots highlight the step height. Each pulse of stimulation is represented in the spatiotemporal patterns of stimulation shown at the bottom. (d) The plot displays the relationships between the stimulation frequency and the step height for all the rats together and each rat individually (thin line, n = 6).

Figure 6. Spatiotemporal neuromodulation improves motor control after clinically relevant SCI

(a) Rats received a contusion at T8. (b) Locomotion was recorded 14 days after injury on a treadmill. Continuous neuromodulation led to rapid fatigue, but spatiotemporal neuromodulation instantaneously restored locomotion. (c) Histogram plots reporting the maximum weight–bearing capacities, maximal vertical ground reaction forces, and amplitude of extensor muscle activity measured in the same rats during continuous versus spatiotemporal neuromodulation (paired t–test, n = 6, *, P < 0.05, **, P < 0.01). (d) Left, successive step heights measured during a sequence recorded in the same rat under both paradigms. The horizontal line indicates the mean of step height measured in intact rats (n = 3). The shaded area reports the SEM. Right, bar plots report the normalized duration of locomotion (paired t–test, n = 6, P < 0.01). (e) Rats were recorded during quadrupedal locomotion at two months post–SCI. Parameters related to trunk dynamics were computed to obtain an index quantifying trunk stability relative to intact rats. The improvement of this index is shown for each rat individually (paired t–test, n = 6, P < 0.01). (f) Stick diagram decomposition of hindlimb and trunk movements together with hindlimb endpoint trajectory during staircase climbing under spatiotemporal neuromodulation. The diagrams report the percent of tumbles, touches and passes over the steps under both conditions. The percent of successful passes over the step is shown for each rat individually (paired t–test, n = 6, P < 0.01).