Transformation of nonfunctional spinal circuits into functional states after the loss of brain input

Abstract

After complete spinal cord transections that removed all supraspinal inputs in adult rats, combinations of serotonergic agonists and epidural electrical stimulation were able to acutely transform spinal networks from nonfunctional to highly functional and adaptive states as early as 1 week after injury. Using kinematics, physiological and anatomical analyses, we found that these interventions could recruit specific populations of spinal circuits, refine their control via sensory input and functionally remodel these locomotor pathways when combined with training. The emergence of these new functional states enabled full weight-bearing treadmill locomotion in paralyzed rats that was almost indistinguishable from voluntary stepping. We propose that, in the absence of supraspinal input, spinal locomotion can emerge from a combination of central pattern-generating capability and the ability of these spinal circuits to use sensory afferent input to control stepping. These findings provide a strategy by which individuals with spinal cord injuries could regain substantial levels of motor control.

Figures

Figure 1

Accessing spinal locomotor circuits 1 week after the interruption of all supraspinal input. (a–i) EMG and kinematic characteristics underlying locomotion recorded pre-injury (a) and 7–8 d post-injury (b) without any intervention, as well as under various combinations of serotonergic agonists and/or EES (c–i). The full combination (i) included quipazine, 8-OHDPAT and EES at L2 and S1. Recordings were performed sequentially in the same rat. Horizontal arrows indicate the chronology of the different recordings. A representative stick diagram decomposition of hindlimb motion during swing is shown for each condition with successive color-coded trajectories of limb endpoint. Vectors represent the direction and intensity of the limb endpoint velocity at swing onset. A sequence of raw EMG activity from tibialis anterior (TA) and soleus (Sol) muscles is shown below. Grey and red bars indicate the duration of stance and drag phases, respectively. The BWS of the represented rat under each condition is shown. Finally, a polar plot representation documents the coordination between the left and right hindlimbs (thin arrow, single gait cycle; thick red arrow, average of all gait cycles; 50%, out of phase). (j) Three-dimensional statistical representation of locomotor patterns. Each small colored label represents the gait pattern from an individual rat under a given combination of interventions. The area defined by individual points under a given condition is traced to emphasize the differences between gait patterns under specific combinations. This analysis revealed that each combination of interventions resulted in distinct, but reproducible, patterns of locomotion. (k–m) Bar graphs of average scores on principal components 1–3. (n) Color-coded representation of factor loadings that identify the variables that contributed most to the differences observed between the experimental conditions. For example, principal component 2 captured the differences between stepping with EES at L2 versus S1. Variables associated with changes in joint angles toward flexion and limb endpoint trajectory (left and right step heights, 18–19) clustered on principal component 2, indicating that EES at L2 enhanced flexion, whereas EES at S1 enhanced extension. All of the computed kinematic and EMG variables (n = 135) are reported in Supplementary Table 1. Error bars represent s.e.m. * P < 0.05, different from all other conditions. ** P < 0.05, significantly different conditions.

Figure 2

Kinematics and EMG features of locomotor patterns. (a–h) Bar graphs of average values (n = 7 or 8 rats per group) for locomotor parameters computed under the different experimental conditions. In each graph, the green horizontal bar represents the pre-lesion baseline recorded in the same rat 1 week pre-injury. The duration of stance (light gray), swing (dark gray) and drag (red) phases is shown in a. The r values at t = 0 for the cross-correlation function between oscillations of the left and right hindlimbs computed over a gait sequence of ten steps is shown in b. A r value of −0.5 indicates out of phase coupling between the limbs. The maximum level of weight bearing (percentage of body weight) at which the rat could perform ten successful steps is shown in c. Cross-correlation functions were computed between datasets obtained pre-injury and under a given experimental condition for the hip, knee, ankle and MTP joint angles, and associated joint velocity profiles (d). Maximum r values were extracted from each cross-correlation function and averaged across joint angle and joint angle velocity profiles. The variability of gait parameters computed as the mean coefficient of variation for all the computed parameters normalized to the pre-injury baseline are shown in e. Step height, defined as the maximum vertical distance between the foot (MTP marker) and the stepping surface, is shown in f. The average EMG burst amplitude for left and right soleus (g) and tibialis (h) anterior muscles normalized to pre-injury values are shown. Error bars represent s.e.m. ** P < 0.05, significantly different conditions.

Figure 3

Site-specific effects of EES during standing and stepping. (a) Stick diagram decomposition of hindlimb movements and time course of changes in hindlimb joint angles when delivering EES at L2 (left) or S1 (right) during standing (20% of weight bearing). Each diagram is separated by 5 ms (L2) or 50 ms (S1). The dark gray shaded areas indicate the period during which EES-induced changes in hindlimb posture were observed. Light gray shaded areas represent periods during which EES-induced posture was maintained. (b) Effects of increasing EES by 0.4 and 0.8 V at L2 versus S1 on hindlimb movements during locomotion enabled by the full combination of interventions. Data are presented as in Figure 1. Arrows indicate increased flexion with EES at L2 versus increased extension with EES at S1. (c) Bar graph of average values of maximum foot height during standing and step height during locomotion. Opt, optimal EES intensity to encourage stepping. (d) Bar graphs of average values of angular changes during standing and stepping. For standing, values were obtained by measuring, for each joint angle, the difference between positions at EES onset and at the time of maximum EES-induced change in hindlimb posture at each joint and then averaging these values across joints. Ext, extension. Fle, flexion. For stepping, the maximum position of the hip joint angle in flexion during swing was computed. Error bars represent s.e.m. * P < 0.05, different from all the other conditions. ** P < 0.05, significantly different conditions.

Figure 4

Rehabilitation locomotor training enabled by pharmacological and EES interventions improves stepping ability. (a–d) Representative illustrations of EMG and kinematic characteristics underlying bipedal hindlimb locomotion recorded at 9 weeks post-injury under the full combination of interventions for a nontrained rat with SCI that did not receive pharmacological or EES interventions for the entire duration of the post-injury period until the day of testing (a), a rat with SCI that was trained with serotonergic agonists only (b), a rat with SCI that was trained with EES at L2 and S1 only (c), and a rat with SCI that was trained with the full combination of interventions (d). Data for this rat are also shown pre-injury in Figure 1a and at 1 week post-injury in Figure 1i. (e) Successive limb endpoint trajectories from the right hindlimb are shown for all of the other rats from each experimental group. The BWS of each rat is reported below each limb endpoint trajectory. (f) Color-coded representation of factor loadings of each variable on principal components 1–3 (as shown in Fig. 1n). Principal component 1 identified improved gait in rats tested at 1 week post-injury and then trained with the full combination compared with the other groups. The analysis of variables that clustered on principal component 1 indicated that reduced variability of gait parameters, improved gait stability, increased amplitude of EMG activity and recovery of full weight-bearing capacities were the more salient features for explaining the improved stepping performances of rats trained with the full combination. (g) Three-dimensional statistical representation of locomotor patterns. The near absence of spatial interceptions between the different groups indicates that each group of rats had unique stepping patterns. (h–j) Bar graphs of average scores on principal components 1–3, which each captured specific effects. Error bars represent s.e.m. * P < 0.05, different from all the other conditions. ** P < 0.05, significantly different conditions. Data are presented as in Figure 1.

Figure 5

Functional remodeling of spinal circuits after rehabilitative locomotor training. (a) Representative average (n = 10) traces of monosynaptic motor-evoked potentials recorded from the soleus muscle pre-injury and at 1 and 9 weeks post-injury. Data are shown for one nontrained and one rat trained with the full combination of interventions. Dark shaded areas indicate the amplitude of pre-injury motor-evoked potentials. Bar graphs report the average amplitude of motor-evoked potentials recorded in the soleus muscle at the different time points. (b) Data are presented as in a for the tibialis anterior muscle. (c) Representative example of camera lucida drawings of FOS-positive cells in spinal segments L2, L4 and S1 of a noninjured rat, a nontrained rat with SCI and a rat with SCI trained with the full combination of interventions. (d) Average values for the total FOS-positive cell count (all laminae) per spinal segment. (e) Correlation between the total number of FOS-positive cells (all laminae from L1 to S2) and gait performance measured as individual scores along the principal component 1 axis. PCA was applied on locomotor data (n = 135) recorded from the same rats 3–5 d before the FOS experiments under the same conditions, that is, no intervention for noninjured rats and under the full combination for rats with SCI. Error bars represent s.e.m. * P < 0.05, different from trained group. ** P < 0.05, different from noninjured group.

Figure 6

Effects of velocity-dependent afferent input on motor patterns. (a) Representative example of hindlimb kinematics and EMG activity recorded from a continuous sequence of steps during which the speed of the treadmill belt was changed gradually (0, 5, 15, 25 and 0 cm s−1). Data are presented as in Figure 1, except that changes in hindlimb joint angles are also shown. Stick diagram decomposition of the first step is shown to demonstrate the smooth transition from standing to stepping. MG, medial gastrocnemius; St, semitendinosus; VL, vastus lateralis. (b) The durations of the swing and stance phases are plotted against the cycle duration. Color-coded labels indicate the measured treadmill belt speed during the performance of the represented gait cycles. (c) The durations of flexor (TA) and extensor (MG) EMG bursts are plotted against the cycle duration. (d) The temporal lag between oscillations (with respect to the direction of gravity) of adjacent hindlimb segments is plotted against the cycle duration. Inter-limb lags were computed by means of cross-correlation functions and expressed as a percent of cycle duration. b–d are shown for a representative rat. Mean ± s.e.m. correlation values computed by averaging values obtained from linear regressions performed on each rat (n = 6) individually are reported in each plot. All rats were trained with the full combination of interventions for 3 weeks before the experimental testing.

Figure 7

Effects of load-dependent afferent input on motor patterns. (a) Representative example of hindlimb kinematics and EMG activity induced by the full combination while the rat transited from suspended in the air (0% of body weight) to contact with the immobile treadmill belt (40% of body weight support). (b) Representative example of limb endpoint trajectories, mean EMG activity and mean vertical reaction forces during stepping with 0, 20, 60 or 100% weight bearing. (c) The mean vertical reaction force measured during stance is plotted against the amplitude of the medial gastrocnemius EMG burst for a representative rat that demonstrated full weight-bearing capacities after 3 weeks of training with the full combination of interventions. Color-coded labels represent the amount of body weight supported by the hindlimbs of the rat. A strong relationship was observed in all of the rats (n = 6). The mean ± s.e.m. correlation value computed by averaging the values obtained from linear correlation computed on each rat is reported. (d) Bar graphs of average amplitudes (n = 6) of EMG bursts in selected hindlimb muscles. Values are normalized to values measured during standing (40% of body weight support). The 100% weight-bearing condition is not represented because only two rats could step without any support after 3 weeks of training. (e) Bar graphs of average values (n = 6) of vertical reaction forces measured during stance. Error bars represent s.e.m. ** P < 0.05, significantly different conditions.

Figure 8

Effects of direction-dependent afferent input on motor patterns. (a–c) Representative example of mean (+ s.d.) hindlimb kinematics and raw EMG activity during continuous locomotion in the forward (a), backward (b) and sideways (c) directions. The same limb from the same rat is shown for the three directions, which corresponds to the leading (front) limb during sideward locomotion. Bar graphs show the average (n = 6 rats) linear distance traveled by the foot during swing with respect to the pelvis orientation for the different directions of stepping. Backward (BW) and forward (FW) motions correspond to displacements in the sagittal plane (defined by the pelvis orientation), whereas outward (OW) and inward (IW) motions correspond to displacements in the medio-lateral direction. Probability density distributions of normalized EMG amplitudes between the semitendinosus and medial gastrocnemius muscles, and the tibialis anterior and vastus lateralis muscles are shown at the bottom. L-shape patterns indicate reciprocal activation between the pair of muscles, whereas line-shape patterns indicate coactivation. Abd, abduction (increasing value); Add, adduction. Data are presented as in Figure 1, except that stick diagrams are represented in three dimensions, with the main plane oriented with the direction of the treadmill belt motion.