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1 Departments of Neurology and 2Physiological Science and 3The Brain Research Institute, University of California, Los Angeles, California 90095
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ABSTRACT |
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 Abstract
 Introduction
Methods
Results
Discussion
References
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Harkema, Susan J., Seanna L. Hurley, Uday K. Patel, Philip S. Requejo, Bruce H. Dobkin, and V. Reggie Edgerton. Human lumbosacral spinal cord interprets loading during stepping. J. Neurophysiol. 77: 797-811, 1997. Studies suggest that the human lumbosacral spinal cord can generate steplike oscillating electromyographic (EMG) patterns, but it remains unclear to what degree these efferent patterns depend on the phasic peripheral sensory information associated with bilateral limb movements and loading. We examined the role of sensory information related to lower-extremity weight bearing in modulating the efferent motor patterns of spinal-cord-injured (SCI) subjects during manually assisted stepping on a treadmill. Four nonambulatory subjects, each with a chronic thoracic spinal cord injury, and two nondisabled subjects were studied. The level of loading, EMG patterns, and kinematics of the lower limbs were studied during manually assisted or unassisted stepping on a treadmill with body weight support. The relationships among lumbosacral motor pool activity [soleus (SOL), medial gastrocnemius (MG), and tibialis anterior (TA)], limb load, muscle-tendon length, and velocity of muscle-tendon length change were examined. The EMG mean amplitude of the SOL, MG, and TA was directly related to the peak load per step on the lower limb during locomotion. The effects on the EMG amplitude were qualitatively similar in subjects with normal, partial, or no detectable supraspinal input. Responses were most consistent in the SOL and MG at load levels of <50% of a subject's body weight. The modulation of the EMG amplitude from the SOL and MG, both across steps and within a step, was more closely associated with limb peak load than muscle-tendon stretch or the velocity of muscle-tendon stretch. Thus stretch reflexes were not the sole source of the phasic EMG activity in flexors and extensors during manually assisted stepping in SCI subjects. The EMG amplitude within a step was highly dependent on the phase of the step cycle regardless of level of load. These data suggest that level of loading on the lower limbs provides cues that enable the human lumbosacral spinal cord to modulate efferent output in a manner that may facilitate the generation of stepping. These data provide a rationale for gait rehabilitation strategies that utilize the level of load-bearing stepping to enhance the locomotor capability of SCI subjects.
In the absence of supraspinal input, the adult cat spinal cord can generate efferent motor patterns that enable independent stepping (Barbeau and Rossignol 1987 Subject population
Two nondisabled (ND) and four chronic SCI subjects were studied. SCI subjects were classified with the use of the American Spinal Injury Association (ASIA) impairment scale, which categorizes individuals by their sensorimotor function below the level of lesion and designates a cumulative motor score (Ditunno et al. 1994
Experimental procedures
Soleus (SOL), medial gastrocnemius (MG), and tibialis anterior (TA) EMG activity, ankle and knee joint angles, level of body weight support, and amount of lower limb loading were measured during stepping on a treadmill. All subjects wore a harness connected to an overhead motorized lift that allowed adjustment of lower limb loading. Body weight load (BWL) on the lower extremities of ND subjects ranged from no limb loading (0%) to full loading (100%). Loading of SCI subjects ranged from no limb loading to the maximum loading level at which knee flexion during stance could be avoided (50-80% BWL). The different loads used for each sequence of steps were randomly ordered. SCI subjects were assisted as necessary during stepping. Trainers held each leg distal to the patella to assist with knee extension during stance and distal to the ankle to assist with swing and foot placement. A third person stabilized the pelvis. ND subjects stepped unassisted.
Data acquisition
A 24-channel hard-wired system (Konigsberg Instruments, Pasadena, CA) was used to acquire EMG, electrogoniometer, footswitch, and force transducer signals. EMG data from the SOL, MG, and TA were sampled from 0.1 to 1 kHz and AC coupled into a differential amplifier. The signals were pulse-interval modulated to a fiberoptic transmitter and relayed to decoding electronics. All analog signals were digitally sampled at 1 kHz (National Instruments, Austin, TX).
Limb load sensor calibration
All sensors were dynamically calibrated (Carollo et al. 1993 Data analyses
Data from ~800 steps from the six subjects were analyzed. Data reduction was performed with the use of Labview (National Instruments, Austin, TX) customized by our laboratory. The EMG data were rectified and high-pass filtered at 32 Hz. Force signals from the pressure sensors were interpolated from 50 Hz to 1 kHz and synchronized with EMG signals. Onset of EMG bursts was defined as the time when the signal amplitude remained above the threshold (mean of the baseline + 3 SD) for 50 ms. The end of the EMG burst was defined by the time when the signal amplitude remained below the threshold level for 150 ms. Bursts were analyzed independent of where they occurred in the step cycle; that is, the complete EMG burst in each step was quantified regardless of whether it was synchronized with the onset and termination of loading or muscle stretch. Mean EMG amplitude was calculated by dividing the sum of the amplitudes of each burst by the burst duration. For each step, limb peak load was determined by subtracting the minimum force signal from the maximum. Percent BWL was calculated by dividing the limb peak load by body weight. Mean EMG amplitude versus limb peak load was plotted for the SOL, MG, and TA muscles of each subject.
Statistics
The correlation among EMG mean amplitude and each variable (peak load, muscle-tendon stretch, and velocity of muscle-tendon stretch) were evaluated by regression analysis during a step cycle and during EMG activity. In most cases, quadratic (y = ax2 + bx + c + error) regressions were reported for each muscle (SOL, MG, and TA) because they provided substantially higher correlation coefficients (thus explaining more of the variability) and lower P values than linear (y = ax + c + error) regressions (Tables 3 and 4). When there was a nonsignificant quadratic term and a significant linear term, the linear regression was reported. Additionally, stepwise regression (minimum tolerance for entry intomodel = 0.010,
Motor pool activity during stepping on a treadmill with partial body weight support
EMG bursts of the primary ankle muscles were synchronized with the stepping cadence in all subjects during weight-bearing stepping with body weight support, as shown by representative data from one ND and two SCI subjects (ND-1, SCI-C1, and SCI-A2; Fig. 1). The SOL and MG were active during stance (as displayed by load sensors and footswitches) in all subjects. The MG consistently preceded the onset of loading as well as the initiation of the SOL burst in SCI-A2. The TA was active primarily during swing in both ND-1 and SCI-C1, whereas cocontraction with plantarflexors was observed in SCI-A2. Cocontraction of dorsiflexors with plantarflexors was also observed in both SCI-B1 and SCI-A1 (not shown). Clonic EMG activity (characterized by high amplitude and low frequency) as detected in SCI-C1 was often seen in the bursts of SCI subjects. Overall knee and ankle dynamics were similar among SCI subjects that were assisted and the unassisted ND subjects during stepping.
SOL EMG response to limb load
SOL EMG mean amplitudes were modulated by limb peak load in all subjects. The EMG mean amplitudes of each step of each subject for the range of loads studied are shown in Fig. 2. Each point represents the average of the SOL EMG mean amplitude of 5-30 steps within a 10% BWL range. Regression lines were determined from single steps as plotted in Fig. 3 (Table 2). A significant relationship between SOL EMG mean amplitude and limb peak load was detected in 11 of 11 muscles studied. The responsiveness to loading was most consistent at loads of <40-50% BWL. The magnitude of the effects and the shapes of the regression lines differed among subjects and even between limbs within some subjects (Fig. 2, Table 2). The statistical model of best fit between SOL EMG mean amplitude and limb peak load was curvilinear in 10 of 11 muscles. In one muscle there was no significant curvilinear relationship but a significant linear one (see right leg data for SCI-A1 in Fig. 2, Table 2). SOL and MG EMG activity was not detected during stepping from the left limb of SCI-A2 during multiple experiments. These data illustrate that SOL EMG amplitudes were modulated as a function of load in subjects with intact minimal or no detectable supraspinal input to the motor pools studied.
SOL EMG responses to muscle-tendon length changes
The relationships among SOL EMG mean amplitudes, level of load, and changes in muscle-tendon stretch were examined to determine whether limb loading affected the range of joint motion. These altered kinematics could change muscle-tendon stretch during gait, which could modulate EMG activity through reflex pathways. The relationships among SOL EMG mean amplitude, limb peak load, muscle-tendon stretch, and velocity of muscle-tendon stretch per step were examined. Representative data are shown for one subject from each functional class (Fig. 3, A-C, E, G-I, K, M-O, and Q). Statistical analyses of the relationships between SOL EMG mean amplitude and muscle-tendon stretch for one limb of each subject are listed in Table 2. A positive relationship between SOL EMG mean amplitude and limb peak loading was evident for the ND and both SCI subjects (Fig. 3, A, G, and M) shown. Higher limb peak loading resulted in a significant increase in muscle-tendon stretch in SCI-A1 (r = 0.86; Fig. 3N) and also SCI-C1 and ND-1, but with relatively low correlation (r = 0.50 and 0.58, respectively; Fig. 3, H and B). In five of the six subjects there was a significant curvilinear relationship between SOL EMG mean amplitude and SOL muscle-tendon stretch and between SOL EMG mean amplitude and velocity of muscle-tendon stretch during the step cycle. The changes in SOL EMG mean amplitudes in all subjects were correlated more closely with peak load per step (Fig. 3, A, G, and M) than to changes in muscle-tendon stretch (Fig. 3, C, I, and O) or velocity of muscle-tendon stretch (Fig. 3, E, K, and Q; Table 2) per step.
MG EMG responses to limb load
As in the SOL, MG motor pool mean amplitude was modulated by limb peak load during manually assisted and independent stepping (Fig. 5). A significant relationship between MG EMG mean amplitudes and limb peak load was observed in both ND subjects and three of the four SCI subjects studied (SCI-C1, SCI-A1, and SCI-A2; Fig. 5, Table 3). The statistical model of best fit was curvilinear in five muscles (ND-1, SCI-A1, and SCI-A2) and linear in four muscles (ND-2 and SCI-C1). A decrease in MG EMG activity was observed at the higher limb peak loads studied in the left limb of SCI-A1.
MG EMG responses to muscle-tendon length changes
The relationships among MG EMG amplitude, limb peak loading, muscle-tendon stretch, and velocity of muscle-tendon stretch were examined to determine whether joint mechanics affecting muscle-tendon stretch could be providing the sensory cues that regulate motor pool modulation. All data points used to derive the correlations in Table 3 were plotted for one subject from each functional group in Fig. 6 as described previously for the SOL. The higher loads were associated with greater muscle-tendon stretch in all three subjects (ND-1, r = 0.71; SCI-C1, r = 0.69; SCI-A1, r = 0.87; Fig. 6, B, H, and N). However, in several cases the relationships between MG EMG mean amplitude and selected variables associated with muscle-tendon stretch were not significant (Table 3, see ND-1, ND-2, SCI-B1, and SCI-A2). Further, significant relationships between EMG mean amplitude and muscle-tendon stretch that occurred during a step cycle or length changes that were synchronized with MG EMG activity were not as highly correlated as those observed between EMG mean amplitude and limb peak load (Table 3). In ND-1 and SCI-C1, the shape of the relationships between EMG amplitude and limb peak load (Fig. 6, A and G) were not similar to those between MG EMG mean amplitude and muscle-tendon stretch during a step (Fig. 6, C, E, I, and K) or synchronized with MG EMG activity (Fig. 6, D, F, J, and L). A multivariate, forward stepwise regression model incorporating all of these variables selected limb peak load as the parameter primarily responsible for the variability in MG EMG mean amplitude in both of these subjects as well as ND-2.
TA EMG responses to limb load
There was a significant relationship between mean TA EMG amplitude and limb peak load in 10 of the 11 muscles studied (Fig. 8, Table 4) but the correlation coefficients were lower than in the plantarflexors. The statistical model of best fit was linear in four muscles, curvilinear in six. A higher level of TA EMG activity was evident in ND subjects than in SCI subjects during no limb loading. Despite the low levels of activity in the TA of SCI subjects, EMG mean amplitude did respond to limb peak load. Subjects SCI-C1, SCI-B1, and SCI-A1 showed the highest correlations (Fig. 8 and Table 4, right leg data). Subject SCI-B1 demonstrated some load dependence from ~50 to 80% BWL.
TA EMG responses to muscle-tendon length changes
We examined the relationships among TA EMG amplitude, limb load, muscle-tendon length, and velocity of muscle-tendon length change within a step. Representative data averaged from eight step cycles are shown for ND-1 (Fig. 9A) and SCI-A2 (Fig. 9B). In both subjects, higher TA EMG amplitude was detected with increased limb peak loading. However, unlike the plantarflexors, minimal modulation of TA EMG amplitude was synchronized to load or muscle-tendon stretch in the subjects studied. In ND subjects the EMG activity primarily occurred during the swing phase, although, especially at the higher limb loading conditions, observable TA EMG activity was present during stance. A significant correlation between TA EMG mean amplitude and muscle-tendon stretch and velocity of muscle-tendon stretch during a step cycle was observed in ND-1 and ND-2 (Table 4). However, a significant correlation was observed between TA EMG mean amplitude and muscle-tendon stretch and velocity of muscle-tendon stretch during EMG activity in ND-2 but not in ND-1. These measurements were taken only during periods when the TA EMG activity was observed and the TA was lengthening. This represents only a portion of the total EMG activity per step cycle.
These data demonstrate that the level of loading through the limbs during cyclic activity can provide important information that facilitates the generation of steplike efferent patterns by the human lumbosacral spinal cord. Although a modulation of the EMG amplitude and coordination of the EMG bursts in lower limb muscles by load of completely spinalized cats (de Guzman et al. 1991 Sensory modalities that contribute to weight-bearing stepping
The load-imposed modulation of EMG amplitude illustrates one of many possible kinetic and kinematic signals concurrent with stepping that shape the output of the motor pools controlling the lower limbs. A more parsimonious interpretation has been that the motor patterns seen in clinically complete SCI patients reflect only the rhythmic muscle stretches induced by the mechanical events associated with stepping (Rossignol and Barbeau 1995 Phase dependence of EMG modulation
The present results demonstrate a very consistent phase-specific modulation of the efferent output of muscles in response to loading and the kinematics of a complete step cycle. A phase-dependent modulation of plantarflexor motor pool activity occurred when the limbs were allowed to support weight. Within a step, the relationships among SOL EMG activity, level of load, and muscle-tendon length were remarkably similar in all subjects. A strong phase dependence was indicated by the observations that SOL EMG amplitudes differed substantially throughout the step cycle at the same load, muscle-tendon length, and velocity. This phase-specific modulation was also observed consistentlyin the MG, whereas phase dependence was not obvious inthe TA.
Sensory information interpreted by the human lumbosacral spinal cord
The present results are a clear demonstration that load- and phase-dependent responses of the extensor and flexor motor pools of the lower limbs can be attributed to the human lumbosacral spinal cord, i.e., present in subjects with no detectable supraspinal influence available to the lower limb motor pools. Further, although responses of EMG activity to loading levels in humans with intact supraspinal input have been observed during standing (Horstmann and Dietz 1990 Muscle specificity of load modulation
Within a step the limb peak load occurred approximately at the EMG peak amplitude in the SOL in both subjects studied, but was only observed in the MG of the ND subject. SOL activation usually occurred after the initiation of stance in all subjects. Differential regulation of SOL and MG activation during walking has been reported previously (Duysens et al. 1991 Implications of present data
Because the lumbosacral motor pools appear to respond to multiple sensory modalities simultaneously and the type and magnitude of the response is dependent on the phase of the step cycle, it follows that the ensemble of sensory inflow from all sources and modalities concurs with the actual kinetic, kinematic, and cutaneous events appropriate for a specific instant in a step cycle. This interpretation is consistent with observations in chronic spinal cats that the specific manner of loading and the control of other sources of sensory inflow are crucial to the success of weight-bearing stepping (Barbeau and Rossignol 1987
INTRODUCTION
Abstract
Introduction
Methods
Results
Discussion
References
; Lovely et al. 1986). The oscillating neural circuitry responsible for this task (Grillner and Zangger 1979) is highly dependent on peripheral modalities associated with stepping such as weight bearing and joint position (de Guzman et al. 1991; Forssberg 1979; Grillner and Dubuc 1988; Lovely et al. 1990). The neural pathways in the lumbosacral spinal cord of the cat respond to sensory information specifically associated with locomotion to generate the efferent motor patterns necessary to execute stepping (Edgerton et al. 1996). Whether similar locomotor neural circuits are present in humans has not been determined, although it now appears that at least some of the circuitry necessary to generate fictive locomotion has been conserved in nonhuman primates(Hultborn et al. 1993).
; Dietz et al. 1995; Wernig and Müller 1992). SCI subjects with no supraspinal input below their lesions can generate oscillating motor patterns in the lower limbs without the sensations of weight-bearing stepping (Bussel et al. 1988; Rosenfeld et al. 1995) and can generate steplike efferent patterns when partially supported over a moving treadmill belt and manually assisted with stepping (Dietz et al. 1995; Dobkin et al. 1995). These studies suggest that the human lumbosacral spinal cord can generate steplike oscillating electromyographic (EMG) patterns when assisted by the sensory information associated with weight-bearing stepping. It remains unclear, however, to what degree these efferent patterns depend on the phasic peripheral sensory information associated with bilateral limb movements and loading.
METHODS
Abstract
Introduction
Methods
Results
Discussion
References
) (Table 1). In brief, ASIA classifies individuals as follows: A, no sensory or motor function below the lesion including the sacral segments S4-S5; B, sensory but not motor function is preserved below the neurological level; and C, motor function is preserved below the neurological level but there is no active movement against gravity. SCI subjects in this study could not voluntarily stand or step over ground. Their injuries were at the thoracic level (Table 1) and a result of traumatic injury (SCI-A1, SCI-A2, and SCI-B1) or ischemia (SCI-C1). Sensory evoked potentials were recorded from the popliteal fossa, lumbar spine, and scalp (vertex positive peak) with the use of unilateral and bilateral posterior tibial nerve electrical stimulation (Table 1). No response confirmed the absence of detectable supraspinal conductivity to the lower limbs in the ASIA A subjects. Subjects took no medications during these studies. Informed consent was obtained from each subject and the experiments were approved by the University of California at Los Angeles Human Subjects Protection Committee.
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TABLE 1.
Clinical characteristics of SCI subjects
) with the use of a force platform (Kistler, Amherst, NY), with ND individuals matched by body weight and shoe size to SCI subjects. Calibrations from ND persons were used for all SCI subjects because they were not capable of over-ground walking. The force signal from the sensors was compared with the signal from a force plate recorded simultaneously during a step for each subject. The peak forces (maximum amplitude during contact) and mean forces (average of the amplitude during contact) from the pressure sensors and the force plate from several steps were compared for each subject. A significant (P < 0.05), positive linear relationship was observed for both the peak (y = 0.98x 
10, r = 0.99) and mean forces (y = 1.0x 46, r = 0.96) from the pressure sensors versus the force plate.
0.92 ± 0.15, mean ± SE) between mean unloading and loading and the intercepts closely corresponded to the individual's body weight.
. The rate of change of muscle-tendon length (referred to as velocity of muscle-tendon length change) was determined by the derivation of the calculated muscle-tendon length curves. The relationship between mean EMG activity and increases in muscle-tendon length (muscle-tendon stretch) were compared in two ways. First, the mean EMG activity (as described above) was plotted versus the maximum muscle-tendon stretch of each step cycle, independent of whether the EMG activity of the muscle was synchronized with the muscle-tendon stretch. The mean EMG activity was also plotted versus the velocity of muscle-tendon stretch. Second, to assess the immediate response of the EMG activity to muscle-tendon stretch, the mean EMG activity was determined solely during the period of the muscle-tendon stretch incorporating a delay for signal conduction velocity (70 ms). The mean EMG activity during this period was plotted versus muscle-tendon stretch and velocity of muscle-tendon stretch.
to enter = 0.150, to remove = 0.150) of all variables per muscle was performed.
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TABLE 3.
Correlation coefficients between mean EMG amplitude and limb load, muscle-tendon stretch, and
velocity of muscle-tendon stretch for the medial gastrocnemius
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TABLE 4.
Correlation coefficients between mean EMG amplitude and limb load, muscle-tendon stretch, and
velocity of muscle-tendon stretch for the tibialis anterior
RESULTS
Abstract
Introduction
Methods
Results
Discussion
References
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FIG. 1.
Representative electromyographic (EMG) activity (µV; rectified, high-pass filtered at 32 Hz) from soleus (SOL), medial gastrocnemius (MG), and tibialis anterior (TA); knee and ankle angles (°); limb load (N); body weight support (BWS, N); and foot contact (FS) during stepping at a treadmill speed of 0.45 m/s. Data from a nondisabled (ND) subject (ND-1) and 2 spinal-cord-injured (SCI) subjects (SCI-C1 and SCI-A2) are shown. The ND subject stepped independently and SCI subjects stepped with assistance. Dashed lines: transition between stance and swing phases of a step cycle as detected by the footswitches.
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FIG. 2.
Averaged SOL EMG mean amplitude per burst (µV; rectified, high-pass filtered at 32 Hz) vs. limb peak load [% body weight load (BWL)] for each subject. Each point represents the average of SOL EMG amplitudes (means ± SE) within a 10% interval of the BWL range (i.e., 0-10%, 10-20%, etc.). Data from the right limb ( 
) and left limb (
) of each subject are shown separately. Data were not available for every BWL interval. The regression line was based on individual data points from each step cycle and drawn for each muscle when there was a significant relationship (P < 0.05; see Table 2 for correlation ratios).
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FIG. 3.
Relationships among SOL EMG mean amplitude (µV), limb peak load (N), SOL muscle-tendon stretch [% shank length (SL)], and velocity of SOL muscle-tendon stretch [% shank length per s (SL/s)] from ND-1 (A-F), SCI-C1 (G-L), and SCI-A1 (M-R) over a range of loading conditions are shown. Muscle-tendon stretch and velocity of muscle-tendon stretch were measured during an entire step cycle (C, E, I, K, O, and Q) and also during the period synchronized with SOL EMG activity (SYNCH mean EMG; D, F, J, L, P, and R). Each data point represents 1 step and each symbol represents a series of consecutive steps at 1 level of body weight support. There was a significant relationship between SOL muscle-tendon stretch and limb peak load in ND-1 (B; r = 0.58), SCI-C1 (H; r = 0.50), and SCI-A1 (N; r = 0.86).
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TABLE 2.
Correlation coefficients between mean EMG amplitude and limb load, muscle-tendon stretch, and
velocity of muscle-tendon stretch for the soleus
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FIG. 4.
SOL EMG amplitude (µV; rectified, high-pass filtered at 32 Hz, low-pass filtered at 5 Hz), limb load (N), SOL muscle-tendon length (% SL), and velocity of SOL muscle-tendon length change (% SL/s) from 2 consecutive steps with inadvertently different limb loads from ND-1 (A-D) and SCI-A1 (E-H). All data are shown relative to the phase of the step cycle (s) in A and E. Vertical dashed lines: transitions between stance and swing phases. SOL EMG amplitude vs. limb load (B and F), SOL EMG amplitude vs. muscle-tendon length (C and G), and SOL EMG amplitude vs. velocity of muscle-tendon length change (D and H) are illustrated. The stance phases of the steps are represented by a dashed line (high load) and solid line (low load). The swing phases of the steps are represented by open symbols (circles, high load; squares, low load). Arrows indicate the direction of the step cycle and are located at the time point that represents 50% of the stance phase.
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FIG. 5.
Averaged MG EMG mean amplitude per burst (µV) vs. limb peak load (% BWL) are presented for each subject. Each point represents the average of MG EMG amplitude (mean ± SE) within a 10% interval of the BWL range. Data from the right limb ( ) and left limb () of each subject are shown separately. Data were not available for every BWL interval. The regression line was based on individual data points from each step cycle and drawn for each muscle when there was a significant relationship (P < 0.05; see Table 3 for correlation ratios).
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FIG. 6.
Relationships among MG EMG mean amplitude (µV), limb peak load (N), MG muscle-tendon stretch (% SL), and velocity of MG muscle-tendon stretch (% SL/s) from ND-1 (A-F), SCI-C1 (G-L), and SCI-A1 (M-R) over a range of loading conditions. Muscle-tendon stretch and velocity of muscle-tendon stretch were measured during an entire step cycle (C, E, I, K, O, and Q) and also during the period synchronized with MG EMG activity (SYNCH mean EMG; D, F, J, L, P, and R). Each data point represents 1 step and each symbol represents a series of consecutive steps at 1 level of body weight support. There was a significant relationship between MG muscle-tendon stretch and limb peak load in ND-1 (B; r = 0.71), SCI-C1 (H; r = 0.69), and SCI-A1 (N; r = 0.87).
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FIG. 7.
MG EMG amplitude (µV), limb load (N), muscle-tendon length (% SL), and velocity of muscle-tendon length change (% SL/s) from 2 consecutive steps with inadvertently different limb peak loads from ND-1 (A-D) and SCI-A1 (E-H). All data are shown relative to the phase of the step cycle in A and E. Vertical dashed lines: transitions between stance and swing phases. MG EMG amplitude vs. limb load (B and F), EMG amplitude vs. muscle-tendon length (C and G), and MG EMG amplitude vs. velocity of muscle-tendon length change (D and H) are illustrated. The stance phases of the steps are represented by a dashed line (high load) and solid line (low load). The swing phases of the steps are represented by open symbols (circles, high load; squares, low load). Arrows indicate the direction of the step cycle and are located at the time point that represents 50% of the stance phase.
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FIG. 8.
Averaged TA EMG mean amplitude per burst (µV) vs. limb peak load (% BWL) for each subject. Each point represents the average TA EMG amplitudes (means ± SE) within a 10% interval of the BWL range. Data from the right limb ( ) and left limb () of each subject are shown separately. Data were not available for every BWL interval. The regression line was based on individual data points from each step cycle and was drawn for each muscle when there was a significant relationship (P < 0.05; see Table 4 for correlation ratios).
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FIG. 9.
TA EMG amplitude (µV, rectified, high-pass filtered at 32 Hz, low-pass filtered at 5 Hz), limb load (N), muscle-tendon length (% SL), and velocity of muscle-tendon length change (% SL/s) during a high-load and a low-load condition from ND-1 (A) and SCI-A2 (B) are shown. Consecutive step cycles were averaged by triggering from the point at which stance was initiated (increase in force measured from the pressure sensors). The average (heavy line) and average + SD (light line) for both load conditions relative to the phase of the step cycle (% STEP CYCLE) are shown for each subject in A and B. Vertical dashed lines: transitions between the stance and swing phase.
DISCUSSION
Abstract
Introduction
Methods
Results
Discussion
References
; Edgerton et al. 1991) and decerebrate cats (Conway et al. 1987; Duysens and Pearson 1980) has been described previously, Dietz et al. (1995) reported that in SCI humans with no supraspinal input "there was little correlation between the amplitude of EMG activity and unloading of the body weight." The present data demonstrate, however, that the level of loading does provide cues that modulate the motor pool activity in a similar manner in subjects with either full, partial, or no supraspinal control of the motor pools that innervate the lower limb muscles.
; Stewart et al. 1991). The present study shows that, although sensory information associated with muscle-tendon stretch and velocity of muscle-tendon stretch during a complete step cycle could have had some effect on the efferent motor patterns of the lower limbs, the EMG amplitudes were clearly less coupled to length factors than limb peak load per step. Higher correlations were not consistently observed when comparing these kinetic variables only during the period when an EMG burst occurred. Although the present data are consistent with the possibility that muscle-tendon stretch could contribute to the total ensemble of proprioceptive and cutaneous information, it is evident that this single mechanical event does not play the dominant role in EMG amplitude modulation during manually assisted stepping on a treadmill with body weight support.
; Fung and Barbeau 1994; Yang and Stein 1990). Further, limb unloading has been shown to be an important signal for the termination of stance and the initiation of swing in cat locomotion (Conway et al. 1987; Duysens and Pearson 1980; Pearson and Duysens 1976). The present data emphasize that there are other afferent systems that are not closely associated with limb loading that can markedly affect the output of the motor pools. For example, a series of steps at the same loading condition resulted in varying levels of EMG activity in the muscles studied. Hip joint position (Andersson and Grillner 1981; Andersson et al. 1978b; Grillner and Rossignol 1978), contralateral limb information (Berger et al. 1984; Burke et al. 1991), and cutaneous stimulation (Abraham et al. 1985; Andersson et al. 1978a; Duysens 1977) could have a significant effect on EMG amplitudes. It is feasible that all of these sources of sensory information contribute to defining the kinetic and kinematic events associated with stepping in ND as well as SCI subjects (Wernig and Müller 1992).
; Capaday and Stein 1986; Crenna and Frigo 1987; Duysens et al. 1990, 1992; Yang et al. 1991a,b). Further, phase-dependent modulations of reflexes in mammals during cyclic motor output in response to cutaneous stimulation (Abraham et al. 1985; Duysens 1977), changes in hip position (Andersson and Grillner 1981; Andersson et al. 1978b), and loading (Conway et al. 1987; Duysens and Pearson 1980) have been reported for a range of experimental conditions in cats. The present data do not add to our understanding of the relative contribution of each afferent system at each point in the step cycle to locomotion in humans. It seems likely, however, that these neural pathways form an ensemble of temporally appropriate inputs that shape the patterns of motor pool output of the lower limbs.
) and stepping on a treadmill (Dietz et al. 1995; Finch et al. 1991), there has been no previous report of phase-dependent sensitivity to loading in humans. In the current study, responses of EMG activity to limb peak loading were evident in subjects independent of the amount of supraspinal input available to the lumbosacral motor pools and whether or not manual assistance for stepping was provided, suggesting a general mechanism within the spinal cord for interpreting load during locomotion in humans.
). Many studies have suggested that the neural circuits of the human locomotor system (Bussel et al. 1988, 1989; Calancie et al. 1994; Crenna and Frigo 1987; Duysens et al. 1992; Roby-Brami and Bussel 1987) are organized in a manner similar to that in the cat (Gossard and Hultborn 1991). The present study provides additional evidence that the human lumbosacral spinal cord may have maintained at least some of the same neural components that respond to limb load as in cats (Pearson 1995).
). In the present study the SOL response to load and muscle-tendon length change was similar in the SCI and ND subjects. The MG motor pools were active before the onset of limb load in SCI but not in ND subjects. Muscle-tendon stretch may play a more important role in initiation of the MG EMG amplitude in the SCI subjects than in ND subjects.
). These results indicate that there may be a common source for activation of the plantarflexors and dorsiflexors during stepping in SCI subjects. Further study is required to determine the significance and cause of the response of TA EMG amplitude to load.
; Duysens and Pearson 1980). Load may not only facilitate ipsilateral extension but it may simultaneously facilitate contralateral flexion. Simultaneous activation of MG and contralateral TA has been observed during gait after a perturbation in humans (Berger et al. 1984). Regardless, the enhanced flexor activity has two important implications. First, it seems to facilitate flexion ipsilaterally, even though the timing may not be normal in an SCI subject. Second, it shows that the spinal cord interprets the afferent signals regarding load in an integrative manner such that the response to load is not immediate or reflexive.
; Edgerton et al. 1991; Lovely et al. 1990). Further, the necessity of the careful control of this sensory information when training a spinalized cat to step indicates an ability of the sensory motor pathways of the lumbosacral spinal cord to "perceive" and respond to the details of step related events (Edgerton et al. 1996). Our present understanding of the human lumbosacral spinal cord is not nearly as extensive as it is for other mammals. The present data do suggest that some of the fundamental properties are at least qualitatively similar in humans and cats.
; Dietz et al. 1995; Wernig et al. 1995). The efficacy of this rehabilitative approach not only depends on the capacity of the spinal cord to interpret ongoing sensory inflow from the limbs during stepping, but it also assumes some use-dependent plasticity in the sensorimotor pathways that generate stepping. Further studies identifying the effective sensory modalities, the neural pathways involved, and level of adaptability and motor learning available to the human spinal cord are essential for understanding the potential of step training on a treadmill with body weight support for the rehabilitation of individuals with spinal cord injury.
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ACKNOWLEDGEMENTS |
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We thank M. Bieschke and D. Rose for assistance during data collection; R. Yap and B. Mares for participation in data collection and analysis; and J. Foster for data reduction. We appreciate the statistical advice of Dr. Stephanie R. Land and the review of the manuscript by Dr. Roland R. Roy.
This research was supported by National Institutes of Health Grants NS-16333, HD-07416, and MO1-RR-00865. We gratefully acknowledge the generous support of the Ahmanson Foundation, J. Sachs, X. Sachs, A. Zaky, X. Zaky, and H. Michelsen and W. Kaye and S. Kaye of the Jewish Federation Council of Greater Los Angeles.
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FOOTNOTES |
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Address for reprint requests: V. R. Edgerton, 2322 Life Sciences, Dept. of Physiological Science, UCLA, Los Angeles, CA 90095-1527.
Received 9 April 1996; accepted in final form 18 September 1996.
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REFERENCES |
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Abstract
Introduction
Methods
Results
Discussion
References
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