In press – Journal of Neurophysiology

Kinematic and EMG Determinants in Quadrupedal Locomotion of a Non-Human Primate (Rhesus) Contributors:

Grégoire Courtine 1, Roland R. Roy 2, John Hodgson 1, 2, Heather McKay 3, Joseph Raven1, Hui Zhong 1, Hong Yang 4, Mark H. Tuszynski 4, 5 and V. Reggie Edgerton 1, 2 Affiliations: 1. Department of Physiological Science, University of California, Los Angeles, California. 2. Brain Research Institute, University of California, Los Angeles, California. 3. California National Primate Research Center (CNPRC), University of California, Davis, California. 4. Department of Neurosciences, University of California, San Diego, La Jolla, California. 5. Veterans Affairs Medical Center, San Diego, California.

Running title: Kinematic and EMG patterns during Rhesus locomotion CA

Correspondence:

V. Reggie Edgerton, Ph.D. Dept. of Physiological Science University of California, Los Angeles 405 Hilgard Ave. Los Angeles, CA 90095-1527. 310-825-1910 (phone) 310-267-2071 (fax) [email protected]

Kinematic and EMG patterns during rhesus monkey locomotion

Acknowledgments This work was supported by the National Institutes of Health (Grant Number: RO1-NS42291) and the California Roman Reed Bill.

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Abstract We hypothesized that the activation patterns of flexor and extensor muscles and the resulting kinematics of the forelimbs and hindlimbs during locomotion in the Rhesus would have unique characteristics relative to other quadrupedal mammals. Adaptations of limb movements and in motor pool recruitment patterns in accommodating a range of treadmill speeds similar to other terrestrial animals in both the hindlimb and forelimb were observed. Flexor and extensor motor neurons from motor pools in the lumbar segments, however, were more highly coordinated than in the cervical segments. Unlike the lateral sequence characterizing subprimate quadrupedal locomotion, non-human primates use diagonal coordination between the hindlimbs and forelimbs, similar to that observed in humans between the legs and arms. Although there was a high level of coordination between hindlimb and forelimb locomotion kinematics, limb-specific neural control strategies were evident in the inter-segmental coordination patterns and limb endpoint trajectories. Based on limb kinematics and muscle recruitment patterns, it appears that the hindlimbs, and notably the distal extremities, contribute more to body propulsion than the forelimbs. Furthermore, we found adaptive changes in the recruitment patterns of distal muscles in the hindlimb and forelimb with increased treadmill speed that likely correlate with the anatomical and functional evolution of hand and foot digits in monkeys. Changes in the properties of both the spinal and supraspinal circuitry related to stepping, probably account for the peculiarities in the kinematic and EMG properties during non-human primate locomotion. We suggest that such adaptive changes may have facilitated evolution toward bipedal locomotion.

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Kinematic and EMG patterns during rhesus monkey locomotion

Introduction Mammalian locomotion is characterized by repetitive and stereotypic limb movements associated with highly-structured patterns of muscle activity. Examination of the features of muscle synergies underlying stepping-related limb movements has provided important information regarding the functions of the skeletal musculature (Roy et al. 1991b; Zajac et al. 2003; Zernicke and Smith 1996), and the organization of the neural processes that drive motor neuron activity during locomotion (Grillner 1981; Ivanenko et al. 2004b; Rossignol 1996). Likewise, locomotion kinematics has provided insight into the neural strategies by which the CNS coordinates the oscillations of the limbs and the trunk during walking in both cats (Shen and Poppele 1995) and humans (Courtine and Schieppati 2004; Lacquaniti et al. 2002). There are relatively few data on the kinematic and electromyographic (EMG) determinants for non-human primate locomotion (Mori et al. 1996; Recktenwald et al. 1999). For example, little is known about the pattern of hindlimb (HL) and forelimb (FL) muscle activation during walking, nor about how the recruitment of these motor neuron pools is modulated with respect to the speed of locomotion. Similarly, there is virtually no information on how the CNS of monkeys coordinates the oscillation of HL and FL segments during gait, and adjusts the structure of inter-segmental coordination patterns to increasing locomotor velocities. Inter-species comparisons of the kinematic and EMG characteristics of locomotor control have highlighted many similarities in the features of the motor program for walking among mammals, thereby supporting the idea of robust conservation of the neural strategies that control terrestrial locomotion (Lacquaniti et al. 1999; Orlovskii et al. 1999). On the other hand, important differences have been emphasized. Notably, erect bipedal stepping encompasses characteristics in limb kinematics and muscle activity patterns that are unique to human locomotion (Capaday 2002). Interestingly, laboratory-based anthropological studies show that non-human primate quadrupedalism exhibits a variety of features that distinguish it from that observed in most other mammals (Larson 1998; Schmitt 2003). One view is that these alterations in the organization of gait in primates necessitated adaptations in the underlying neurological control mechanisms, including an increased dependence of spinal circuits upon supraspinal modulating commands (Capaday 2002; Duysens and Van de Crommert 1998; Eidelberg et al. 1981; Fedirchuk et al. 1998; Nielsen 2002; Vilensky and O'Connor 1998). Nevertheless, no conclusive evidence for non-human primate-specific neural control mechanisms for stepping has been obtained, principally because of the lack of detailed information on the neuromechanics of locomotion under carefully controlled conditions. This information is critical, however, since comparative features of the neural architecture for monkey and human locomotion would have major implications for understanding the evolution toward bipedal locomotion in humans. This information also would have direct relevance in the use of non-human primates to formulate interventions to enhance motor recovery following spinal cord injury (SCI). The degree to which interventions developed in cats (Edgerton et al. 2001; Rossignol et al. 2004) and rats (Jones et al. 2001) will be effective in larger animals and in primates continues to be an open question. The effectiveness of these interventions may depend upon the extent of the similarities in the functional and anatomical organization of the motor infrastructure between lower and higher mammals (Edgerton and Roy 2002; Tuszynski et al. 2002).

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Courtine et al. In the current study, we detail the spatial and temporal characteristics of the gait pattern in the intact Rhesus, and show how these features are adjusted with increasing locomotor velocities. We document for the first time the kinematics of both the HLs and FLs and their associated patterns of muscle activity during quadrupedal locomotion on a treadmill over a range of speeds. Our objectives were 1) to compare the kinematic and EMG features of walking in a non-human primate with subprimate quadrupedal mammals and with humans; and 2) to provide a solid baseline to identify the effects of selective interruptions of descending and ascending pathways on Rhesus locomotor control (Courtine et al. 2004), and the putative recovery of motor function that can occur following selected interventions to enhance this recovery (Yang et al. 2004). We hypothesized that the activation patterns of flexor and extensor muscles and the resulting kinematics of the FLs and HLs during locomotion in the Rhesus would reflect some ability to decouple the interdependence of selected motor pools relative to other quadrupedal mammals, and that these characteristics would be consistent with the evolution of bipedal locomotion in primates. Although our results show that Rhesus monkeys share a number of similarities in the organization of gait with other mammals, there are some unique features in their stepping-related kinematic and muscle activation patterns. We suggest that these differences are related to adaptation of non-human primate gait to the arboreal environment, and the evolutionary forces that led to the evolution of bipedalism in human primates.

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Kinematic and EMG patterns during rhesus monkey locomotion

Materials and Methods Experimental subjects and treadmill training protocol Six adult (7 to 15 years of age, 11.2 kg (range, 7-14 kg) body weight), male Rhesus monkeys (Macaca mulatta) were studied. Each animal was trained to walk quadrupedally on a motor-driven treadmill. A plexiglass enclosure was used to maintain the animal in position while allowing video recording of their movements. The initial training sessions were used to acclimatize the animals to the treadmill environment without the belt moving. Subsequent sessions were used to train the animals to locomote consistently at speeds of 0.45, 0.89, 1.34, and 1.79 m/s. Each animal was trained for a minimum of one month before any locomotor data were collected. Each training session consisted of eight locomotor trials (2 repetitions at each speed) with approximately a one-min rest period between each trial. The duration of each session was ~40 min. A variety of food items were used as rewards after each locomotor trial.

Surgical Procedures After the training period, the six clinically normal Rhesus were implanted with bipolar intramuscular EMG electrodes under aseptic conditions. The Rhesus were housed individually in standard 4.3 or 6.1 square feet stainless steel cages. Prior to any surgical procedures, the Rhesus were trained to wear a specially designed jacket that would protect exteriorized instrumentation. EMG electrode arrays similar to those described by Hodgson et al. (2001) were purchased from a commercial source (Model TK-12, Konigsberg Instruments, Pasadena, CA, USA). The EMG implants were manufactured from teflon-coated multistrand stainless steel wire (32 gauge; Cooner Wire, Chatsworth, CA). The wires from the EMG implants were embedded in silicon rubber in 1.5 mm silicon rubber tubes, and terminated in a small multipin connector (a skin button) attached to the skin between the scapulae. The Rhesus were housed and all surgical procedures were performed at the California National Primate Research Center (CNPRC, University of California, Davis, CA, USA). The following anesthesia regimen was followed. Preoperative management consisted of food restriction for ~8 h. Induction of anesthesia was with ketamine HCl (10 mg/kg i.m.). Atropine sulfate (0.04 mg/kg i.m.) was administered during induction. A catheter was placed in either the saphenous or cephalic vein to supply fluids during the procedure, and a tracheal tube was placed to give a free airway for gas anesthesia. Anaesthesia was maintained with isoflurane gas (1.25%) in 100% oxygen delivered via a cuffed orotracheal tube. Throughout the surgery, a trained animal health technician monitored heart rate, blood pressure, O2 saturation, CO2 expiration levels, core body temperature, respiratory rate, respiratory pressure, and tidal volume using a surgical Ohmeda-Datex unit. Lactated Ringers solution (10 ml/kg/h) was administered at a continuous infusion rate for the duration of anesthesia. Prior to any incisions being made, the depth of anesthesia was assessed by checking heart rate, blood pressure, jaw tone, and toe-pinch response. Adjustments in the level of anesthesia were made as needed. Selected HL muscles were implanted in Rhesus #1, #2, and #3 and selected FL muscles were implanted in Rhesus #4, #5 and #6. Table 1 identifies the muscles implanted in each animal, and lists the main actions of each muscle as well as the muscle abbreviations used throughout the text. Rhesus #4 rejected the implants, and only kinematic data (see below) were recorded from this animal. A ~4-cm incision was made at the midline of the upper back on line with the caudal border of p.6

Courtine et al. the scapula. For the HL implants, skin incisions (~4-6 cm) were made over the bellies of the VL, the triceps surae and the TA. The bellies of the VL, MG, Sol, FDL, FHL, EDL and/or TA in the right leg and the Sol, MG and/or TA in the left leg were exposed as clearly as possible. Using a smooth rod, the EMG wires were routed subcutaneously from the back incision to the appropriate locations in the HL. Bipolar intramuscular EMG electrodes were inserted into the medial, midbelly of the Sol, the distal, medial deep region of the MG, the midbelly of the TA, the lateral midbelly of the FHL, the lateral midbelly of the FDL, the lateral distal portion of the EDL, and/or the lateral, deep region of the VL using procedures described in detail previously (Hodgson et al. 2001). For the FL implants, skin incisions were made over the bellies of the Bic, Tri, palmaris longus, EDC and the thenar eminence. The wires were routed to the incision sites as described above and EMG electrodes were implanted in the right medial midbelly of the medial head of the Tri, lateral midbelly of the long head of the Bic, lateral midbelly of the FDS, and lateral midbelly of the FDP, and in the right and left lateral midbelly of the EDC and midbelly of the FPB. The EMG wires were coiled near each implant site to provide stress relief. Back stimulation through the skin button (see below) was used to verify the proper placement of the electrodes in each muscle. In addition, the electrode placement was verified in a terminal experiment at the end of the study. A small incision then was made approximately one inch lateral to the upper back incision. A skin button was passed through the hole and the skin was sutured around the button. To provide stress relief, the wires were looped subcutaneously near the skin button. All incision areas were irrigated liberally with warm, sterile saline and closed in layers, i.e., investing fascia and then the skin. All closed incision sites were cleansed thoroughly with saline solution. Postoperative care consisted of intensive monitoring until the monkeys regained their equilibrium and were able to sit upright. Analgesia was provided by either oxymorphone (0.15 mg/kg, IM, TID) or buprenex (0.5–1.0 mg/kg im, TID). The analgesics were initiated prior to completion of the surgery and continued for a minimum of 3-5 days. The monkeys were monitored closely for food and water intake and were supplemented liberally with fresh fruit and vegetables on a daily basis until a proper appetite resumed. Antibiotic therapy with cephazolin (20 mg/kg im, TID) or cephalexin (30 mg/kg, oral, BID) was initiated preoperatively (given every two hours during the procedure) and continued for 5-10 days. Wound healing was monitored closely by a dedicated veterinary and therapeutics staff. Initial care immediately after surgery consisted of monitoring the transcutaneous exit sites for erythema or exudation. If deterioration of the exit sites was noted, the incision sites were cleansed with dilute Novalsan solution (chlorohexadine) and topical antibiotics were used if deemed necessary by the veterinarian staff. If necessary, systemic antibiotic therapy was initiated with the drug of choice from the case veterinarian. All surgical and experimental procedures in these experiments were carried out using the principles outlined by the Laboratory Animal Care (National Institutes of Health Publication 85-23, revised 1985) and were approved by the Institutional Animal Care and Use Committee (IACUC).

Testing protocols and data collection After a recuperation period of ~2-3 weeks, kinematic data and EMG activity were recorded under the same experimental conditions as during the training procedures. Kinematics. Video recordings (60 Hz) were made using one (Rhesus #1, #2, and #3) or two (Rhesus #4, #5 and #6) cameras (Panasonic System Camera, WV D5100; Panasonic AG1280P Panasonic, Cypress, CA, USA) oriented perpendicular to (one camera), or at 45o and 135o (two cameras) with respect to the direction of the locomotion, i.e., the p.7

Kinematic and EMG patterns during rhesus monkey locomotion animal’s sagittal plane. Before each testing session, a calibration device was placed in the treadmill and recorded. Nontoxic white paint was used to mark shaved areas of skin overlying the following body landmarks (right side): for the HL, the greater trochanter (GT), the knee joint (K), the malleolus (M), the 5th metatarsal (MT) and the outside tip (T) of the fifth digit; for the FL, the head of the humerus (H), the elbow joint (E), the distal head of the ulna (U), the metacarpo-phalangeal (MCP) joint, and the outside tip of the third digit (D) (see Fig. 3). The body was modeled as an interconnected chain of rigid segments: GT-K for the thigh; K-M for the shank; M-MT for the foot; MT-T for the fifth digit; GT-H for the trunk; HE for the arm; E-U for the forearm; U-MCP for the hand; MCP-D for the third digit. In addition, the limb axis was defined as the virtual line connecting the hip to the MT joint, and the shoulder to the MCP joint for the HL and FL, respectively. EMG. Two telemeters (Model T-47, Konigsberg Instruments, Pasadena, CA, USA) were attached to the EMG skin button and placed in two large pouches on the back of the monkey’s jacket. The telemeters weighed ~150 g and did not appear to interfere with the performance of the locomotor task. Output from the telemetry receiver was recorded at 2 kHz on FM tape (TEAC Model XR-510, TEAC, Montebello, CA, USA). A Society of Motion Picture and Television Engineers (SMPTE) time code generator (model F30, Fast Forward Video, Irvine, CA, USA) was used to synchronize video frames with the EMG signals recorded on FM tape for Rhesus #5 and #6.

Data processing Kinematics. Selected video recordings were digitized with a video grabber card and recorded to disc. The Motus software (Peak Performance Technologies, Inc., Centennial, CO, USA) was used to automatically detect the centroid of the (x, y) coordinates of the reflecting points attached to the skin of the monkey. We used these (x, y) coordinates to reconstruct the trajectory of the limb and to calculate joint angles at the hip, knee, ankle, MT joint, shoulder, elbow, wrist, and MCP joint (see Fig. 3). Flexion, plantarflexion (MT), ventro-flexion (wrist, MCP), and retraction (shoulder) were defined as a decrease in the measured angle. The angle of each segment with respect to the direction of gravity (elevation angle) in the sagittal plane also was computed. These angles were positive in the forward direction, i.e., when the distal marker crossed the vertical line passing through the proximal marker.

Spatial and temporal features of the gait pattern. The gait cycle was defined as the time interval between two successive paw contacts of one limb. Successive paw contacts were visually defined by the investigators with an accuracy of ± 1 video frame. Ten or more successive, consistent HL and FL gait cycles were typically recorded from each animal at each treadmill speed. Swing phase onset was set at the zero crossing of the rate of change of the elevation angle of the limb axis, i.e., at the onset of forward oscillation (Borghese et al. 1996; Courtine and Schieppati 2004). Cycle duration was computed for each limb and stance and swing phase durations were expressed as a percentage of the cycle duration. A gait diagram was constructed for each trial and the timing of HL and FL displacements with respect to the right HL gait cycle was determined (Fig. 1). Stride length for each limb was considered to be the linear spatial distance between the malleolus position at successive paw contacts plus the cycle duration multiplied by the treadmill speed. Consequently, the measured stride length directly reflected the actual forward displacement of the limb during a complete gait cycle. We also computed the mean body (limb) speed during each gait cycle as the stride length divided by the cycle duration. We introduced this difference between treadmill speed and mean body speed to take into account the variability in the limb movements (see p.8

Courtine et al. Fig. 2B and C) and the possible drifting of the animal on the moving treadmill belt. The location of the foot with respect to the hand at foot contact (stance onset) was measured as the distance between the position of the HL (toe, T) and FL (finger, F) endpoint markers.

Average angular waveforms. Each data set was time-interpolated over individual gait cycles on a time base with 100 points. Averages were constructed for all gait cycles at each speed for each animal. Velocity-curvature power law. To compute the velocity-curvature relationship, limb endpoint spatial coordinates corresponding to the swing phase of a given limb at a given speed were extracted and pooled. We performed a linear regression analysis in log-log scales of the equation:

ω (t ) = K ⋅ C (t ) β where ω(t) and C(t) are the instantaneous values of the angular velocity and the path curvature of the limb endpoint, respectively, K is a velocity gain factor that depends on overall movement duration, and β is the power exponent. In logarithmic scales, a power function becomes a straight line whose slope corresponds to the exponent (Ivanenko et al. 2002).

Inter-segmental coordination. General procedures have been described elsewhere (Courtine and Schieppati 2004). Briefly, the timing of HL and FL segment oscillations in the sagittal plane was determined through Fast Fourier Transformation (FFT). The phase φ of the first order Fourier series component of each angle was taken as the timing of its oscillation during a given gait cycle. This timing was expressed in percent with respect to the normalized gait cycle duration (φ * 100/2π).

Principal component analysis. We used principal component (PC) analysis to quantify the spatio-temporal structure of the inter-segmental coordination among body segments (Bianchi et al. 1998; Borghese et al. 1996; Courtine and Schieppati 2004). For each set of trial data, the analysis was performed by computing the covariance matrix A of the ensemble of selected time-varying angles over the gait cycle, after subtraction of their respective mean values. The PCs were computed from eigenvalues λj and eigenvectors Uj of A. The PCs were ordered according to the amount of data variance accounted for by each component:

λj n

∑λ

i

i =1

The number of components required to account for data variance was used to provide insight into the complexity of the spatio-temporal structure of the underlying inter-segmental coordination pattern. PC analysis was carried out on timevarying elevations of the HL and FL segments, either separately (n = 4) or together (n = 8). To allow for direct comparisons with data previously reported in humans (Borghese et al. 1996; Courtine and Schieppati 2004), we also applied the PC analysis on thigh, leg and foot elevations angles for the HL, and arm, forearm, and hand elevation angles for the FL. The variance accounted for by PC1 plus PC2 (Borghese et al. 1996) quantified the extent of planarity in the co-variation among the HL or FL segment oscillations (Fig. 6).

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Kinematic and EMG patterns during rhesus monkey locomotion Time shift of limb oscillation with increases in treadmill speed. The cross-correlation function between pairs of timenormalized waveforms was computed to quantify the time shift of kinematic parameters with treadmill speed by means of the following formula:

R xy (∆) =

∫ x(t ) y(t + ∆t )dt ∫ x (t )dt ∫ y (t )dt 2

2

where x and y are the two waveforms (after subtraction of their mean values), and ∆ is the time lag between the two signals. The highest positive correlation and its corresponding time lag were detected and expressed as a percentage of gait cycle duration. Using this method, we calculated time delays of mean elevation angles (HL and FL) at 0.89, 1.34, and 1.79 m/s with respect to those at 0.45 m/s (Ivanenko et al. 2004b).

EMG. Raw EMG signals were band-pass filtered (30 Hz to 1 kHz), rectified, time-interpolated over a time base with 1000 points for individual gait cycles, and averaged. Onsets and ends of EMG burst activity of each muscle recorded during each gait cycle were established at the points at which muscle activity exceeded and fell below, respectively, the mean activity plus 1.5 SD recorded during a period (200 ms) when this muscle was least active (Courtine and Schieppati 2003). A mobile average (40 ms width) was first applied on the signal to reduce the effects of signal oscillation (Courtine and Schieppati 2003). The time between the onset and end of an individual burst was considered the burst duration. The onset and ending points of the EMG bursts were used to determine the relative timing of EMG activity recorded from different muscles. For the HL, cycle period was calculated as the time between the onset points of successive bursts of EMG activity in the Sol muscle. Activity of FL muscles was synchronized with kinematic data: cycle period corresponded to the time interval during two successive paw contacts. Mean EMG amplitude was calculated as the integral of the muscle envelope divided by the burst duration (Roy et al. 1991b). The EMG amplitude of each burst was normalized to the mean burst amplitude of the same muscle when walking at 0.45 m/s, i.e., the slowest treadmill speed.

Statistical analysis. For each animal, we calculated the mean values and standard deviations (SD) of the different parameters over all trials for each experimental condition. Repeated-measures ANOVA’s were used to test the effect of the different conditions on the experimental parameters. The factors examined were the limb (HL, FL) and the treadmill speed (0.45, 0.89, 1.34, and 1.79 m/s). Post-hoc differences were assessed by the Newman-Keuls test. Regression linear analyses were performed to determine the relationships between variables and reported as correlation coefficients.

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Results The spatial and temporal characteristics of gait parameters, locomotion kinematics and muscle activation patterns were analyzed in six adult Rhesus walking quadrupedally over a wide range of treadmill speeds. Gait, kinematic, and EMG descriptors routinely used to describe cat and human walking were derived from recordings of Rhesus locomotion.

Spatial and temporal characteristics of the Rhesus gait pattern and their relationship with treadmill speed The animals used a diagonal footfall sequence when walking over the imposed range of speeds, i.e., the footfall of a FL usually followed that of the contralateral HL. As a consequence, the stance phase of a diagonally-opposed pair of limbs occurred approximately at the same time, and in general, either four or two limbs were simultaneously supporting body weight at the slower and higher speeds, respectively (Fig. 1). An interesting consequence of this diagonal footfall sequence was that the foot contacted the treadmill belt in close proximity to the ipsilateral hand. The distance between HL and FL endpoints was approximately equal to zero at HL footfall for all speeds (Fig. 2D). Conversely, the FL contacted the treadmill belt near the end of the HL stance phase, i.e., when the HL segments reached a backward position, and the interlimb distance was substantial at this time (limb effect, p < 0.001). Despite the fact that the Rhesus generally used a diagonal footfall sequence, the precise timing of FL footfall with respect to HL footfall varied appreciably with speed (Fig. 2A). The interlimb timing was quantified by relating the time of footfall of either limb relative to two successive footfalls of the right HL. With increased treadmill speed, the footfall of the contralateral FL (black circles) and the ipsilateral FL (grey circles) was delayed with respect to the ipsilateral HL cycle duration. For example, the footfall of the left FL could precede, coincide with, or occur consistently after the footfall of the right HL footfall with increasing treadmill speed (Fig. 1A). However, the two FLs maintained a near-perfect out of phase coupling over the range of speeds studied. Changes in the timing of footfall with increasing speeds were virtually identical for the two FLs (left and right FL best-fitting lines are parallel in Fig. 2A). Likewise, contralateral HL footfalls (open circles) occurred at half of the ipsilateral HL cycle duration (49 ± 3%) regardless of the actual body velocity. One Rhesus (#5) used a lateral footfall sequence at the three faster speeds in which a FL footfall followed the ipsilateral HL footfall. In Fig. 2A the triangle symbols represent the timing of ipsilateral FL footfall for this Rhesus. Figure 2B depicts the relationship between the cycle duration and the mean body velocity for the right HL and FL. A monotonic decrease in cycle duration accompanied an increase in mean body velocity (speed effect, p < 0.001), which was nearly identical for the HLs and FLs (limb effect, p > 0.10). Accordingly, the HLs and FLs displayed similar stride lengths under comparable treadmill speeds (Fig. 2C) (limb effect, p > 0.10). In spite of the observation that HL and FL cycle duration and stride length progressed similarly, systematic differences were detected between the duration of their stance phases (speed * limb interaction effect, p < 0.05). Except at the slowest speed (p= 0.30), stance duration was significantly longer for the HL than the FL (Fig. 2E, all post hoc comparisons, p < 0.05). This difference between HL and FL duty factors is highlighted in Fig. 2F where the stance and swing durations are plotted vs. the total duration of the cycle for Rhesus #1. p.11

Kinematic and EMG patterns during rhesus monkey locomotion

Spatial and temporal characteristics of HL and FL kinematics and their relationship with speed of locomotion Figure 3 displays the average (±SD) joint angle waveforms at each joint of the HL and FL recorded at each treadmill speed in a representative Rhesus (#4). Each joint angle of the HL and FL changed cyclically within a single step and the time course for the changes was consistent for the six Rhesus. We observed a gradual decrease in stance duration (Fig. 2E and F) and a progressive lengthening of the stride (Fig. 2C) with increases in treadmill speed. These progressive changes in spatial and temporal gait parameters were reflected in the graded adjustment of the timing and amplitude of the HL and FL joint angles (Fig. 3). Changes in timing with increases in treadmill speed included an earlier maximal joint extension of all HL and FL angles that paralleled the decrease in stance phase duration (compare Fig. 2E and F with Fig. 3). The range of joint excursions increased as treadmill speed increased for both the distal and proximal segments of the HL and FL (Fig. 3). The increase in amplitude of HL joint angles with increasing treadmill speed was significant at all joints (speed effect, p < 0.01). This modification was progressive and significant correlations were detected between the mean body velocity and the joint angle amplitude at the hip (r = 0.82 ± 0.05), knee (r = 0.88 ± 0.05), ankle (r = 0.90 ± 0.06), and MT (r = 0.88 ± 0.07). In the FL, an increase in the amplitude of the joint angles with increased treadmill speed was mainly located at the shoulder (speed effect, p < 0.01), and at the elbow, though to a lesser extent (speed effect p < 0.05). A significant correlation between treadmill speed and amplitude of joint angle changes was observed only at the shoulder joint (r = 0.72 ± 0.10). Superimposed trajectories of the HL and FL endpoints during the swing phase for six consecutive step cycles at each treadmill speed are shown in Fig. 4A. Treadmill speed-related increases in the length (speed effect, p < 0.01) and height (speed effect, p < 0.01) of the path of the endpoint trajectories are seen for both the HL and FL. Ivanenko et al. (2002) showed that in human locomotion, the foot trajectory obeys the so-called 2/3 power relationship between the instantaneous curvature (C) and angular velocity (Ω), i.e., the exponent β of the Ω—C relationship is very close to two-thirds (see Materials and Methods). We assessed whether the same relationship characterizes HL and FL endpoint trajectories in the Rhesus. As in humans, the correlation value of the linear regression were very high for all Rhesus, regardless of the limb and speed (r = 0.98 ± 0.01). The typical Ω—C relationship obtained from all steps performed by a representative Rhesus (#4) at 0.89 m/s is depicted in Fig. 4B for the HL (left panel) and FL (right panel). The exponent β was very close to 2/3 for both the HL and FL at all treadmill speeds (Fig. 4C). Nevertheless, β was significantly closer to 2/3 when regressions were computed for the FL compared to the HL endpoint trajectories (limb effect, p < 0.05). This difference was small (mean β HL minus mean β FL was 0.03 ± 0.02), but consistent across animals.

Inter-segmental coordination patterns between the HL and FL We investigated the inter-segmental coordination among the HL and FL segments during quadrupedal walking in the Rhesus as previously described for humans (Courtine and Schieppati 2004). Figure 5A displays the average (for 6 Rhesus) waveforms of the HL and FL elevation angles and at each treadmill speed studied. In addition, the 3-D gait loops obtained p.12

Courtine et al. by plotting these elevation angles vs. each other are shown for Rhesus #4 in Figure 6A. Spatio-temporal modifications of HL and FL segment oscillations with increasing speed were assessed by computing the cross-correlation function between the mean angular waveforms at 0.89, 1.34, and 1.79 m/s relative to those at 0.45 m/s in each animal (see Methods and Materials). Phase lag and correlation coefficients quantify the changes in timing and shape of the elevation angles with increasing speed, respectively (Fig. 5B). The correlations (insets of Fig. 5B) were very high across speeds for all HL and FL elevation angles, although r values tended (p < 0.1) to decrease for the distal segments of both limbs. Indeed, the amplitude of distal segment backward oscillation generally increased at the highest treadmill speed. An increase in speed was generally associated with a progressive lead in the time of maximal backward oscillation, i.e., end of stance. This shift corresponded to the decrease in stance duration (filled squares in Fig. 5B), and was more pronounced for distal compared to proximal segments. Vertical lines joining the data points to the grid in the plots in Figure 6A indicate the distance between the 3-D gait loop and the best-fitting plane obtained by linear regression: systematic deviations from a perfect plane were detected for both the HL and FL segments. The mean (SD) value (for 6 Rhesus) of the index of planarity (see Materials and Methods) is reported above each 3-D plot, where a value of 100 corresponds to a perfect plane. The size of the 3-D gait loop described by HL elevation angles increased considerably at the higher speeds, as a consequence of the combined increase in amplitude and phase lag among segment oscillations (Fig. 5A and B), whereas the spatial orientation and general shape were unaffected. The coupling between the oscillations of HL and FL segments was examined further through principal component (PC) analysis (Courtine and Schieppati 2004). PC analysis was performed on time-varying elevation angles of the HL and FL during complete gait cycles, either independently for each limb (n = 4), or simultaneously (n = 8). Mean values of the variance accounted for by each PC are shown in Figure 6 B, C, and D: the higher the variance explained, the stronger is the linear co-variation between spatio-temporal changes of segment angles (Bianchi et al. 1998). Four main results emerged from this analysis: (1) the variance accounted for by PC1 was generally high when PC analysis was applied independently on either the HL or FL datasets (Fig. 6B). PC1 explained more than 80% and PC1 plus PC2 about 98% of the variance in the data. (2) The variance accounted for by PC1 was higher (limb effect, p < 0.005) for HL (86 ± 2%) than FL segments (77 ± 3%). (3) When PC analysis was performed simultaneously on the HL and FL segments, PC1 plus PC2 generally accounted for nearly all of the variance in the data, i.e., mean of 91% (range, 85 to 94%) (Fig. 6C). (4) In this latter analysis (n=8), the variance accounted for by PC1 plus PC2 increased linearly with increasing body velocity (speed effect, p < 0.0001) (Fig. 6D).

Amplitude and timing characteristics of HL and FL muscle activity and their relationship with treadmill speed The averaged EMG activity recorded from selected HL (Rhesus #1, #2, and #3) and FL (Rhesus #5 and #6) muscles (Table 1) is plotted vs. the normalized gait cycle duration at each treadmill speed (Fig. 7). Figure 8 shows how the characteristics of the HL and FL muscle bursts, i.e. integrated (time-normalized integral) EMG activity and timing, are modulated with changes in cycle duration.

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Kinematic and EMG patterns during rhesus monkey locomotion HL muscles. HL muscle activities were very consistent across the three Rhesus (#1, #2 and #3). The pattern of HL muscle activity typically showed an alternate activation of extensor and flexor muscles during the stance and swing phase of gait, respectively. The onset of extensor activity began slightly before the time of paw contact as reported previously in cats (Rasmussen et al. 1978). The VL and MG generally were activated prior to Sol activation (Fig. 7). The duration of extensor muscle bursts gradually decreased with a decrease in cycle and, therefore, stance duration (see Fig. 2E). The level of EMG activity was unchanged in the Sol and slightly increased (p > 0.05) in the VL with increasing velocities. In contrast, the integrated activity of the MG bursts increased as much as 6-fold (Rhesus #2) when locomoting at 1.79 vs. 0.45 m/s (p < 0.001). FDL and FHL, which are close anatomical synergists, were differentially recruited during walking. The FHL muscle produces toe flexion and plantarflexion torque at the ankle, and was activated concomitantly with the Sol muscle throughout the stance phase of gait. The duration of the FHL EMG burst (Fig. 8) decreased with increasing velocities (decreased stance phase). The integrated EMG activity of the FHL muscle sharply increased as a function of speed (p < 0.001). FDL burst onset did not coincide with the onset of the extensor muscles, i.e., the FDL was activated near early-mid stance and ceased its activity around the time of toe-off. This pattern of activity was observed in both Rhesus #2 and #3. Speed-related modulation of FDL EMG activity was substantial (Figs. 7 and 8); FDL burst integral increased (p < 0.001) as much as 20-fold when walking at 1.79 vs. 0.45 m/s (Rhesus #3). The TA was activated during the swing phase (Fig. 7). The duration of the TA burst was roughly invariant over the range of speeds studied, whereas the EMG integral gradually increased with speed (p < 0.001) (Figs. 7 and 8). The EDL burst was initiated with the TA burst at the onset of swing, but its activity persisted after TA burst extinction (Fig. 7). Therefore, the EDL was co-activated with ankle extensors during ~10-20% of the cycle duration, depending on the Rhesus and the speed of locomotion. EDL burst duration decreased modestly (p < 0.01) with a decrease in cycle duration (Fig. 8). The EMG burst integral gradually increased (p < 0.001) with a decrease in cycle duration (Fig. 8). FL muscles. FL muscle activity is shown for both Rhesus #5 and #6 to illustrate individual differences as well as similarities. Both Rhesus exhibited the same reciprocal activation pattern between the Tri and Bic in the FL as between the Sol/MG and TA in the HL. Activation of Tri began prior to (Rhesus #5) or at (Rhesus #6) paw contact, and persisted throughout the stance phase. As observed in the ankle extensors, the EMG burst duration of the Tri gradually decreased with a decrease in cycle duration. The integral of the Tri EMG burst gradually increased with an increase in treadmill speed (p < 0.01) (Fig. 8). However, the speed-related increase in Tri recruitment was not as large as that observed in the MG muscle (p < 0.05). Bic activity was initiated around the time of paw-off and terminated before (Rhesus #5) or at (Rhesus #6) paw contact. As observed in the TA, the Bic burst duration was constant over the range of speeds studied (Fig. 8). In turn, both Rhesus showed a sizeable increase (p < 0.001) in Bic activity with speed (Figs. 7 and 8). We recorded the EMG activity of four muscles acting at the wrist, digits, and thumb. FDP and FDS, which both produce a flexion of the wrist and digits, were activated throughout stance (Fig. 7). However, both the left (not shown) and right FDP muscles exhibited an additional burst of activity starting immediately after paw-off. In Rhesus #5, we found a high level of EMG activity in the FDS muscle during the stance phase, whereas the muscle was recruited modestly (low speed) or was quiescent (high speed) during swing. In contrast, the FDS EMG activity recorded in Rhesus #6 was dramatically larger during the swing phase than during the stance phase. The FPB is a thumb flexor. FPB activity was initiated just after paw p.14

Courtine et al. contact, and ceased before paw-off, i.e., when the wrist started flexing (Fig. 3), thereby raising the thumb off the treadmill belt. The EDC extends the wrist and digits. In Rhesus #5, EDC showed a modest, short burst occurring around paw contact and was followed by a period of tonic activity that ended with swing onset (Fig. 7). This latter activity was particularly high at the fastest treadmill speed. The recruitment of EDC began near the end of swing (90 ± 1.4%) in Rhesus #6, and ended when the hand and digits were laid flat on the treadmill belt (20% and 12% of the cycle period when walking at 1.79 vs. 0.45 m/s; Fig. 3). Characteristics of digit muscle activity were gradually modulated with increasing speed, both in timing and amplitude. All digit muscles recorded in Rhesus #5 were active during the stance phase of gait (Fig. 7). Accordingly, the burst duration decreased with increased treadmill speed (Fig. 8). In contrast, FDP (swing-related burst) and EDC burst durations were invariant over the range of speeds studied in the Rhesus #6. The burst integrals were significantly (p < 0.05) correlated with the cycle duration in all digit muscles of both animals. The level of EMG activity significantly increased as a function of speed in all of the digit muscles (p < 0.001), particularly in the FDS and FDP during stance and swing, respectively (Fig. 8).

Coordination of muscle activity We further investigated the temporal tuning of FL muscle activity by scrutinizing the relationships between the temporal features of their bursts and the timing of FL and HL segment oscillations (Fig. 9). Temporal burst features related to the end of stance were selected because modifications in stance duration reflected the speed-related changes in the temporal structure of the gait pattern (Fig. 5B). The timing of HL and FL segment oscillations was computed through FFT analysis (see Methods and Materials), and was expressed as a percent of the normalized gait cycle duration. An increase in the value of the timing indicates that the segment oscillation peaked earlier with respect to the normalized gait cycle duration. This typically corresponds to a decrease in stance duration, i.e., an increase in the speed of movement (Fig. 5B). Fig. 9 shows the relationship between the temporal features related to the end of stance for all bursts from the right FL muscles and the timing of FL (top) and HL (bottom) segment oscillations. These relationships were computed from gait cycles recorded from Rhesus #5; however, similar relationships were observed for Rhesus #6. The timing of FL muscle activity was significantly (p < 0.01) correlated with the timing of all FL and HL segment oscillations. The relationships associated with each HL and FL segment are depicted with different symbols to emphasize the lag between oscillations of the different segments. The results indicate that any modification in FL muscle timing was coordinated on a cycle-to-cycle basis with similar changes in the timing of all HL and FL segments. Finally, we explored how the CNS coordinates the tuning of the motor patterns to increase locomotor velocity. Figure 10 shows the modulation of the burst integral of each muscle in the HL relative to the burst integral of the Sol and TA and of each muscle in the FL relative to the burst integral of the Bic and Tri. The Sol and TA and Tri and Bic muscles were selected as a reference for the HL and FL, respectively, because similar reciprocal activation and speed-related modulation of burst characteristics were detected for each extensor-flexor muscle pair. Burst integral of each muscle was normalized to p.15

Kinematic and EMG patterns during rhesus monkey locomotion its mean value computed during walking at the slowest treadmill speed (0.45 m/s). These values were not time-normalized since each burst integral was computed during the same gait cycle. Significant correlations (p < 0.05) were detected between the burst integral of the right Sol and TA and those of the right and left thigh and shank muscles. The relationships tended to be less robust when considering the distal HL muscles. In contrast, the correlations between the activities of individual FL muscles were very low for Rhesus #5 and inconsistent (i.e. low or high) for Rhesus #6.

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Courtine et al.

Discussion We examined the spatial and temporal characteristics of gait parameters, locomotion kinematics, and associated patterns of HL and FL muscle activity during quadrupedal treadmill locomotion over a wide range of speeds in the Rhesus monkey. Our results point out a number of similarities, but also some differences, in the kinematic and EMG determinants sustaining Rhesus locomotion compared to sub-primate mammals and to humans. The adaptation process of non-human primates to an arboreal environment thus involved a number of changes in gait control including alterations to muscle recruitment and the associated patterns of interlimb and intralimb coordination. Substantial changes either in the properties of the steppingrelated spinal neuronal circuitry or in the level of supraspinal control, or both necessarily accompanied these modifications of the locomotor program (Capaday 2002; Duysens and Van de Crommert 1998; Nielsen 2003; Vilensky and O'Connor 1997).

Interlimb coordination One of the most distinctive aspects of primate quadrupedal walking is the frequent use of diagonal sequence footfalls (Fig. 1) in combination with diagonal-couplet interlimb timing (Fig. 2A). Most other quadrupedal mammals use a lateral sequence footfall pattern (Hildebrand 1967). Evolutionary correlates of primate-specific interlimb coordination have been addressed in anthropological studies. Biomechanical analyses suggest that the diagonal sequence gait increases body stability in monkeys due to the posterior location of their center-of-mass (Kimura 1985; Schmitt 2003; Vilensky 1989). Furthermore, Cartmill et al. (2002) argued that monkeys use a diagonal sequence footfall pattern to place the grasping hindfoot in a protracted position and on an already-tested support at touchdown: the distance between the ipsilateral foot and hand is roughly null at HL contact (Fig. 2D). Consequently, the supporting hindfoot is advantageously located underneath the animal’s center of mass when the contralateral hand strikes the ground on an untested, arboreal support. Interestingly, humans exhibit a similar diagonal-coupled leg-to-arm coordination when creeping, swimming, or walking erect on two legs (Wannier et al. 2001). Investigations in cats (Miller et al. 1975) and rats (Butt et al. 2002; Butt and Kiehn 2003) disclose some neural elements responsible for interlimb coordination during locomotion. These studies show that short-range commissural interneurons ensure left-right coordination, whereas long-range propriospinal interneurons connect lumbar and cervical enlargements and assist coupling between the HL and FL (Grillner 1981). In the current study, we generally observed a high correlation in the modulation of the EMG activity in the left and right limbs, although this relationship was stronger between the lumbar compared to the cervical motor pools (Fig. 10). Moreover, the temporal features of the proximal and distal FL muscle recruitment patterns were highly coordinated with the timing of the oscillations of all the segments of both the FL and the HL (Fig. 9). Finally, principal component analysis applied simultaneously on elevation angles of all HL and FL segments revealed a high degree of coupling in the generation of limb oscillations during Rhesus locomotion (Fig. 6C and D). Thus similar neural mechanisms could operate in the spinal cord of sub-primates and primates to ensure inter-limb coordination during quadrupedal walking. Indeed, the existence of long projecting propriospinal neurons coupling lumbar and cervical enlargements has been demonstrated not only in non-human primates (Molenaar and Kuypers 1978) but also humans (Nathan et al. 1996). Accordingly, recent studies also provided evidences for a strong coupling in the control of the

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Kinematic and EMG patterns during rhesus monkey locomotion two leg movements (Courtine and Schieppati 2004; Ting et al. 2000) and, though to a less extent, between the neuronal processes generating leg and arm oscillations during human locomotion (Dietz et al. 2001; Zehr and Duysens 2004). Nevertheless, similar mechanisms for interlimb coordination in mammals do not account for two important specific features of primate locomotion: 1) the emergence of a diagonal coupling pattern, and 2) the high versatility in the control of limb movements. Indeed, walking in the primate species is characterized by frequent changes in interlimb coupling, e.g., to grasp a supporting branch or an object while locomoting (see Fig. 5 in D'Aout et al. 2004). Interestingly, juvenile monkeys preferentially use lateral gait when walking in an arboreal environment, and then shift to a diagonal gait after maturation of the descending tracts (Dunbar and Badam 1998). Furthermore, interruption of the descending spinal pathways results in severe disruption of interlimb coupling during locomotion (Vilensky et al. 1992). It seems that such flexibility in interlimb coordination and associated postural regulation (Mori et al. 2004) requires a significant contribution from supraspinal control mechanisms (Drew et al. 2004). The theoretical analysis of coupled oscillator-based interlimb coordination during quadrupedal locomotion stipulates that versatility in limb movements can be achieved easily by adjusting the phase relationships between the neural oscillators controlling HL and FL movements (Schoner et al. 1990). It is plausible, therefore, that supraspinal commands modulate the coupling between propriospinal neuronal networks to regulate interlimb coordination during locomotion. For example, Jankowska et al. (2003) showed that lumbar commissural interneurons are monosynaptically activated from the ipsilateral reticular formation in cats. Indeed, cerebellar contributions to interlimb coordination via reticulospinal neurons occur in both cats (Armstrong 1988) and humans (Morton and Bastian 2004).

Intralimb coordination Lacquaniti and co-workers (Lacquaniti et al. 2002) demonstrated the oscillation of lower limb segments with respect to the direction of gravity do not evolve independently of each other during walking in humans. On the contrary, a kinematic law of planar co-variation among lower limb segment oscillations characterize intralimb coordination pattern during a variety of locomotor tasks (Bianchi et al. 1998; Borghese et al. 1996; Courtine and Schieppati 2004). In comparison with human gait, the elevation angles of thigh, shank, and foot segments did not evolve close to a plane during non-human primate locomotion, nor did the elevation angles of the FL segments (Fig. 5A). Furthermore, the 3-D gait loop size described by HL elevation angles increased greatly with speed, as a consequence of larger segment oscillations (Fig 5A), whereas its spatial orientation changed minimally in the Rhesus. In contrast, the plane of angular co-variation systematically rotates and the 3D gait loop shape varies little for the production of higher velocities in humans (Bianchi et al. 1998). This corresponds to a parametric tuning in the phase-relationship of inter-segmental coordination (Courtine and Schieppati 2004) that is used by the nervous system to optimize pendulum-like movements, and thereby limit the overall energy expenditure in humans (Bianchi et al. 1998). The current results, therefore, suggest that the strategy by which the CNS achieves inter-segmental coordination in nonhuman primates and adapts its spatio-temporal structure to increase speed differ somewhat from the kinematic principles that operate in human gait control. Monkeys do not achieve optimum inverted pendulum-type gait (D'Aout et al. 2004), nor do human toddlers (Ivanenko et al. 2004a) during their first steps. Development of pendulum mechanisms in infants correlates with the emergence of planar p.18

Courtine et al. co-variation among lower limb oscillations (Ivanenko et al. 2004a). We, thus, conclude that control of bipedal, erect walking movements likely required further constraints on the control of inter-segmental coupling to stabilize the vertical trunk (Hirasaki et al. 2004) and to optimize energy-saving pendulum movements (Ivanenko et al. 2004a). Such differences in intralimb coordination strategy must be taken into consideration when studying bipedal walking in normally quadrupedal animals (D'Aout et al. 2002; Mori et al. 2001) since we have to assume that the human is not a monkey walking on two legs, and that refinement in the organization of motor control centers has undoubtedly taken place during the evolution toward habitual bipedalism (Nielsen 2003).

Speed-related changes in gait parameters and muscle activity Modulation of spatial and temporal characteristics of gait and muscle activity with respect to speed has been thoroughly studied in some mammals, especially in cats (Grillner 1981) and humans (Nilsson et al. 1985). These studies highlight the common organizational principles by which the stepping-related motor program smoothly adapts to an increase in locomotor velocity. Our results show that these same neural strategies operate during Rhesus locomotion. As in other mammals, an increase in body speed was associated with a monotonic decrease and increase in cycle period duration and stride length, respectively, which was virtually identical for the HL and FL (Fig. 2; Mori et al. 1996; Vilensky 1983). Moreover, an increase in treadmill speed typically involved a graded decrease in the EMG burst duration of extensor muscles that paralleled a decrease in stance duration (Figs. 2F and 8). In contrast, the durations of flexor muscle bursts and of the swing phase remained almost constant over the range of speeds studied (Fig. 2F and 8). This speed-related modulation was observed in both the HL and FL muscles, and a similar reciprocal activation pattern was detected between the Sol/MG and TA muscles in the HL, and the Tri and Bic muscles in the FL. These observations are consistent with the idea that the ankle and elbow joints share a similar function during locomotion in quadrupeds (English 1978; Rossignol 1996). Another similarity between the Rhesus and other mammals was the differential recruitment of slow vs. fast muscles with respect to speed of locomotion. For example, the MG muscle, comprised of a high proportion of fast, fatigable motor units (Roy et al. 1991a), was more heavily recruited at the faster than the slower speeds of locomotion, whereas the predominantly slow Sol muscle was already heavily recruited at the slower treadmill speeds (Figs. 7 and 8). Similar observations have been reported in the Rhesus (Recktenwald et al. 1999) as well as in rats (Roy et al. 1991b; de Leon et al. 1994) cats (English 1984; Pierotti et al. 1989), and humans (Nilsson et al. 1985). This coordinated, speed-related modulation of HL and FL muscle activity may arise from similar mechanisms in Rhesus and in cats, i.e., velocitydependent tuning of spinal circuits via the brainstem tonic commands and the phasic afferent input signaling hip extension (Grillner and Rossignol 1978) and loading of the leg (Duysens and Pearson 1980). For example, Shik et al. (1966) showed that electrical stimulation of brainstem nuclei in decerebrate cats evokes quadrupedal locomotor activity and that the stepping rate depends on the intensity of the stimulation. Eidelberg et al. (1981) similarly detected a ‘positive site’ for stimulation in the posterior subthalamic region that elicited locomotor movements in monkeys. Stronger stimulation intensities increased the stepping rate, and even provoked a shift from a walking to a galloping gait.

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Kinematic and EMG patterns during rhesus monkey locomotion

Control of the support and propulsion phases of locomotion In the current study, we observed a variety of differences in the operative principles regulating speed-related adjustments of HL and FL movements during Rhesus locomotion. For example, an increase in treadmill speed typically involved a larger decrease in stance duration, and thus extensor burst duration, in the FL than the HL (Fig. 2E). Moreover, the increase in extensor muscle recruitment was greater in the HL than the FL. This substantial, speed-related tuning of HL EMG activity was most noticeable in the predominantly fast leg muscles (see above) as well as in the muscles functioning at the distal joints such as the FHL and FDL (Fig. 8). In the cat, the FDL exhibits a phasic burst at swing onset to produce the very early plantarflexion of the digits needed to clear the paw from the ground (Fleshman et al. 1984; O'Donovan et al. 1982). In the Rhesus, the FDL muscle was recruited during more than one-half of the stance phase, and its activity increased as much as 20-fold at the faster compared to the slower treadmill speeds. This modulation of ankle and digit muscle activity was reflected in the monotonic increase in the excursion of the distal joints (Fig. 3), as well as in the gradual lag of the distal with respect to the proximal segment oscillations (Fig. 5B) with increased treadmill speed. In contrast, speed-related changes in the amplitude of the FL oscillations were limited, and mainly located at the shoulder joint (Fig. 3; Larson 1998). All of these differences between the HL and FL are aspects of the same adaptive processes, and provide evidence that the HL muscles generate a majority of the force necessary to support and propel the body mass during quadrupedal locomotion in the Rhesus. Kimura (1985) likewise concluded from kinetic recordings that the HLs carry most of body weight during walking in non-human primates, whereas the FLs primarily steer the animal. Such specialization of the HL for propulsion and support may result from evolutionary pressure to reduce compressive forces on the FL (Larney and Larson 2004; Schmitt 1999). It is also conceivable that this active use of the HL digits along with the generation of substantial forces to keep up with faster treadmill speeds may require more contribution from supraspinal control systems (Mori et al. 1996).

Control of limb oscillation Differences in the recruitment of HL vs. FL muscles in Rhesus compared to other quadrupedal mammals, were detected not only during the support phase, but also during the period of limb oscillation. For instance, the speed-related increase in EMG amplitude was greater in the Bic than its counterpart, the TA (Fig. 8). The compliant gait used by monkeys to reduce peak locomotor stresses on the FL is reflected in substantial elbow yield during stance (Larson et al. 2000; Schmitt 1999). Therefore, the increased level of recruitment of the Bic in the beginning of swing may be linked to clearing the paw from the ground while the HL muscles are generating forces to propel the body forward (Fig. 3). Similarly, the recruitment of the FDP early in swing (Fig. 7) contributes to the rapid and pronounced MCP flexion that follows paw lift (Fig. 3). The emergence of FDP activation during the swing phase of primate locomotion thus correlates with the development of FL digits (Okada 1985). Similar anatomical modifications of the foot digits may have necessitated adaptations in distal HL muscle activity patterns. Indeed, we found that the EDL in the Rhesus is not strictly activated during the swing phase of gait as has been observed in other quadrupedal mammals (Trank et al. 1996). Instead, the EDL has a prolonged burst of activity and, similar to the EDC in the FL, is co-activated with extensor muscles during early stance. This activation pattern of physiological flexors (Rho et al. 1999) of the HL (EDL) and FL (EDC) probably prevents the slender digits from dragging along the walking surface until there is complete loading of the foot and hand. Interestingly, activation of dorsiflexors at the beginning of stance is required during human walking to achieve heel strike first (Nilsson et al. 1985). p.20

Courtine et al. The emergence of prolonged activation in distal flexor muscles following foot contact during monkey locomotion, therefore, may have created a favorable context for the development of bipedal gait mechanisms.

Comparison with cat locomotion Investigations performed in cats have provided most of our knowledge on the neuronal control of locomotion in mammals (reviews in Drew et al. 2004; Edgerton et al. 2004; Orlovskii et al. 1999; Pearson 2004; Rossignol 1996). Therefore, it is important to emphasize the differences in the kinematic and EMG determinants underlying cat vs. Rhesus quadrupedal stepping. Although cats and Rhesus share a number of common gait features, there are three important differences: 1) the inter-limb coupling; 2) the different modulation of HL vs. FL gait features with speed; and 3) the EMG patterns of HL and FL distal muscles. Indeed, cats, as most mammals, use a lateral footfall sequence during walking (Halbertsma et al. 1976). Moreover, contrary to Rhesus (Figs. 2 and 8), the durations of the flexor and extensor phases change to the same extent in the HL and FL with increasing speed in the cat (Halbertsma et al. 1976). Accordingly, speed-related increases in the amplitude of HL joint angle changes and the level of HL muscle activity are larger in Rhesus (present results) than in cats (Halbertsma et al. 1976; Rossignol, 1996; Jiang and Drew 1996), particularly in the distal extremities. In the same vein, vertical reaction forces are larger on the FL than the HL during cat locomotion (Manter 1938; Lavoie et al. 1995), whereas the inverse ratio characterizes non-human primate stepping (Kimura, 1985). Similar to that observed in monkeys, however, the cat HLs provide propelling forces for a longer period than the FLs, and the FLs are used mainly for braking (Lavoie et al. 1995). This substantial contribution of HL muscles to body propulsion in Rhesus at higher speeds is reflected in the dramatic modulation of distal muscle activity, such as the FDL and FHL muscles, which is not observed in cats (Fleshman et al. 1984; O'Donovan et al. 1982). Finally, flexor (FDL) and extensor (EDL) muscles of the HL show different recruitment patterns in Rhesus vs. cats (Fleshman et al. 1984; O'Donovan et al. 1982; Trank et al. 1996). Likewise, whereas the EDC exhibits a phasic burst at the swing to stance transition during locomotion in the cat (Drew 1993) as observed in Rhesus, significant differences were noted in the recruitment of distal flexor muscles of the FL. The FDP muscle is activated only during the support phase during cat locomotion, whereas a second burst emerges during the swing phase in the Rhesus. Accordingly, the amplitude of flexion of the MCP joint at swing onset is not as large in cats (Drew and Kably, cited in Rossignol 1996) as in Rhesus. These changes in the recruitment pattern of distal muscles of the HL and FL during Rhesus locomotion are interesting since they are associated with an increased representation of the same muscles in the motor cortex (Park et al. 2004). We discuss in the next section that such adaptive modifications may correlate with an increase in the level of supraspinal control of gait in the Rhesus vs. the cat.

Adaptation in neurobiological control mechanisms We showed that inter-muscular coordination tended to be less consistent when considering the activity of the distal muscles of the HL and FL (Fig. 10), i.e., the muscles showing primate-specific activation. This would be expected if the cortical input superimposes its neural drive upon the ongoing, spinally-generated motor activity in order to adjust the amplitude of the motor pool activity of the distal musculature in a step-to-step basis, as originally proposed by Kuypers (1978). We found that the generation of distal HL and FL trajectories generally obeys the relationship between instantaneous curvature and angular velocity (known as the two-thirds power law) similar to that observed in humans (Ivanenko et al. 2002; Lacquaniti et al. 1983) (Fig. 4). This law is derived from principles of optimum endpoint control (Harris and Wolpert 1998), and is reflected in the neuronal activity of the primate motor cortex during manual tracking (Schwartz and Moran p.21

Kinematic and EMG patterns during rhesus monkey locomotion 1999). The existence of such rules is consistent with the notion that the motor cortex may actively control the activity of distal muscles during primate gait. Accordingly, Georgopoulos and Grillner (1989) postulated that the same neural mechanisms are involved in voluntary reaching and accurate positioning of the limb during locomotion in primates. Increased cortical control of stepping-related limb movements may have been critical for successful travel in a discontinuous, complex small-branch arboreal environment (Larson et al. 2000). Furthermore, this increased “voluntary” control of distal limb movements may have created favorable conditions for the development of fine manipulatory ability independent of locomotion (Georgopoulos and Grillner 1989), and the evolution toward bipedalism. Nonetheless, only direct recordings of stepping-related pyramidal neuron activity could provide the opportunity to address this hypothesis (Drew et al. 2002).

Conclusions The organization of gait control mechanisms in Rhesus show a number of similarities with other terrestrial animals, suggesting that common principles operate during stepping among a wide range of mammals. However, the non-human primate exhibits differences in interlimb and intralimb coupling, hindlimb and forelimb EMG patterns as well as propulsive and support systems compared to sub-primate animals. Some of the major differences are: 1) the frequent use of diagonalcouplet interlimb timing; 2) the more important propulsive role of the HL compared to the FL with increase in locomotor velocity; 3) the novel recruitment pattern of some distal HL and FL muscles; and 4) the limb-specific neural control strategies in the inter-segmental coordination patterns and limb endpoint trajectories. The current analyses of kinematic and EMG determinants of Rhesus locomotion provide a baseline for future comparisons of neuromotor properties following spontaneously occurring dysfunctions and selected experimental interventions.

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Kinematic and EMG patterns during rhesus monkey locomotion

Legends Figure 1. Footfall patterns Gait diagrams obtained from a typical Rhesus (#1) locomoting at 0.45, 0.89, 1.34, 1.79 m/s, from top to bottom, respectively. Five complete gait cycles of the right hindlimb (HL) are represented for each treadmill speed. Each box indicates the time during which a given limb is in contact with the ground (stance phase). The histograms on the left show the percentage (± SD) of time during which 1, 2, 3, or 4 limbs are simultaneously in contact with the ground during a complete right HL gait cycle.

Figure 2. Spatial and temporal features of the gait pattern A. Relationship between the mean body velocity and the time that the contralateral (left) HL (LHL), ipsilateral (right) forelimb (RFL), and contralateral (left) FL (LFL) contact the ground relative to right HL contact expressed as a percentage of the right (ipsilateral) HL cycle duration. The shaded area indicates the duration during which the right HL is in contact with the ground (stance). The triangles depict data from Rhesus #5 (see text for details). B. Relationship between mean body velocity and cycle duration for the right HL and FL obtained from a representative Rhesus (Rhesus #1). Mean correlation coefficient values (±SD) computed independently for the HL and FL in each Rhesus (n=6) are shown. C. Relationship between mean body velocity and stride length for Rhesus #1. Mean correlation coefficient values (±SD) computed independently for the HL and FL in each Rhesus (n=6) are shown. D. Mean values of the horizontal distance between the endpoints of the right HL and FL at the time of contact (see inset). Bars with asterisk indicate significant differences between HL and FL E. Mean values of the stance phase duration for the HL and FL expressed as a percentage of cycle duration for each treadmill speed. Significant differences between HL and FL are reported. F. Relationship between cycle duration and the duration of the stance and swing phases for the HL and FL for Rhesus #1. Mean correlation coefficients values (±SD) computed independently for the HL and FL in each Rhesus (n=6) are shown (p < 0.05 for all relationships).

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Figure 3. Time course of hindlimb and forelimb joint angle changes during the gait cycle The mean waveforms (thick line ± SD, thin lines) of each joint angle recorded in a representative Rhesus (#4) during each complete step cycle (n=10) at each treadmill speed is plotted vs. the normalized cycle duration. The horizontal bars at the bottom indicate stance phase duration at each speed. The stick diagrams at the top show the successive HL and FL positions during the stance and swing phases reconstructed from a representative step cycle at 0.79 m/s. The conventions used to compute angles are shown in the insets at the top. The top and bottom of the vertical histograms at the right of each joint plot show the mean (±SD) maximum and minimum joint angle positions, respectively, at each treadmill speed for six Rhesus.

Figure 4. Control of limb endpoint trajectories A. Superimposed endpoint trajectories of the HL (tip of the fifth toe) and FL (tip of the fifth finger) during the swing phase for 6 consecutive step cycles at each treadmill speed for Rhesus #4. The plots are anisotropic with the vertical scale being expanded relative to the horizontal scale. The top diagrams display the typical schematic decomposition of HL and FL movements during the swing of gait. Endpoint trajectory (continuous line) is also shown, the scale being isotropic B. Relationship (in logarithmic scales) between the angular velocity and curvature of HL (left) and FL (right) endpoints in the same animal. All samples corresponding to the swing phases at 0.89 m/s were pooled in these examples. All linear regressions were significant (p < 0.001). Linear regression analysis (black line) was performed to estimate the exponent β from the slope. The grey line indicates the two-third slope, i.e. β = 0.66. C. Mean exponent (β) values (±SD) of the angular velocity–curvature relationship for the HL and FL at each treadmill speed for six Rhesus are shown. The horizontal dotted line designates the two-third value. Asterisks indicate a lower value in the FL than the HL at p < 0.05.

Figure 5. Characteristics of segment angular oscillations with increasing speed A. The mean waveforms (for 6 Rhesus) of HL and FL elevation angles (with respect to the direction of gravity) computed from all step cycles at each treadmill speed are plotted vs. the normalized cycle duration. The circles identify the point of maximal backward oscillation for each segment and point out the phase-relationships between the different limb segments. B. Mean values (for 6 Rhesus) of the shift in timing of HL and FL segment oscillations with increasing treadmill speed are shown. These values correspond to the phase lag required to provide the best fit between the angular waveforms at 0.89, 1.34, and 1.79 m/s relative to those at 0.45 m/s. Changes in the relative duration of the stance phase with increasing speed are represented by the filled squares. Increase in speed corresponds to a decrease in stance duration (Fig. 2), which is reported as a percent of change in cycle duration. The maximal positive value of r obtained from each cross-correlation function was averaged across speeds and animals for each segment and the values (±SD) are shown to the right of the plot (all linear regressions were significant, p < 0.001).

Figure 6. Pattern of inter-segmental coordination in Rhesus gait A. The 3-D gait loops obtained by plotting thigh, shank, and foot elevation angles for the HL or arm, forearm, and hand elevation angles for the FL vs. each other at the different treadmill speeds are shown for Rhesus #4. The mean value for each angular coordinate has been subtracted. The time of stance (St) and swing (Sw) onset is indicated by the black circles. The arrows indicate the direction along which the 3-D gait loop evolves during stance and swing. The grids identify the best-fitting planes whose intersections with the cubic wire frame provide information about its spatial orientation. The p.29

Kinematic and EMG patterns during rhesus monkey locomotion planarity of the 3-D gait loops was computed as the percent variance accounted for by PC1 plus PC2. The mean values (± SD) (for 6 Rhesus) of the planarity index are reported above each 3-D plot. The vertical lines connecting the data points to the grid indicate the distance between the 3-D gait loops and the best-fitting plane. B. Mean values (SD) (for 6 Rhesus) of the variance accounted for by each principal component (PC) for HL or FL segment oscillations (all treadmill speeds pooled) are shown. Asterisks indicate significant differences between the HL and FL values. C. Mean (SD) values (for 6 Rhesus) of the variance accounted for by each PC (PC5 to PC8 are summed) when PC analysis was applied to HL and FL segment oscillations simultaneously (all treadmill speeds pooled). The cumulative variance accounted for by each PC is shown above each histogram bar. D. The variance accounted for by PC1 plus PC2 (PC applied on HL and FL segments simultaneously) is plotted vs. the mean body velocity. All the extracted gait cycles from the 6 Rhesus are reported. The linear regression was significant (p < 0.05).

Figure 7. EMG activity of HL and FL muscles Mean (rectified) EMG activity of HL and FL muscles plotted vs. the normalized gait cycle duration at each treadmill speed (as shown for the EDL). HL EMG traces are the ensemble average of all gait cycles performed by Rhesus #3 (knee and ankle joints) and #1 (MT joint). HL waveforms are aligned at the onset of the right Sol EMG burst. Mean EMG activities for the right FL muscles are illustrated for both Rhesus #5 and #6. FL waveforms are aligned at paw contact of the right FL. The horizontal bars at the bottom of the FL traces indicate the mean duration of stance and swing phase for each Rhesus.

Figure 8. Temporal and spatial characteristics of EMG bursts (Typical relationships between EMG burst duration (top) and integral (bottom) for the HL and FL muscles vs. the cycle duration are shown. HL-related data were obtained from Rhesus (#1 and #3). FL-related relationships were obtained from Rhesus #5. In addition, diamond data points illustrate the temporal modulation of the EDC and swing-related FDP EMG bursts with respect to cycle duration for Rhesus #6.

Figure 9. Temporal coordination between muscle activity and oscillations of HL and FL segments The timing of FL (top) and HL (bottom) segment oscillations is plotted vs. the temporal features related to the end of stance of all right FL muscles for all step cycles from Rhesus #5. This corresponds to the timing of burst termination for all muscles but the Bic for which burst onset was related to stance end (Fig. 7). Timing of segment oscillations were computed as the phase φ of the first order Fourier series component of the angle, and expressed as a percent of the normalized gait cycle duration. The minimum value recorded in FL segments was set at zero, and all HL and FL values were modified accordingly. The relative timing between the oscillation of HL and FL segments is preserved. Similar relationships were detected in Rhesus #6 (data not shown). All the linear regression were significant (p < 0.05)

Figure 10. Coordination between the EMG activity of HL and FL muscles The EMG burst integral (non-time normalized) for each HL muscle is computed for each gait cycle and plotted vs. the right Sol and TA EMG burst integral during the same gait cycle for the HL (top), and plotted vs. the right Tri and Bic EMG burst integral for the FL (bottom). These relationships were consistent among the Rhesus in the HL, and representative plots are displayed. These relationships, however, were less consistent in the FL and data are shown for both Rhesus #5 and #6. p.30

Courtine et al. Mean (± SD, if n > 1) correlation coefficient (r) values are reported. The EMG burst integral of each muscle from each Rhesus was normalized to its mean value computed during walking at the slowest treadmill speed, i.e., at 0.45 m/s. Left and right muscles are identified by open and filled symbols, respectively. FHL and EDL, FDS and EDC were recorded from the right muscles only, and are represented by open and filled circles, respectively.

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Kinematic and EMG patterns during rhesus monkey locomotion Table 1. Summary of muscles studied and their primary actions.

HINDLIMB

Abb.

Main action

Vastus lateralis

VL

Medial Gastrocnemius Soleus Tibialis Anterior

MG Plantarflexion / Knee flexion Sol Plantarflexion TA Dorsiflexion / inversion

Flexor digitorum longus Extensor digitorum longus Flexor hallucis longus

FDL 2nd - 5th toes flexion / Weak plantarflexion EDL Toe extension / Weak dorsiflexion FHL Big toe flexion / Plantarflexion

FORELIMB Triceps brachii medial head Biceps long head Extensor digitorum communis Flexor digitorum profundus Flexor digitorum superficialis Flexor pollicis brevis profundus

Knee extension

Abb. Tri Bic EDC FDP FDS FPB

Monkey # 3 (LR)

Main action

# 1 # 2 # 3 (all LR) # 1 (R) # 2 (R) # 3 (LR) # 1 # 2 # 3 (all LR) # 1 # 2 (R) # 1 # 2 (R) # 1 # 2 (R)

Monkey

Forearm extension Forearm flexion and supination / Arm flexion

# 5 # 6 (R) # 5 # 6 (R)

Medial digit extension / Wrist extension Distal PH flexion / Assist proximal PH and wrist flexion Middle-proximal PH digit flexion / Wrist flexion Proximal PH thumb flexion

# 5 # 6 (R) # 5 # 6 (LR) # 5 # 6 (R) # 5 # 6 (LR)

L, left; R, right; Abb., abbreviations; PH, phalange

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% of time

1.34 m/s 0.89 m/s

0.45 m/s

Right Left

4 3 2 1

50

0 1.79 m/s

Limb in contact

Courtine et al.

100

HL FL

1s

FIG. 1

p.33

60 40

#5

20 0 -20 0.5

1

1.5

HL : r = - 0.97 ± 0.01 FL : r = - 0.97 ± 0.01 1.2

2

B HL FL

0.8 0.4 0

0.5

1

1.5

0.8 0.4 0

0.5

1

1.5

Mean body velocity (m/s)

+ distance

60

2

D

*

*

1.34

1.79

40 20 0 -20

* 0.45

*

0.89

*

75

*

E

*

HL

50

FL

25 0

0.45

0.89

1.34

1.79

Treadmill speed (m/s)

C

HL : r = 0.98 ± 0.01 FL : r = 0.97 ± 0.01

1.2

80

2 0.8 Duration (s)

Stride length (m)

LHL RFL LFL

Inter-limb distance at contact (cm)

A

80

Stance duration (% of cycle duration)

Cycle duration (s)

RFL Contact time (%)

Kinematic and EMG patterns during rhesus monkey locomotion

r = 0.99 ± 0.01 HL FL

0.6 0.4

ce Stan

F

Swing

0.2

r = 0.69 ± 0.18

0 0.5

0.7

0.9

1.1

Cycle duration (s)

FIG. 2 p.34

Courtine et al.

Hindlimb Stance

Forelimb

Swing

Stance

hip knee

30

Elbow

150 100 50

200 150 240

210

MCP

Flex

260 160

Ventro Flex

60

170 100 280 200

Ventro Flex

Wrist

110 Flex

Ankle

30

100

160

MT

70

Flex

70

120 Retr

Flex

110

Shoulder

wrist MCP

Flex

Knee

Hip (deg)

ankle MT

Swing

shoulder elbow

0

25 50 75 100 Cycle duration (%)

Treadmill speed (m/s)

120 0.45 0.89 1.34 1.79

0

25 50 75 100 Cycle duration (%)

FIG. 3 p.35

Kinematic and EMG patterns during rhesus monkey locomotion

Forelimb A

Hindlimb

0.45 0.89

4 cm

1.34 1.79

Angular velocity (rad/s)

2 cm 103 β = 0.71 r = 0.99

10 10-1

β

10-3 0.8

=

6 0.6

β=

6 0.6

1 102 104 Curvature (m-1)

*

*

*

C

* 0.66

0.6 β

B

β = 0.68 r = 0.99

HL

0.4

FL

0.2 0 0.45 0.89 1.34 1.79 Treadmill speed (m/s)

FIG. 4 p.36

Courtine et al.

Hindlimb

100 50 0 -50 -100

Thigh Shank Foot Toe

0.89 m/s

10

Stance

1

1.34 m/s

Arm Forearm Hand Finger

1.79 m/s

0 18 12

0.8

5

6

0

0 0.45 0.89 1.34 1.79 Treadmill speed (m/s)

25 50 75 100 Cycle duration (%) Stance

r value

15

25 50 75 100 Cycle duration (%) r value

Phase lag (% of cycle)

0.45 m/s

Backward

0

B

Forelimb

90 45 0 -30 -60

Forward

Elevation angle (deg)

A

1

0.8

0.45 0.89 1.34 1.79 Treadmill speed (m/s)

FIG. 5 p.37

Hand

swing

-80

40 Sh ) 5 an eg 0 d 0 ( k -4 igh Th Sw

98.0 ± 0.56

97.9 ± 0.52

98.1 ± 0.57

7 Fo 0

0.89 m/s

1.34 m/s

1.79 m/s

Sw

0

4 re eg) ar -70 0 d ( m -4 rm A 98.5 ± 0.34

98.0 ± 0.52

97.9 ± 0.4

Variance accounted for (%)

St

80

stanc e

Foot

St

-60 60

98.7 ± 0.50

0.45 m/s

swing

60

A

stance

97.8 ± 0.66

Forelimb

Variance accounted for (%)

Hindlimb

Variance accounted for (%)

Kinematic and EMG patterns during rhesus monkey locomotion

100

50

0

B

* HL FL

*

PC1 PC2 PC3 PC4

C

100 60%

50

0 100

91% 97% 99%100%

PC1 PC2 PC3 PC4 PC5-8 r = 0.80

D

90 80

PC1 plus PC2

70

0 0.5 1 1.5 2 Mean body velocity (m/s)

FIG. 6 p.38

Courtine et al.

Hindlimb Left

# 5 (right)

# 6(right)

EDC

DIGITS FDL

FPB

FHL

DIGITS

TA

FDP

FDS

ANKLE Sol

Bic

MG

ELBOW

Tri

KNEE VL

Right

Forelimb

EDL

0.45 m/s 0.89 m/s 1.34 m/s 1.79 m/s Solonset

0 50 100 Cycle duration (%)

STANCE

SWING

Paw contact

0 50 100 Cycle duration (%)

FIG. 7 p.39

Kinematic and EMG patterns during rhesus monkey locomotion

Burst duration (s)

Knee VL

0.4 0.6 0.8 1 Cycle duration (s)

Knee VL

Ankle MG SL TA

TA

AMPLITUDE Ankle

MG 5 SL 4 3 2 1 0

TIMING

Hindlimb

3

1

2

1

0 0.4 0.6 0.8 1 Cycle duration (s)

0

2

3

0.8 0.6 0.4 0.2 0

Normalized Burst integral

Digits FHL EDL

FDL

Digits FHL 8 FDL 6 4 2 0 EDL

4

#5

EDC FPB FDS

FPB

Digits

FDS

0

2

4

AMPLITUDE Digits (#5) 6

FDP

TIMING

Forelimb Elbow Tri Bic

Elbow Tri Bic

2 0

#6

EDC

EDC

FDP

FDP

FIG. 8

p.40

Courtine et al.

Forelimb Timing of segment oscillation (% of cycle duration)

FDS

FDP

EDC

FPB

Bic

Tri

50 40 30 20 10 0 Arm

80 30

80 35 Forearm

Hand

Finger

30

FDS

FDP

85 20

65 20

Hindlimb EDC

80 25

65

Thigh

Shank

Foot

Toe

FPB

Bic

Tri

80 60 40 20 30

80 30

80 35 85 20 65 20 Temporal burst feature (% of cycle duration)

80 25

65

FIG. 9 p.41

Kinematic and EMG patterns during rhesus monkey locomotion

burst integral

Hindlimb TA

MG

r = - 0.74 ± 0.09 3 r = - 0.58 ± 0.1

6

VL

r = - 0.78 ± 0.09 2

2

4

r = - 0.81 ± 0.09

0

0 0.4

0.8

1.2 0.4

0.8

r = 0.88

0

1.2

0.4

0.8

FDL r = - 0.52 ± 0.02

2

1

1

1

2

FHL-EDL

r = - 0.62 ± 0.04 3

2

r = 0.93

1 r = - 0.67 ± 0.1

0

1.2 0.4

0.8

0

1.2 0.4

0.8

1.2

burst integral

Right Sol burst integral MG

6 4

TA

3

r = 0.65 ± 0.1

2

2

1 0.4

1.9

0 3.4 0.4

3

0 3.4 0.4

r = - 0.83

1.4

FDL r = - 0.71 ± 0.02

2

1

r = 0.81 ± 0.04

1.9

FHL-EDL

r = 0.53 ± 0.04

2

r = - 0.88

1

r = 0.46 ± 0.2

0

VL

2

1

0 2.4 0.4

r = 0.52 ± 0.23

1.9

0 3.4 0.4

1.9

3.4

Right TA burst integral

0

burst integral

1 Bic

10

r = -0.17

0 1.5 0.5

1

Tri

2

0

4 Tri

3 2

4

8

FDP

10

r = -0.17

0

4

8

4

8

1

#5 2

FPB r = -0.27 r = -0.64

1 r = 0.19

0

4

0 8

0

FDS-EDC

4

8

FPB

r = -0.05 3

r = -0.39 r = - 0.04

2 1 r = - 0.37

0 0

0

2

1

0

0

0 2

r = 0.04

2

r = -0.1 r = -0.06

5

1

1

FDS-EDC

0 0

8

0

1

0

0

1 r = 0.83

2

1

1.5

r = 0.81 r = 0.78

2

Right Tri burst integral r = -0.35 r = 0.01

2

1

2

FDP

3

r = 0.13

1

1 FPB

3

r = 0.21

0 0

2

0 1.5 0.5

1

0

0

1 FDS-EDC

2

r = 0.86 r = -0.06

5

5

r = -0.03

1.5 0.5

FDP

10

r = -0.12 r = -0.02

1

0 1

Left

FPB

2

r = 0.18

1

1

0

burst integral

2

r = -0.15 r = 0.07

2

4

0.5

burst integral

3

r = 0.13

FDS-EDC

#6

8

FDP

#5

Bic

Right

#6

burst integral

Forelimb

0

4

0 8

0

4

8

Right Bic burst integral

FIG. 10 p.42