or spinal cord injury

Jean-Pierre Gossard, Réjean Dubuc and Arlette Kolta (Eds.) Progress in Brain Research, Vol. 188 ISSN: 0079-6123 Copyright Ó 2011 Elsevier B.V. All rights reserved.

CHAPTER 7

Interindividual variability and its implications for locomotor adaptation following peripheral nerve and/or spinal cord injury Alain Frigon* Département de physiologie et biophysique, Université de Sherbrooke, Sherbrooke, Quebec, Canada

Abstract: Following injury to the nervous system, there is a range of possible functional outcomes that can only be partly explained by the extent of injury. Moreover, treatments effective in certain individuals might not work in others. Why such variability from one individual to another, in terms of functional outcomes and responsiveness to a given treatment following a similar injury? The answer to that question is not simple, and to begin to answer we must first consider that individuals of the same species can be quite variable in terms of neuronal circuit parameters involved in performing a given task. Interindividual variability can be subtle but the term “variability” in this chapter will be used to denote marked differences between individuals at the systems level (e.g., spinal reflexes, bursts of muscle activity, kinematics) during the same motor behavior, with an emphasis on locomotion. Injury to any level of the nervous system, in turn, can further compound this variability by altering spared neuronal connections. The aim of the present chapter is to (1) review studies that have investigated interindividual variability, (2) review studies that have described variable adaptive mechanisms following spinal and/or peripheral nerve lesions during locomotion, and (3) discuss the implications of intersubject variability for locomotor adaptation. Keywords: variability; locomotion; spinal cord injury; peripheral nerve injury; reflex; adaptation.

called a central pattern generator (CPG) (reviewed in Delcomyn, 1980; Grillner, 1981; Lundberg, 1981; McCrea and Rybak, 2008; Rossignol et al., 2006). A key feature of the spinal locomotor CPG is its enormous flexibility in adapting to changing conditions in the short and long terms (Pearson, 2000). One consequence of this flexibility is that different solutions can

Introduction In vertebrates, the basic pattern of walking is produced by neuronal circuitry within the spinal cord

*Corresponding author. Tel.: þ1-312-503-1323 E-mail: [email protected] DOI: 10.1016/B978-0-444-53825-3.00012-7

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achieve the same goal. As a result, interindividual variability can arise in neuronal connections. Before delving into the issue of interindividual variability, it is important to discuss how the locomotor program is configured in the first place via a predetermined genetic program and through interactions with the environment during development. It is possible that the spinal network acquires the ability to produce locomotion through developmental processes, as the animal learns to walk in the early stages of life. However, several animals walk and run almost immediately after birth and studies have shown that completely transecting the spinal cord soon after birth does not prevent the expression of hindlimb locomotion (i.e., spinal locomotion), when the legs are placed on a motorized treadmill (Rossignol, 1996). In fact, hindlimb locomotion is often better in animals spinalized soon after birth compared to the same lesion performed in adults. Well-developed bipedal walking can also be observed in human infants within the first year of life, well before learning to walk voluntarily, when the legs are placed on a moving treadmill (reviewed in Yang et al., 2004). In human infants, the corticospinal tract is extremely immature at birth and locomotor-like movements are largely independent of volitional descending control (Forssberg, 1985; Yang et al., 2004). However, descending pathways from the brainstem are functioning and capable of configuring the spinal network to produce locomotion if appropriate sensory cues are provided. Therefore, it is likely that the locomotor program has a very strong genetically predetermined component. Additional support for a genetically predetermined program comes from tendon transfers in adult cats and kittens, in which crossed or transferred muscles largely retain their normal activation pattern (Forssberg and Svartengren, 1983; Loeb, 1999; O'Donovan et al., 1985). For example, transposing the tendons of the lateral (LG) and medial gastrocnemii (MG), both ankle extensors, on to the cut tendon of the tibialis

anterior (TA), an ankle flexor, in adult cats and kittens, did not disrupt the normal activation of gastrocnemii muscles during the stance phase of locomotion, even though they now functioned as anatomical ankle flexors, which resulted in deficits (Forssberg and Svartengren, 1983; Loeb, 1999). However, although transferred or crossed muscles retained their normal locomotor activity pattern, cutaneous reflex responses could display considerable left/right asymmetry, suggesting some plasticity in the interactions between the spinal CPG and reflex pathways (Loeb, 1999). Evidently, some of these changes could be mediated by altered interactions between sensory feedback and supraspinal and spinal structures, as the animal learns to cope with an altered anatomical organization. Therefore, although it is clear that the basic locomotor program is in large part genetically determined, some components of the spinal CPG, and in the interactions between spinal, peripheral, and supraspinal structures, are malleable. During development and through repeated practice (i.e., trial and error), some connections within the central nervous system become stronger, or weaker, than others (recently reviewed in Butz et al., 2009; Holtmaat and Svoboda, 2009). Neuronal connections within the spinal locomotor CPG are most likely not exempt from this phenomenon. For example, in spinalized kittens (Forssberg et al., 1980) and human infants, the walking pattern of the legs adapts to different treadmill speeds, to independent speeds for each leg on a split-belt treadmill, and to load-related signals (Musselman and Yang, 2007; Yang et al., 1998, 2005), thus showing that sensory feedback interacts with the spinal locomotor CPG in the earliest stages of life. The consequences of the interactions between sensory feedback and the spinal CPG during development and over a lifetime is largely unknown but there is evidence that some intrinsic properties of the locomotor circuitry are permanently altered. For instance, we recently showed that the stance and swing phases of the cycle period (i.e., the time between

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successive bursts) were asymmetrically controlled during fictive locomotion in adult cats in which supraspinal signals and phasic sensory feedback were abolished, as they were curarized, decerebrated, and spinalized (Frigon and Gossard, 2009). Specifically, cycle period varied more with the duration of the extension phase (i.e., extensor-dominated), while the flexion phase remained relatively invariant, similar to what is observed during normal walking where cycle period changes as a function of the stance phase with the swing phase remaining invariant (Grillner et al., 1979; Halbertsma, 1983). However, in neonatal rats during fictive locomotion (Juvin et al., 2007), and human infants during air-stepping (Musselman and Yang, 2007), extensor-dominant asymmetry in regulating cycle period is only observed if phasic sensory inputs consistent with locomotion are provided. It might be that the locomotor spinal CPG, over several years, develops an intrinsic extensor-dominant asymmetry due to repetitive interactions with stance-related sensory feedback (Frigon and Gossard, 2009). The pattern of walking in human infants also shows marked differences with the adult pattern in terms of foot placement and intralimb coordination (Forssberg, 1985; Yang et al., 2004). Therefore, as the interactions between spinal, supraspinal, and peripheral systems mature and strengthen over several years, it is likely that “hard-wired” changes occur within some components of the locomotor circuitry. Consequently, because each animal is confronted with a different set of circumstances over a lifetime, interindividual differences within the locomotor circuitry can arise. Changes in reflex pathways during development are well demonstrated in the nociceptive withdrawal reflex of the rat (Levinsson et al., 2002; Schouenborg, 2002, 2008). At birth, withdrawal reflexes are maladaptive, often causing movements toward the noxious stimuli. In the first 3 weeks after birth, the reflex circuitry is shaped by experience-dependent mechanisms, whereby erroneous connections are eliminated or reduced and appropriate

connections are strengthened, becoming proportional to withdrawal efficiency. Changes in reflex pathways also occur in the adult system following lesions or using learning paradigms. In the past 20 years, Wolpaw, Chen, and colleagues have clearly established that operant conditioning of the soleus H-reflex in adult rats induces persistent changes in the spinal circuitry and in how these pathways interact with supraspinal signals (reviewed in Wolpaw, 2007; Wolpaw and Tennissen, 2001). In invertebrate motor systems, recent experimental and modeling studies have shown considerable variability in the production of motor patterns between animals, in a relatively simple motor system, the pyloric rhythm of the lobster (Bucher et al., 2005; Marder and Goaillard, 2006; Prinz et al., 2004). What emerges from these studies is that widely disparate circuit parameters can produce similar network activity. Moreover, eliminating one parameter (e.g., a specific conductance) often does not alter network activity because of different combinations of compensatory mechanisms. During development, as the animal grows in size, parameters of the network change but network performance remains essentially the same (Bucher et al., 2005; Marder and Goaillard, 2006; Schulz et al., 2006). As a consequence, because there are many ways to generate a desired outcome, interindividual variability can arise in how the neuronal network is configured. Therefore, although the act of walking is similar between individuals of the same species, including humans, substantial differences can occur in the locomotor circuitry due to activitydependent mechanisms. In fact, humans might be the most “variable” animal of all, because in humans, physical activity is influenced by myriad factors, such as gender, cultural background, socioeconomic status, genetic predispositions (e.g., height, weight), age, climate, geography, etc. (reviewed in Caperchione et al., 2009; Kumanyika, 2008; Seefeldt et al., 2002). As such, it is not surprising that neuronal connections at the systems level can differ so dramatically from

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Interindividual variability during locomotion Gait transitions and walking speed Walking, whether tested on a treadmill or on the ground, can vary from one animal to another. For instance, during treadmill locomotion at matched speeds some cats will walk, whereas others will trot or gallop (Vilensky and Patrick, 1984; Vilensky et al., 1990). The transition speed from one gait pattern to another, such as walking, trotting, and galloping varies between animals. Additionally, some animals will even trot or gallop at the same speed on different days (Vilensky and Patrick, 1984). The coordination between the four limbs can also display intra- and interanimal variability during walking in the cat (English, 1979). For example, some cats prefer a trot-like coordination (i.e., homolateral limbs are out-ofphase), whereas some cats adopt a pacing pattern (i.e., homolateral limbs are in-phase) during overground or treadmill walking (English, 1979; Stuart et al., 1973; Vilensky and Patrick, 1984; Wetzel et al., 1975). Although the four limbs are loosely coupled, individual cats will primarily adopt one type of strategy (English, 1979). Walking speed is also interesting in the context of interindividual variability. Individuals have a preferred walking speed and any modification requires conscious voluntary control. For example, we alter our speed when walking with someone else, or increase our speed when in a hurry, both of which requires volitional control. Cats are no different. When walking on a treadmill they usually walk most consistently at a specific speed, which does not necessarily depend on the size of the animal. Some cats will prefer a slow treadmill speed (e.g., 0.3 m/s), whereas other cats will walk more consistently at higher speeds (e.g., 0.6 m/s). The majority of cats prefer a treadmill

speed of 0.4–0.5 m/s. During fictive locomotion the rhythm also displays a range of “speeds,” which can be inferred by measuring cycle period (i.e., the time between successive bursts of activity in a given nerve). Figure 1 shows cycle periods from 34 episodes of spontaneous fictive locomotion recorded in 27 adult cats that had undergone the same experimental procedure (Frigon and Gossard, 2009), and from 21 episodes of treadmill locomotion in 21 intact cats walking at their “preferred speed” (i.e., 0.4 or 0.5 m/s). During fictive locomotion, the average locomotor cycle period was 1065  466 ms, which approximately corresponds to a treadmill speed of 0.3–0.35 m/s during intact

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Treadmill locomotion Spontaneous fictive locomotion Fig. 1. Cycle periods during spontaneous fictive locomotion in adult decerebrate cats and during treadmill locomotion in intact cats. Cycle period was measured from successive burst onsets in selected extensor nerves during spontaneous fictive locomotion and from successive left foot contacts during treadmill locomotion. The data set consists of 34 episodes of fictive locomotion from 27 adult decerebrate cats (Frigon and Gossard, 2009), and from 21 episodes of treadmill locomotion in 21 intact cats. Each data point is the average of 15 cycles.

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treadmill locomotion in the cat (Halbertsma, 1983). Of the 34 episodes only 3 fell outside one standard deviation of the mean (2 above and 1 below). Thus, if we omit the few outliers, the locomotor CPG, without phasic sensory feedback and volitional control, operates within a narrow range and the cycle period from these 31 episodes now becomes 983  241 ms. This is remarkable considering that, in such preparations, there are some interindividual differences in supraspinal drive, overall excitability, static limb posture, etc. In intact cats walking on a treadmill, the cycle period was 941  98 ms, which is strikingly similar to the average cycle period during spontaneous fictive locomotion. Moreover, even though cycle period is constrained by the speed of the treadmill there is still some interanimal variability. The small range of speeds during spontaneous fictive locomotion, inferred by measuring cycle period, could partly reflect some interindividual variability in the intrinsic organization of the CPG, which might play a role in determining the animal's preferred walking speed. To change this “default” speed would require voluntary control or peripheral sensory feedback, as observed during treadmill locomotion, where the imposed speed narrows the range of cycle periods. Although interindividual variability appears to be an inescapable component of motor systems, few studies have specifically assessed it, directly or indirectly, in vertebrate preparations. Improved chronic recording and stimulating procedures have made quantifiable measures possible in animal studies for prolonged periods, while reducing variability related to methodological sources (Loeb, 1993). That is not to say that interindividual variability is not partly related to experimental procedures; only that methodological issues cannot account fully for observed interindividual variability. What emerges from such studies is that activation patterns and reflexes during locomotion, or other natural behaviors, are very consistent from day to day in the same animal but can be quite variable from one animal to another.

Recruitment patterns and spinal reflexes Loeb (1993) investigated interanimal variability in the activation profile of certain muscles and in electrically evoked cutaneous reflexes during treadmill locomotion in the cat and found that most animals showed idiosyncratic patterns of activation and reflex responses in at least some hindlimb muscles. For instance, flexor digitorum longus (FDL, ankle adductor/digit flexor) was consistently activated at the time of foot lift but in some animals it could also be recruited during the stance phase (Loeb, 1993). Its close synergist, flexor hallucis longus (FHL, ankle extensor/digit flexor), which shares origins and insertions with FDL (Abraham and Loeb, 1985; Fleshman et al., 1984; O'Donovan et al., 1982; Schmidt et al., 1988), was consistently recruited during the stance phase, along with other extensors, in all cats. Interestingly, cutaneous reflex responses evoked by stimulating the superficial peroneal (SP) nerve differed between animals (i.e., showed idiosyncratic responses) in FHL but not FDL. Thus in the muscle that was consistently recruited during stance in all animals (i.e., FHL), cutaneous reflex responses differed from one animal to the other, whereas in a muscle that showed interanimal variability in its recruitment pattern (i.e., FDL), reflex responses were very consistent. The peroneus longus (ankle abductor/flexor) was another muscle that showed considerable variability in its recruitment pattern but consistent excitatory reflexes during swing and late stance. It could be coactive with TA during swing, active during both stance and swing, and in other cats it could be relatively silent during the entire locomotor cycle (Loeb, 1993). Interindividual variability in cutaneous reflexes is also present in humans at rest and during walking. During human walking, in some subjects, instead of a middle latency excitatory response, there is an inhibitory response, particularly with stimulation of cutaneous afferents that supply the plantar surface of the foot (Haridas et al., 2005; Zehr et al., 1997). Therefore, variability in recruitment

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patterns and/or cutaneous reflexes is observed in multiple muscles during walking in the cat and humans. Figure 2 shows reflex responses in the left semitendinosus (St) muscle, evoked by stimulating the tibial (Tib) nerve with a cuff electrode just distal to the medial malleolus, in two intact cats during treadmill locomotion (Frigon and Rossignol, 2008a). Stimulating the Tib nerve during locomotion usually evokes short- (P1) and longer latency (P2) excitatory responses, which are modulated according to the phase of the cycle (Abraham et al., 1985; Drew and Rossignol, 1985; Duysens and Stein, 1978; Loeb, 1993). In the example depicted in Fig. 2, P1 and P2 responses were modulated throughout the locomotor cycle in cat 1, with large responses during the swing phase and the stance-to-swing transition (Fig. 2a). In cat 2, however, there were virtually no P1 responses throughout the locomotor cycle but there were large P2 responses during swing, the swing-tostance transition, early stance, and the stance-toswing transition. The rectified electromyography (EMG) of the left St pooled from nonstimulated cycles is shown on the far right for both cats, and clearly, differences in reflex responses between cats cannot be explained by large differences in the timing of bursting activity of the muscle. As we will see later, this interindividual variability in reflex response can be altered following a complete spinal transection, which abolishes all supraspinal inputs to the spinal locomotor circuitry.

Muscle synergies It is thought that the central nervous system simplifies the task of controlling movement by combining multiple muscles into synergies or modules (Bizzi et al., 2000, 2008; Grillner, 1981; Grillner and Wallen, 1985; Ivanenko et al., 2007; Jordan, 1991; Krouchev et al., 2006; Loeb et al., 2000; Schouenborg, 2003; Stein and Smith,

1997). Quantifying muscle synergies during normal motor behaviors offers another means of testing recruitment patterns/activation profiles. There is good evidence that most, but not all, muscle synergies are organized centrally and that sensory feedback from the periphery and/or from supraspinal signals adjusts the temporal pattern of synergy activation (Cheung et al., 2005). Interestingly, the number and type of muscle synergies involved in producing a given movement can differ from one animal to another (Cheung et al., 2005; Krouchev et al., 2006). For instance, in the bullfrog, it was shown that hindlimb swimming was produced by four different muscle synergies in some frogs, whereas in other frogs up to six different synergies were used (Cheung et al., 2005). Although many of the synergies were shared between animals, some frogs displayed unique synergies. How can the system give rise to different muscle synergies between animals? One obvious answer is that the interindividual variability in sensory feedback from the periphery and/or supraspinal signals shape the activation of different muscle synergies. Cheung et al. (2005) performed a unilateral deafferentation of dorsal roots 7–9 in bullfrogs to abolish some sensory feedback from the hindlimbs, and although most synergies were preserved, the total number of synergies changed slightly in three of four frogs. The interindividual difference in the number of synergies also persisted. Moreover, deafferentation appeared to compound the interindividual variability in the temporal pattern of synergy activation with some frogs showing an increase, a decrease, or no change in specific synergies. Therefore, it is likely that the emergence of synergies within the spinal cord is a complex process that involves interactions between genetically determined motor programs and experiencedependent processes. The use of slightly different strategies from one animal to another to accomplish the same movement is the resultant of these complex interactions.

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Fig. 2. Reflex responses of the left St evoked by stimulating the tibial (Tib) nerve at 1.5 times the motor threshold in two intact cats, (a, Cat 1; b, Cat 2) during treadmill locomotion. Values for each horizontal trace in a single graph are at the same scale in mV. Each horizontal trace is the average of approximately 10 cycles with stimulation superimposed on the background level of EMG derived from control cycles. The first horizontal trace in each figure is phase 0.05 (i.e., from 0.0 to 0.10) of the locomotor cycle synchronized to left St burst onset followed by 0.10, 0.15, etc. The rectified activity of the left St from control cycles is shown on the far right at 90 . The dashed vertical line indicates the time of the stimulation, whereas solid vertical lines indicate the time windows used to delineate P1 (black area, 10–25 ms) and P2 (gray area, 25–55 ms) responses.

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Factors inducing interindividual variability What gives rise to this variability in reflexes and activation patterns during locomotion between animals? As pointed out before, experience and genetic factors (e.g., genetic polymorphisms) probably play a large role in inducing interindividual variability but discussing those factors is outside the scope of this chapter. There are, however, more tangible factors that can cause disparities in the locomotor circuitry between individuals. For example, as discussed by Loeb (1993), the presence of consistencies in recruitment patterns between animals is not surprising because the musculoskeletal system imposes mechanical constraints on the control system, which are primarily genetically determined. However, there is anatomical variability between animals of the same species and differences in size, mass, and structure of the mechanical system impose different challenges on the neural control of locomotion. Interestingly, despite a wide range of sizes within a given species there is little relation between body size and specific gait parameters. For instance, in genetically related vervet monkeys, body mass and segment length did not correlate with gait transition speed (i.e., from trotting to galloping) because of considerable interanimal variability (Vilensky and Gankiewicz, 1990; Vilensky et al., 1988, 1990). At a particular body mass, there was a substantial range of transition speeds between animals. Moreover, within a group, changes in gait parameters did not change consistently with age between animals, although as the animal aged and grew in size it tended to maintain a similar gait pattern at match speeds (Vilensky and Gankiewicz, 1990). Therefore, each animal appears to use a unique strategy to produce the propulsive forces necessary for locomotion across speeds and body size. Vilensky et al. (1990) postulated that the underlying factors of this interanimal variability could be physiological, psychological, and/or morphological. What is clear is that the neural locomotor program is extremely flexible.

Variable adaptive mechanisms following peripheral nerve lesions Loeb (1993) made an important point: if neural circuits for a given behavior can differ between animals, this raises important methodological problems for studies that pool data among animals. One way to circumvent this methodological problem is to evaluate changes following a given treatment on a case-by-case basis to determine consistencies between individuals, while concomitantly highlighting variable or unique adaptive strategies. In recent studies, we used this approach to quantify changes in the locomotor pattern and cutaneous reflexes following lesions to the spinal cord and/or peripheral nerves (Frigon and Rossignol, 2007, 2008a,b, 2009; Frigon et al., 2009). This section discusses changes following peripheral nerve lesions in intact (i.e., with an intact spinal cord) and chronic spinal cats while the next section discusses changes following spinal cord lesions. What is clear from these studies is that a given lesion can produce different outcomes and compensatory mechanisms from one animal to another. To evaluate locomotor and reflex adaptation following a peripheral denervation, we performed a lesion of the left lateral gastrocnemius-soleus (LGS) nerve in otherwise intact cats (Frigon and Rossignol, 2007), or in cats that had undergone a complete spinal transection (i.e., spinalization) several weeks before the denervation (Frigon and Rossignol, 2008b). Cats were implanted with recording and stimulating electrodes and hindlimb kinematics, patterns of muscle activation, and cutaneous reflexes evoked by stimulating the Tib nerve, were quantified during treadmill locomotion in the same animal before and after denervating the LGS. Following denervation of the LGS, the most consistent deficit was an increase in ankle flexion, or yield, at the onset of the stance phase in intact (Frigon and Rossignol, 2007; Pearson et al., 1999) and chronic spinal (Bouyer et al., 2001; Frigon and Rossignol, 2008b) cats. However, the magnitude of this

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deficit and the time course of recovery could substantially differ between animals. Figure 3 shows changes in joint excursions in two intact cats following a lesion of the left LGS nerve (Frigon and Rossignol, 2007). For instance, in cat 3, ankle yield increased considerably during early stance following LGS denervation (Fig. 3a), whereas in cat 4 there was virtually no change in ankle yield (Fig. 3b). At the hip and metatarsophalangeal (MTP) joints, changes were consistent between cats 3 and 4, but at the knee there was a marked difference. The left knee joint was more extended throughout the locomotor cycle in cat 3 (Fig. 3a), while in cat 4 it was more flexed (Fig. 3b) following denervation. There were also similarities and marked differences in muscle activation profiles after denervation (Figs. 3c and d). For example, activity increased in the left MG, left VL, and right VL, but remained unaltered in the left Srt in both cats. However, in cat 4, the left MG occupied a greater percentage of the cycle, which was associated with a marked delay in the onset and magnitude of activity in the right St (Fig. 3d). It thus appears that cat 4 had a smaller locomotor deficit (i.e., ankle yield) by using a more prominent bilateral adjustment between hindlimbs. The comparison of these 2 cats illustrates that although some adaptive changes can be similar between cats, there are still unique compensatory strategies that emerge following injury.

Variable adaptive mechanisms following spinal cord lesions Spinal cord injury (SCI) severely disrupts interactions between supraspinal, spinal, and peripheral structures (Cai et al., 2006; Frigon and Rossignol, 2006; Rossignol, 2006; Rossignol et al., 2008, 2009). Due to the inherent interindividual variability in neuronal connections and interactions between structures, it is not surprising that SCI can induce variable changes and adaptive strategies between animals. For

instance, following a complete SCI, changes in reflex responses during locomotion can differ quite dramatically between cats. During locomotion in intact cats, stimulating a cutaneous nerve evokes a pattern of short-latency inhibition (N1) followed by longer latency excitatory responses (P2) in ipsilateral (i.e., on the same side as the stimulation) extensors during stance. This pattern shows little variability between cats. However, following spinalization, there can be considerable interanimal variability. In some cats, following spinalization, P1 responses appear in LG and MG muscles during the stance phase, instead of the more common short-latency inhibition (Frigon and Rossignol, 2008a). In other cats, however, N1 responses persist. In an interesting case, in one cat P1 responses appeared in the left MG but not the left LG. P2 responses could increase, decrease, or remain unaltered from one extensor muscle and cat to another. This intra- and interanimal variability undoubtedly reflects some differences in cutaneous reflex pathways to specific motor pools and/or in independent sources of modulation to close synergists (Degtyarenko et al., 1996). It also indicates that supraspinal signals are important in shaping spinal reflex excitability, because a change from N1 to P1 responses after spinalization suggests activation of an alternate reflex pathway (i.e., a different spinal interneuronal route). Differences in reflex changes between animals can also be more subtle following spinalization. In Fig. 2, we showed that in cat 1 there were prominent P1 and P2 responses that were phasemodulated during locomotion, whereas in cat 2 primarily only P2 responses were found. Figure 4 shows the same nerve stimulation in the same two cats following a complete spinal transection. Note that the scales for reflex responses are larger in Fig. 4, while those for the activity of the St are the same as in control cycles. The bursting behavior of the muscle is shown on the far right and there was little change after spinalization. As can be seen, P2 responses were reduced in both cats, probably because the pathway responsible

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Fig. 3. Angular excursions and patterns of muscular activation before and 2 days after sectioning the left LGS nerve in two cats during locomotion. (a, b) Changes in the angle of the hip, knee, ankle, and metatarsophalangeal (MTP) joints of the left hindlimb during locomotion. The locomotor cycle is normalized to contact of the left foot. Phases and subdivisions (F, E1, E2, E3) are indicated at the top. (c, d) Rectified EMG bursts of selected muscles during the same episodes as in (a) and (b). The locomotor cycle is normalized to the onset of the left MG burst. Srt, anterior part of sartorius (hip flexor/knee extensor); MG, medial gastrocnemius (ankle extensor/knee flexor); VL, vastus lateralis (knee extensor); and St, semitendinosus (knee flexor/hip extensor). Each waveform is the average of approximately 20 cycles.

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Fig. 4. Reflex responses of the left St evoked by stimulating the tibial (Tib) nerve at 1.5 times the motor threshold in the same two cats as Fig. 2 during treadmill locomotion but following a complete spinal transection at T13. The responses shown in (a) and (b) are more than 40 days following spinalization. Values for each horizontal trace in a single graph are at the same scale in mV. However, the scales are different than in Fig. 2. Each horizontal trace is the average of approximately 10 cycles with stimulation superimposed on the background level of EMG derived from control cycles. The first horizontal trace in each figure is phase 0.05 (i.e., from 0.0 to 0.10) of the locomotor cycle synchronized to left St burst onset followed by 0.10, 0.15, etc. The rectified activity of the left St from control cycles is shown on the far right (90 ) at the same scales as in Fig. 2. The dashed vertical line indicates the time of the stimulation, whereas solid vertical lines indicate the time windows used to delineate P1 (black area, 10–25 ms) and P2 (gray area, 25–55 ms) responses.

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depends on a supraspinal contribution. In cat 1, P1 responses increased throughout the locomotor cycle but the pattern of phase-dependent modulation was similar (compare Fig. 4a with Fig. 2a). In cat 2, however, large P1 responses appeared during the swing phase and the swing-to-stance transition, a pattern that now more closely resembles cat 1. Such changes are most likely due to differences in the interactions between supraspinal signals and reflex pathways (Eccles and Lundberg, 1959; Holmqvist and Lundberg, 1959; Lundberg, 1967; Schomburg, 1990). There is a strong supraspinal contribution to reflex responses in flexors, which does not appear to be the case for extensors (Frigon and Rossignol, 2008a; Shimamura et al., 1991). Overall, following spinalization there are not only marked consistencies but also variable changes between animals during spinal locomotion.

Dual-lesion paradigms What happens if a lesion is followed by a complete spinal lesion? In such “dual-lesion paradigms,” the capacity to adapt to the new state of the system following spinalization is critically dependent on the first lesion. For instance, performing a peripheral nerve lesion before a spinalization adversely influences the ability to express spinal locomotion (Bouyer and Rossignol, 2003; Carrier et al., 1997; Frigon and Rossignol, 2009), whereas performing a partial spinal lesion before spinalization facilitates the expression of spinal locomotion (Barriere et al., 2008; Frigon et al., 2009). Recently, we showed that sectioning the left LGS nerve in three cats before spinalization introduced considerable interanimal variability in the ability to express spinal locomotion, which ranged from an inability to express spinal locomotion in one cat to an abnormal form of spinal locomotion in another (Frigon and Rossignol, 2009). Why such variability between animals following the same experimental protocol? It should be noted that no concerted

effort was made to provide a consistent behavioral context (e.g., daily locomotor training) in all cats after the initial peripheral denervation. Consequently, we surmise that activity-dependent processes following the peripheral denervation introduced different compensatory mechanisms, particularly in the interactions between supraspinal and peripheral inputs within the spinal locomotor circuitry. This was reflected by variable changes in reflex responses between cats (Frigon and Rossignol, 2007). As a result, the configuration of the locomotor circuitry was different from one cat to another at the time of spinalization, which produced variable changes following complete SCI. Further experiments are required to confirm this hypothesis. In contrast to a peripheral denervation, if a partial spinal lesion is followed by spinalization, the ability to express spinal locomotion is greatly facilitated (Barriere et al., 2008; Frigon et al., 2009). In some cats, a full weight-bearing hindlimb locomotion that can adjust to very high treadmill speeds is expressed within 24 h of the spinalization (Barriere et al., 2008). Treadmill training following the partial spinal lesion appears particularly helpful for the expression of hindlimb locomotion following spinalization because all trained cats expressed spinal locomotion within 24 h of spinalization. However, cats that were not trained following the partial lesion only showed some unilateral movements on the side of the partial lesion following spinalization. Thus, locomotor training appeared to increase consistency between animals after incomplete SCI. Treadmill training in complete spinal cats was shown to normalize the interactions between the spinal locomotor program and sensory pathways from load and cutaneous receptors (Cote and Gossard, 2004; Cote et al., 2003). A similar phenomenon might occur following incomplete SCI, whereby locomotor training standardizes interactions between the supraspinal, spinal, and peripheral structures, thus reducing interanimal variability following spinalization.

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Thus, as discussed in more detail in the following section, the “state” of the system at the time of spinalization can profoundly influence the recovery of locomotion following complete SCI and induce dramatic differences between individuals. Although this cannot directly extend to human studies it does suggest that the level of motor skill at the time of injury might be an important factor in how well SCI patients can recover.

Implications for locomotor adaptation Interindividual variability within motor systems undoubtedly has functional implications. If circuits were rigidly hard-wired it would prevent or greatly diminish our ability to learn new motor tasks and to recover functions following injury. There might also be an evolutionary function of interindividual variability, which permits new outcomes or motor behaviors to emerge within a population when confronted with a different set of circumstances. If variability was absent, the same solution to a given problem would occur over and over again. That animals use different strategies for learning and in the context of recovery simply reflects the inherent variability within sensorimotor systems, and also highlights the remarkable flexibility of the central nervous system. Consequently, it is not surprising that lesioning a given structure produces different results between animals during the same motor behavior. However, interindividual variability does pose a problem for therapeutic interventions where wishful thinking assumes that a given treatment will produce the desired outcome in all patients. In spinal cord-injured humans, treadmill training has been shown to be an effective treatment to promote the recovery of walking (Barbeau and Fung, 2001; Dietz et al., 1995; Edgerton et al., 2001; Harkema, 2001, 2008; Wernig et al., 1995). However, not all patients respond to treadmill training (Gorassini et al., 2009; Norton and

Gorassini, 2006). For instance, in 17 SCI patients subjected to the same treadmill training protocol, 9 responded positively, whereas 8 SCI showed no significant improvements (Gorassini et al., 2009), a nonnegligible proportion. In responders, locomotor training induced several changes in the magnitude and timing of muscular activity during walking, which were not observed in nonresponders. Specifically, Gorassini et al. (2009) found that responders had greater EMG activity in leg muscles prior to locomotor training and greater voluntary muscle strength, compared to nonresponders, indicating that the efficacy of spared descending pathways might be an important contributing factor in the recovery of walking. Interestingly, locomotor training increased the regularity of walking in responders and nonresponders (i.e., the timing of EMG bursts was more consistent from one walk cycle to another). In another study, the trajectory of the foot was also more consistent following training in SCI subjects (Grasso et al., 2004). Treadmill training is based on the principle that providing sensory cues consistent with normal walking facilitates locomotor recovery (Harkema, 2001). As suggested by others, this might be accomplished by modifying, or “normalizing” interactions between peripheral sensory feedback and the spinal locomotor CPG (Cote and Gossard, 2004; Cote et al., 2003; Frigon and Rossignol, 2006), a process that normally depends strongly on supraspinal influences. That the walking pattern is more regular after locomotor training in SCI patients is consistent with the hypothesis that training might stabilize interactions in spared descending pathways and peripheral sensory inputs with the spinal locomotor circuitry. It should also be noted that changes in the pattern of muscular activation in responders did not evolve or revert to a pattern normally observed in neurologically intact individuals. Instead, different adaptive strategies, such as increased extensor activity and cocontraction of antagonist muscles, were used (Gorassini et al., 2009). Adaptive strategies can also differ from one SCI

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patient to another. For instance, complete paraplegics make greater use of their arms and body to assist leg movements compared to incomplete paraplegics (Grasso et al., 2004). In effect, SCI patients attempt to maximize their locomotor performance by optimizing the function of remnant pathways and structures. This does not imply that the intrinsic spinal locomotor circuitry is rebuilt anew but that substantial modifications take place in how different inputs interact with the CPG. Consequently, the goal of rehabilitation should not be to alter the locomotor circuitry in one specific way but to shape the circuitry so it can accomplish the task more effectively. As a result, there probably are several optimal solutions to the same problem. Pretend that two very different configurations of the locomotor network can produce adequate walking. Some patients might respond better to one particular treatment because the remaining circuitry (i.e., the current state) can more easily be directed to one type of configuration than the other. For instance, if very little volitional control remains, protocols aimed at enhancing sensory feedback from the legs might be more effective than protocols geared primarily at voluntarily activating the legs. Moreover, some neurologically intact human subjects and animals are less responsive to a given input (e.g., sensory feedback), and it is likely that this phenomenon persists following injury. Therefore, it is important to consider the current state of the system, and hence interindividual variability, before initiating or pursuing costly training protocols.

Concluding remarks If intersubject variability is so ubiquitous, how is it that so few studies have specifically addressed it? Well for one, variability between individuals was often considered to result from experimental error. However, methodological improvements over the years have revealed that much of this variability cannot be explained by experimental

shortcomings. For example, to uncover the underlying mechanisms of locomotor recovery after injury, it is imperative that recordings be made in the same animal before and after a given lesion in order to minimize and evaluate interindividual variability. Interindividual variability in motor responses, such as spinal reflexes, activation patterns, or adaptive strategies should not be dismissed as experimental error, or eliminated by pooling data so that statistical analyses can be performed more easily. Although having a general idea of trends across individuals is necessary, so are changes that are specific to a smaller subset of the group, even at the individual level. This becomes of critical importance for rehabilitation following injury. If another treatment is more conducive to the recovery of motor functions in a small subset of the population than the “gold-standard,” then this treatment should be used. One of the great challenges will be in identifying a priori what treatment will be most effective for a particular individual or to design a method of quickly switching from one form of treatment to another.

Acknowledgments The present work was funded by a postdoctoral fellowship from the Christopher and Dana Reeve Foundation and by a postdoctoral fellowship from the Canadian Institutes of Health Research.

Abbreviations CPG FDL FHL LG MG MTP SCI TA Tib VL

central pattern generator flexor digitorum longus flexor hallucis longus lateral gastrocnemius medial gastrocnemius metatarsophalangeal spinal cord injury tibialis anterior tibial vastus lateralis

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References Abraham, L. D., & Loeb, G. E. (1985). The distal hindlimb musculature of the cat. Patterns of normal use. Experimental Brain Research, 58, 580–593. Abraham, L. D., Marks, W. B., & Loeb, G. E. (1985). The distal hindlimb musculature of the cat. Cutaneous reflexes during locomotion. Experimental Brain Research, 58, 594–603. Barbeau, H., & Fung, J. (2001). The role of rehabilitation in the recovery of walking in the neurological population. Current Opinion in Neurology, 14, 735–740. Barriere, G., Leblond, H., Provencher, J., & Rossignol, S. (2008). Prominent role of the spinal central pattern generator in the recovery of locomotion after partial spinal cord injuries. The Journal of Neuroscience, 28, 3976–3987. Bizzi, E., Tresch, M. C., Saltiel, P., & d'Avella, A. (2000). New perspectives on spinal motor systems. Nature Reviews. Neuroscience, 1, 101–108. Bizzi, E., Cheung, V. C., d'Avella, A., Saltiel, P., & Tresch, M. (2008). Combining modules for movement. Brain Research Reviews, 57, 125–133. Bouyer, L. J. G., & Rossignol, S. (2003). Contribution of cutaneous inputs from the hindpaw to the control of locomotion: 2. Spinal cats. Journal of Neurophysiology, 90, 3640–3653. Bouyer, L. J. G., Whelan, P., Pearson, K. G., & Rossignol, S. (2001). Adaptive locomotor plasticity in chronic spinal cats after ankle extensors neurectomy. The Journal of Neuroscience, 21, 3531–3541. Bucher, D., Prinz, A. A., & Marder, E. (2005). Animal-to-animal variability in motor pattern production in adults and during growth. The Journal of Neuroscience, 25, 1611–1619. Butz, M., Worgotter, F., & van, O. A. (2009). Activity-dependent structural plasticity. Brain Research Reviews, 60, 287–305. Cai, L. L., Courtine, G., Fong, A. J., Burdick, J. W., Roy, R. R., & Edgerton, V. R. (2006). Plasticity of functional connectivity in the adult spinal cord. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 361, 1635–1646. Caperchione, C. M., Kolt, G. S., & Mummery, W. K. (2009). Physical activity in culturally and linguistically diverse migrant groups to Western society: A review of barriers, enablers and experiences. Sports Medicine, 39, 167–177. Carrier, L., Brustein, L., & Rossignol, S. (1997). Locomotion of the hindlimbs after neurectomy of ankle flexors in intact and spinal cats: Model for the study of locomotor plasticity. Journal of Neurophysiology, 77, 1979–1993. Cheung, V. C., d'Avella, A., Tresch, M. C., & Bizzi, E. (2005). Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors. The Journal of Neuroscience, 25, 6419–6434.

Cote, M. P., & Gossard, J. P. (2004). Step training-dependent plasticity in spinal cutaneous pathways. The Journal of Neuroscience, 24, 11317–11327. Cote, M. P., Menard, A., & Gossard, J. P. (2003). Spinal cats on the treadmill: Changes in load pathways. The Journal of Neuroscience, 23, 2789–2796. Degtyarenko, A. M., Simon, E. S., & Burke, R. E. (1996). Differential modulation of disynaptic cutaneous inhibition and excitation in ankle flexor motoneurons during fictive locomotion. Journal of Neurophysiology, 76, 2972–2985. Delcomyn, F. (1980). Neural basis of rhythmic behavior in animals. Science, 210, 492–498. Dietz, V., Colombo, G., Jensen, L., & Baumgartner, L. (1995). Locomotor capacity of spinal cord in paraplegic patients. Annals of Neurology, 37, 574–582. Drew, T., & Rossignol, S. (1985). Forelimb responses to cutaneous nerve stimulation during locomotion in intact cats. Brain Research, 329, 323–328. Duysens, J., & Stein, R. B. (1978). Reflexes induced by nerve stimulation in walking cats with implanted cuff electrodes. Experimental Brain Research, 32, 213–224. Eccles, R. M., & Lundberg, A. (1959). Supraspinal control of interneurons mediating spinal reflexes. Journal de Physiologie, 147, 565–584. Edgerton, V. R., Leon, R. D., Harkema, S. J., et al. (2001). Retraining the injured spinal cord. Journal de Physiologie, 533, 15–22. English, A. W. (1979). Interlimb coordination during stepping in the cat: An electromyographic analysis. Journal of Neurophysiology, 42, 229–243. Fleshman, J. W., Lev-Tov, A., & Burke, R. E. (1984). Peripheral and central control of flexor digitorium longus and flexor hallucis longus motoneurons: The synaptic basis of functional diversity. Experimental Brain Research, 54, 133–149. Forssberg, H. (1985). Ontogeny of human locomotor control: I. Infant stepping, supported locomotion and transition to independent locomotion. Experimental Brain Research, 57, 480–493. Forssberg, H., & Svartengren, G. (1983). Hardwired locomotor network in cat revealed by a retained motor pattern to gastrocnemius after muscle transposition. Neuroscience Letters, 41, 283–288. Forssberg, H., Grillner, S., Halbertsma, J., & Rossignol, S. (1980). The locomotion of the low spinal cat: II. Interlimb coordination. Acta Physiologica Scandinavica, 108, 283–295. Frigon, A., & Gossard, J. P. (2009). Asymmetric control of cycle period by the spinal locomotor rhythm generator in the adult cat. Journal de Physiologie, 587, 4617–4628. Frigon, A., & Rossignol, S. (2006). Functional plasticity following spinal cord lesions. Progress in Brain Research, 157, 231–260.

116 Frigon, A., & Rossignol, S. (2007). Plasticity of reflexes from the foot during locomotion after denervating ankle extensors in intact cats. Journal of Neurophysiology, 98, 2122–2132. Frigon, A., & Rossignol, S. (2008a). Adaptive changes of the locomotor pattern and cutaneous reflexes during locomotion studied in the same cats before and after spinalization. Journal de Physiologie, 586, 2927–2945. Frigon, A., & Rossignol, S. (2008b). Locomotor and reflex adaptation after partial denervation of ankle extensors in chronic spinal cats. Journal of Neurophysiology, 100, 1513–1522. Frigon, A., & Rossignol, S. (2009). Partial denervation of ankle extensors prior to spinalization in cats impacts the expression of locomotion and the phasic modulation of reflexes. Neuroscience, 158, 1675–1690. Frigon, A., Barriere, G., Leblond, H., & Rossignol, S. (2009). Asymmetric changes in cutaneous reflexes after a partial spinal lesion and retention following spinalization during locomotion in the cat. Journal of Neurophysiology, 102, 2667–2680. Gorassini, M. A., Norton, J. A., Nevett-Duchcherer, J., Roy, F. D., & Yang, J. F. (2009). Changes in locomotor muscle activity after treadmill training in subjects with incomplete spinal cord injury. Journal of Neurophysiology, 101, 969–979. Grasso, R., Ivanenko, Y. P., Zago, M., et al. (2004). Distributed plasticity of locomotor pattern generators in spinal cord injured patients. Brain, 127, 1019–1034. Grillner, S. (1981). Control of locomotion in bipeds, tetrapods, and fish. In J. M. Brookhart & V. B. Mountcastle (Eds.), Handbook of physiology. The nervous system II (pp. 1179–1236). Bethesda, MD: The American Physiological Society. Grillner, S., & Wallen, P. (1985). Central pattern generators for locomotion, with special reference to vertebrates. Annual Review of Neuroscience, 8, 233–261. Grillner, S., Halbertsma, J., Nillsson, J., & Thorstensson, A. (1979). The adaptation to speed in human locomotion. Brain Research, 165, 177–182. Halbertsma, J. M. (1983). The stride cycle of the cat: The modelling of locomotion by computerized analysis of automatic recordings. Acta Physiologica Scandinavica, 521 (Suppl.), 1–75. Haridas, C., Zehr, E. P., & Misiaszek, J. E. (2005). Postural uncertainty leads to dynamic control of cutaneous reflexes from the foot during human walking. Brain Research, 1062, 48–62. Harkema, S. J. (2001). Neural plasticity after human spinal cord injury: Application of locomotor training to the rehabilitation of walking. The Neuroscientist, 7, 455–468. Harkema, S. J. (2008). Plasticity of interneuronal networks of the functionally isolated human spinal cord. Brain Research Reviews, 57, 255–264.

Holmqvist, B., & Lundberg, A. (1959). On the organization of the supraspinal inhibitory control of interneurons of various reflex arcs. Archives Italiennes de Biologie, 97, 340–356. Holtmaat, A., & Svoboda, K. (2009). Experience-dependent structural synaptic plasticity in the mammalian brain. Nature Reviews. Neuroscience, 10, 647–658. Ivanenko, Y. P., Cappellini, G., Dominici, N., Poppele, R. E., & Lacquaniti, F. (2007). Modular control of limb movements during human locomotion. The Journal of Neuroscience, 27, 11149–11161. Jordan, L. M. (1991). Brainstem and spinal cord mechanisms for the initiation of locomotion. In M. Shimamura, S. Grillner & V. R. Edgerton (Eds.), Neurobiological basis of human locomotion (pp. 3–20). Tokyo: Japan Scientific Societies Press. Juvin, L., Simmers, J., & Morin, D. (2007). Locomotor rhythmogenesis in the isolated rat spinal cord: A phase-coupled set of symmetrical flexion extension oscillators. Journal de Physiologie, 583, 115–128. Krouchev, N., Kalaska, J. F., & Drew, T. (2006). Sequential activation of muscle synergies during locomotion in the intact cat as revealed by cluster analysis and direct decomposition. Journal of Neurophysiology, 96, 1991–2010. Kumanyika, S. K. (2008). Environmental influences on childhood obesity: Ethnic and cultural influences in context. Physiology & Behavior, 94, 61–70. Levinsson, A., Holmberg, H., Broman, J., Zhang, M., & Schouenborg, J. (2002). Spinal sensorimotor transformation: Relation between cutaneous somatotopy and a reflex network. The Journal of Neuroscience, 22, 8170–8182. Loeb, G. E. (1993). The distal hindlimb musculature of the cat: Interanimal variability of locomotor activity and cutaneous reflexes. Experimental Brain Research, 96, 125–140. Loeb, G. E. (1999). Asymmetry of hindlimb muscle activity and cutaneous reflexes after tendon transfers in kittens. Journal of Neurophysiology, 82, 3392–3405. Loeb, E. P., Giszter, S. F., Saltiel, P., Bizzi, E., & MussaIvaldi, F. A. (2000). Output units of motor behavior: An experimental and modeling study. Journal of Cognitive Neuroscience, 12, 78–97. Lundberg, A. (1967). The supraspinal control of transmission in spinal reflex pathways. Electroencephalography and Clinical Neurophysiology, Suppl, 25, 35–46. Lundberg, A. (1981). Half-centres revisited. In J. Szentagothai, M. Palkovits & J. Hamori (Eds.), Regulatory functions of the CNS. Principles of motion and organization. Advances Physiology Sciences, (Vol. 1, pp. 155–167). Budapest: Pergamon Press. Marder, E., & Goaillard, J. M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews. Neuroscience, 7, 563–574. McCrea, D. A., & Rybak, I. A. (2008). Organization of mammalian locomotor rhythm and pattern generation. Brain Research Reviews, 57, 134–146.

117 Musselman, K. E., & Yang, J. F. (2007). Loading the limb during rhythmic leg movements lengthens the duration of both flexion and extension in human infants. Journal of Neurophysiology, 97, 1247–1257. Norton, J. A., & Gorassini, M. A. (2006). Changes in cortically related intermuscular coherence accompanying improvements in locomotor skills in incomplete spinal cord injury. Journal of Neurophysiology, 95, 2580–2589. O'Donovan, M. J., Pinter, M. J., Dum, R. P., & Burke, R. E. (1982). Actions of FDL and FHL muscles in intact cats: Functional dissociation between anatomical synergists. Journal of Neurophysiology, 47, 1126–1143. O'Donovan, M. J., Pinter, M. J., Dum, R. P., & Burke, R. E. (1985). Kinesiological studies of self- and cross-reinnervated FDL and soleus muscles in freely moving cats. Journal of Neurophysiology, 54, 852–866. Pearson, K. G. (2000). Neural adaptation in the generation of rhythmic behavior. Annual Review of Physiology, 62, 723–753. Pearson, K. G., Fouad, K., & Misiaszek, J. E. (1999). Adaptive changes in motor activity associated with functional recovery following muscle denervation in walking cats. Journal of Neurophysiology, 82, 370–381. Prinz, A. A., Bucher, D., & Marder, E. (2004). Similar network activity from disparate circuit parameters. Nature Neuroscience, 7, 1345–1352. Rossignol, S. (1996). Neural control of stereotypic limb movements. In L. B. Rowell & J. T. Sheperd (Eds.), Handbook of physiology, Section 12. Exercise: Regulation and integration of multiple systems (pp. 173–216). New York: Oxford University Press. Rossignol, S. (2006). Plasticity of connections underlying locomotor recovery after central and/ or peripheral lesions in the adult mammals. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 361, 1647–1671. Rossignol, S., Dubuc, R., & Gossard, J. P. (2006). Dynamic sensorimotor interactions in locomotion. Physiological Reviews, 86, 89–154. Rossignol, S., Barriere, G., Frigon, A., et al. (2008). Plasticity of locomotor sensorimotor interactions after peripheral and/or spinal lesions. Brain Research Reviews, 57, 228–240. Rossignol, S., Barriere, G., Alluin, O., & Frigon, A. (2009). Re-expression of locomotor function after partial spinal cord injury. Physiology (Bethesda), 24, 127–139. Schmidt, B. J., Meyers, D. E. R., Fleshman, J. L., Tokuriki, M., & Burke, R. E. (1988). Phasic modulation of short latency cutaneous excitation in flexor digitorum longus motoneurons during fictive locomotion. Experimental Brain Research, 71, 568–578. Schomburg, E. D. (1990). Spinal sensorimotor systems and their supraspinal control. Neuroscience Research, 7, 265–340.

Schouenborg, J. (2002). Modular organisation and spinal somatosensory imprinting. Brain Research. Brain Research Reviews, 40, 80–91. Schouenborg, J. (2003). Modular organisation and spinal somatosensory imprinting. Brain Research Reviews, 40, 80–91. Schouenborg, J. (2008). Action-based sensory encoding in spinal sensorimotor circuits. Brain Research Reviews, 57, 111–117. Schulz, D. J., Goaillard, J. M., & Marder, E. (2006). Variable channel expression in identified single and electrically coupled neurons in different animals. Nature Neuroscience, 9, 356–362. Seefeldt, V., Malina, R. M., & Clark, M. A. (2002). Factors affecting levels of physical activity in adults. Sports Medicine, 32, 143–168. Shimamura, M., Tanaka, I., & Livingston, R. B. (1991). Longitudinal conduction systems serving spinal and brainstem coordination (spino-bulbo-spinal reflex). In M. Shimamura, S. Grillner & V. R. Edgerton (Eds.), Neurobiological basis of human locomotion (pp. 241–255). Tokyo: Japan Scientific Societies Press. Stein, P. S. G., & Smith, J. L. (1997). Neural and biochemical control strategies for different forms of vertebrate hindlimb locomotor tasks. In P. S. G. Stein, S. Grillner, A. I. Selverston & D. G. Stuart (Eds.), Neurons, networks and motor behavior (pp. 61–73). Computational neurosciences series. Cambridge, MA: MIT Press. Stuart, D. G., Withey, T. P., Wetzel, M. C., & Goslow, G. E. J. (1973). Time contraints for inter-limb co-ordination in the cat during unrestrained locomotion. In R. B. Stein, K. G. Pearson, R. S. Smith & J. B. Redford (Eds.), Control of posture and locomotion (pp. 537–560). New York: Plenum Press. Vilensky, J. A., & Gankiewicz, E. (1990). Effects of growth and speed on hindlimb joint angular displacement patterns in vervet monkeys (Cercopithecus aethiops). American Journal of Physical Anthropology, 81, 441–449. Vilensky, J. A., & Patrick, M. C. (1984). Inter and intratrial variation in cat locomotor behavior. Physiology & Behavior, 33, 733–743. Vilensky, J. A., Gankiewicz, E., & Townsend, D. W. (1988). Effects of size on vervet (Cercopithecus aethips) gait parameters: A cross-sectional approach. American Journal of Physical Anthropology, 76, 463–480. Vilensky, J. A., Gankiewicz, E., & Townsend, D. W. (1990). Effects of size on vervet (Cercopithecus aethiops) gait parameters: A longitudinal approach. American Journal of Physical Anthropology, 81, 429–439. Wernig, A., Muller, S., Nanassy, A., & Cagol, E. (1995). Laufband therapy based on ‘rules of spinal locomotion’ is effective in spinal cord injured persons. The European Journal of Neuroscience, 7, 823–829.

118 Wetzel, M. C., Atwater, A. E., Wait, J. V., & Stuart, G. G. (1975). Neural implications of different profiles between treadmill and overground locomotion timings in cats. Journal of Neurophysiology, 38, 492–501. Wolpaw, J. R. (2007). Spinal cord plasticity in acquisition and maintenance of motor skills. Acta Physiologica (Oxford), 189, 155–169. Wolpaw, J. R., & Tennissen, A. M. (2001). Activity-dependent spinal cord plasticity in health and disease. Annual Review of Neuroscience, 24, 807–843. Yang, J. F., Stephens, M. J., & Vishram, R. (1998). Infant stepping: A method to study the sensory control of human walking. Journal de Physiologie, 507(Pt. 3), 927–937.

Yang, J. F., Lam, T., Pang, M. Y., Lamont, E., Musselman, K., & Seinen, E. (2004). Infant stepping: A window to the behaviour of the human pattern generator for walking. Canadian Journal of Physiology and Pharmacology, 82, 662–674. Yang, J. F., Lamont, E. V., & Pang, M. Y. (2005). Split-belt treadmill stepping in infants suggests autonomous pattern generators for the left and right leg in humans. The Journal of Neuroscience, 25, 6869–6876. Zehr, E. P., Komiyama, T., & Stein, R. B. (1997). Cutaneous reflexes during human gait: Electromyographic and kinematic responses to electrical stimulation. Journal of Neurophysiology, 77, 3311–3325.