Distributed plasticity of locomotor patterngenerators in spinal cord injured patients²Renato Grasso,1 Yuri P. Ivanenko,1 Myrka Zago,1 Marco Molinari,1,2 Giorgio Scivoletto,1

Vincenzo Castellano,1 Velio Macellari3 and Francesco Lacquaniti1,4

1IRCCS Fondazione Santa Lucia, via Ardeatina 306,

00179 Rome, 2Institute of Neurology, Catholic University,

00197 Rome, 3Biomedical Engineering Laboratory,

Istituto Superiore di SanitaÁ, 00168 Rome and 4Department

of Neuroscience and Centre of Space Bio-medicine,

University of Rome Tor Vergata, Via Montpellier 1,

00133 Rome, Italy

Correspondence to: Professor Francesco Lacquaniti,

University of Rome Tor Vergata and IRCCS Fondazione

Santa Lucia, via Ardeatina 306, 00179 Rome, Italy

E-mail: lacquaniti@caspur.it

²Deceased

SummaryRecent progress with spinal cord injured (SCI) patientsindicates that with training they can recover some loco-motor ability. Here we addressed the question ofwhether locomotor responses developed with trainingdepend on re-activation of the normal motor patternsor whether they depend on learning new motor pat-terns. To this end we recorded detailed kinematic andEMG data in SCI patients trained to step on a tread-mill with body-weight support (BWST), and in healthysubjects. We found that all patients could be trained tostep with BWST in the laboratory conditions, but theyused new coordinative strategies. Patients with moresevere lesions used their arms and body to assist the legmovements via the biomechanical coupling of limb andbody segments. In all patients, the phase-relationship ofthe angular motion of the different lower limb segmentswas very different from the control, as was the patternof activity of most recorded muscles. Surprisingly, how-ever, the new motor strategies were quite effective ingenerating foot motion that closely matched the normal

in the laboratory conditions. With training, foot motionrecovered the shape, the step-by-step reproducibility,and the two-thirds power relationship between curva-ture and velocity that characterize normal gait. Wemapped the recorded patterns of muscle activity ontothe approximate rostrocaudal location of motor neuronpools in the human spinal cord. The reconstructedspatiotemporal maps of motor neuron activity in SCIpatients were quite different from those of healthy sub-jects. At the end of training, the locomotor networkreorganized at both supralesional and sublesional levels,from the cervical to the sacral cord segments. We con-clude that locomotor responses in SCI patients may notbe subserved by changes localized to limited regions ofthe spinal cord, but may depend on a plastic redistribu-tion of activity across most of the rostrocaudal extent ofthe spinal cord. Distributed plasticity underlies recoveryof foot kinematics by generating new patterns of muscleactivity that are motor equivalents of the normal ones.

Keywords: human paraplegia; muscle synergies; motor equivalence; human locomotion; central pattern generators

Abbreviations: ASIA = American Spinal Injury Association; BF = long head of biceps femoris; BIC = biceps brachii;

BWS = body weight support; BWST = body-weight-support on treadmill; CPG = central pattern generator; ES = erector

spinae; GCL = gastrocnemius lateralis; GM = gluteus maximus; GT = greater trochanter; IL = ilium; LD = latissimus

dorsi; LE = lateral femur epicondyle; LM = lateral malleolus; MAS = Modi®ed Ashworth Scale; MN = motor neuron; OE

= external oblique; OI = internal oblique; RAM = middle rectus abdominis; RAS = superior rectus abdominis; RF = rectus

femoris; SCI = spinal cord injury; TA = tibialis anterior; TRAP = trapezius; TRIC = triceps brachii; VL = vastus lateralis;

VM = ®fth metatarso-phalangeal joint; VMA = normalized tolerance area of VM; WISCI = Walking Index for Spinal Cord

Injury

Received October 14, 2003. Revised December 18, 2003. Accepted December 19, 2003. Advance Access publication February 25, 2004

Brain Vol. 127 No. 5 ã Guarantors of Brain 2004; all rights reserved

DOI: 10.1093/brain/awh115 Brain (2004), 127, 1019±1034

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IntroductionIn traditional schemes the human spinal cord is assigned a

subservient function for the production of complex move-

ments, being viewed as an in¯exible conduit for information

transmitted to and from supraspinal systems. However, recent

data on spinal cord injury (SCI) patients challenge this view

by showing that the spinal cord has the potential to generate

rhythmic motor activity in a ¯exible, task-dependent manner

(Bussel et al., 1988; Barbeau et al., 1993; Brown et al., 1994;

Calancie et al., 1994; Nathan, 1994; Dobkin et al., 1995;

Wernig et al., 1995; Harkema et al., 1997; Dimitrijevic et al.,

1998; Shapkova and Schomburg, 2001; Dietz et al., 2002;

Ivanenko et al., 2003). Patterned sensory inputs play a key

role in facilitating and modulating the spinal rhythmic output,

and daily locomotor training with body-weight-support on a

treadmill (BWST) often results in signi®cant improvements

in locomotor function in motor-incomplete SCI patients

(Dietz et al., 1995, 1998; Dobkin et al., 1995; Wernig et al.,

1995; Barbeau et al., 1999a, b; Barbeau and Fung, 2001).

Locomotor improvements often extend outside the labora-

tory, some form of walking becoming possible in these

patients.

In motor-complete SCI patients, unsupported walking

seldom, if ever, recovers. However, the peripheral sensory

inputs associated with BWST can in¯uence the motor

patterns in these patients under laboratory conditions

(Dobkin et al., 1995; Wernig et al., 1995; Harkema et al.,

1997; Maegele et al., 2002). It has been shown that the

isolated lumbosacral spinal cord can interpret load (Harkema

et al., 1997; Dietz et al., 2002) and speed (Patel et al., 1998)

information in a state-dependent manner. Under optimal

conditions of limb loading, treadmill speed and appropriate

kinematics, patients with clinically complete SCI could

generate three to 10 consecutive steps without assistance on

at least one leg (Harkema et al., 2000; Maegele et al., 2002).

Understanding the mechanisms of locomotor responses

after a spinal lesion is fundamental to the development of

improved rehabilitation strategies (Barbeau et al., 1999a, b;

Harkema et al., 2000; Wernig et al., 2000: Edgerton et al.,

2001; Dietz et al., 2002; Edgerton and Roy 2002; Dietz, 2003;

Ivanenko et al., 2003). There is a growing consensus that

recovery largely depends on plasticity phenomena induced by

the lesion (Calancie et al., 1994; Wernig et al., 1995;

Harkema et al., 1997; Barbeau et al., 1999a,b; Dietz et al.,

1999; Dobkin 2000; Raineteau and Schwab, 2001; Calancie

et al., 2002). What is the functional outcome of the plastic

reorganization of the lesioned spinal cord? An especially

important but unresolved question is whether locomotor

responses depend on the re-activation of the normal motor

patterns or do they depend on learning new motor patterns

(Dietz et al., 1999; Pearson 2000; de Leon et al., 2001). In the

experimental model of cats trained with BWST after a

complete low-thoracic spinal cord transection, the patterns of

muscle activity are very similar to those in the normal animal

(Belanger et al., 1996). This similarity indicates that recovery

mainly depends on the re-activation of the neuronal circuits

involved in generating the motor patterns in normal animals.

In spinalized cats, however, re-activation is contingent on the

afferent feedback (Pearson, 2001) and the speci®c training

task (de Leon et al., 1998).

The picture in human SCI-subjects is much less clear.

Improved performance in BWST-trained patients is associ-

ated with an increase in the level and extent of modulation of

activity in leg extensor muscles (Dietz et al., 1995), larger

than can be voluntarily recruited from resting positions

(Wernig et al., 1995; Maegele et al., 2002). However, several

motor neuron (MN) pools located below the lesion may

remain unable to generate normal patterns and levels of

activity suf®cient to support body weight and to propel the

limbs and body forward (Dietz et al., 1999; de Leon et al.,

2001). This probably depends on the loss of facilitatory inputs

from supraspinal centres. Cortico-spinal and other supra-

spinal descending systems are more dominant for the control

of locomotion in higher primates than in the other mammals

such as the cat (Duysens and Van de Crommert, 1998).

Therefore, one might hypothesize that, in contrast with

spinalized cats, SCI-subjects cannot entirely re-activate the

normal motor patterns but must develop new compensatory

strategies to replace lost function.

If so, a new question arises: what aspects of movement, if

any, are regained after spinal injury? Although the motor

output from the spinal cord consists of the waveforms of

muscle activity, major locomotor goals are de®ned in terms of

foot kinematics (trajectory and speed) and kinetics (contact

forces). The control of foot position requires more global

coordination than the control of the position of a single joint,

as the former depends on the spatiotemporal coordination of

multiple muscles acting on several body and limb segments

(Winter, 1991; Ivanenko et al., 2002a). There are some

indications that the human spinal cord can interpret global

limb parameters such as foot loading and translation

(Harkema et al., 1997; Dietz et al., 2002). However, the

kinematic determinants that can be expressed by the human

spinal cord are still poorly understood (Barbeau et al., 1999a;

Harkema et al., 2000; Ivanenko et al., 2003).

Here we addressed these questions by applying quantita-

tive analysis to kinematic and EMG data recorded in detail

both in SCI patients trained with BWST and in healthy

subjects. Our aim was not to assess the ef®cacy of BWST as a

therapy, but to explore the mechanisms involved in locomotor

improvements associated with this training.

MethodsSubjectsEleven SCI patients (Table 1) and 11 healthy age-matched subjects

were studied. In a previous study we reported factor analysis from

these subjects (Ivanenko et al., 2003). In most patients the

neurological level of the injuries was located in the thoracic cord

and no patient had signs of conus medullaris syndrome. At hospital

admission, they were submitted to neurological evaluation, routine

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radiological and neurophysiological tests. No signs of denervation

were found in the leg muscles by EMG. They were classi®ed

according to the American Spinal Injury Association (ASIA)

impairment scale (Maynard et al., 1997). Five patients were

classi®ed as ASIA-A (complete paraplegia, no sensory or motor

function below the neurological level including S4±S5 segments),

two as ASIA-B (sensory but not motor function is preserved below

the neurological level), and four as ASIA-C (motor function is

preserved below the neurological level, and more than half of key

muscles below this level have a muscle grade less than 3 out of 5, i.e.

they cannot be actively contracted against gravity). It should be

noticed that the assessment of completeness of a spinal lesion is

based on clinical, radiological and neurophysiological tests indicat-

ing the absence of motor and sensory function below the injury site,

but it does not necessarily imply that there are no axons that cross the

injury site.

At discharge, two ASIA-C patients were re-classi®ed as ASIA-D

(at least half of key muscles below the neurological level have a

muscle grade equal to or higher than 3 out of 5, i.e. they can be

actively contracted against gravity or some resistance), whereas the

classi®cation of the other patients did not change. Written informed

consent was obtained from each subject according to the Declaration

of Helsinki; the experiments and training procedures were approved

by the Ethical Committee of IRCCS Fondazione Santa Lucia.

Experimental set-upSubjects stepped on a treadmill (EN-MILL 3446.527; Bonte Zwolle

BV, Netherlands) at different controlled speeds. They placed the

abducted arms on horizontal rollbars located at the side of the

treadmill, at breast height. Body weight support (BWS) was obtained

by suspending the subjects in a parachute harness (Reha, BONMED,

Germany) connected to a pneumatic device that applied a controlled

upward force at the waist (Ivanenko et al., 2002a). The overall

constant error in the applied force and dynamic force ¯uctuations

monitored by a load cell were <5% of the residual body weight

(Gazzani et al., 2000). Three-dimensional motion of selected body

points was recorded at 100 Hz by means either of the Optotrak

system (Northern Digital, Waterloo, Ontario) (63 SD accuracy

better than 0.2 mm for x, y, z coordinates) or of 9-TV cameras Vicon-

612 system (1-mm accuracy) during about 20±100 s depending on

treadmill speed. Five infrared markers were attached on the right

side of the subject to the skin overlying the following landmarks: the

mid-point between the anterior and the posterior superior iliac spine

(ilium, IL), greater trochanter (GT), lateral femur epicondyle (LE),

lateral malleolus (LM) and ®fth metatarso-phalangeal joint (VM). In

all controls and nine patients EMG activity was recorded by means

of surface electrodes from leg muscles [tibialis anterior (TA),

gastrocnemius lateralis (GCL), long head of biceps femoris (BF),

rectus femoris (RF), vastus lateralis (VL), gluteus maximus (GM)],

axial muscles [middle rectus abdominis (RAM) and superior rectus

abdominis (RAS), external oblique (OE), internal oblique (OI),

latissimus dorsi (LD), erector spinae (ES)] and shoulder girdle

muscles [trapezius (TRAP), triceps brachii (TRIC), biceps brachi

(BIC)]. In six controls and three patients, EMG was also recorded

from peroneus longus, semitendinosus, adductor longus, sartorius,

tensor fasciae latae and deltoid. In two controls and two patients,

EMG was also recorded from soleus, gastrocnemius medialis,

gluteus medius and ilio-psoas. In two patients, EMG was only

recorded from leg muscles (TA, GCL, BF, RF, VL, GM). EMG

signals were preconditioned at the recording site (active electrodes

from BTS, Milan, Italy or DelSys, Boston, MA, USA), digitized,

transmitted to the remote ampli®er (20-Hz high-pass and 200-Hz

low-pass ®lters), and sampled at 500 or 1000 Hz (synchronized with

kinematic sampling).

ProtocolPatients performed daily sessions of BWST training for 1±3 months,

starting from 1±6 months after the lesion. Training began shortly

after admission and continued till discharge. Patients were assisted to

step as necessary by two physiotherapists. Under their guidance,

patients underwent progressive training with increasing treadmill

speed, decreasing BWS and decreasing manual assistance from the

therapists. Each physiotherapist initially held one patient's leg at the

ankle to assist with swing and foot placement, but patients were

encouraged to step independently as soon as possible. BWS was set

at 75% of body weight at the beginning and was subsequently

decreased by 5% steps according to the patient's improvement. Three

ASIA-C/D patients reached 0% BWS and 2±3 km/h at the end of

training, whereas one ASIA-C (SCI-C4) and all ASIA-A/B patients

never went below 60±75% and faster than 1±2 km/h. Treadmill speed

was set at 0.7 km/h in the ®rst session, and was increased to 1, 1.5, 2

and 3 km/h whenever possible. This was done because it has been

Table 1 Subject characteristics

Subject Gender Age(years)

Weight(kg)

Lesionlevel

Aetiology Lesiontime

Trainingtime

Admission Discharge

(months) (months) ASIA MAS RMI WISCI Gar ASIA MAS RMI WISCI Gar

SCI-A1 M 30 75 T9 Trauma 2 2 A 0 0 0 0 A 0 3 0 1SCI-A2 F 35 52 L1 Trauma 6 2 A 5 2 0 0 A 4 4 9 1SCI-A3 M 34 75 L2 Trauma 2 1.5 A 1 0 0 0 A 1 4 0 0SCI-A4 M 46 65 T5 Trauma 6 2 A 0 0 0 0 A 0 3 0 0SCI-A5 M 60 76 T9 Vascular 1 2 A 0 0 0 0 A 0 3 0 0SCI-B1 M 17 65 T12 Vascular 3 3 B 0 0 0 0 B 0 4 9 1SCI-B2 M 56 72 T3 Neoplastic 2 1 B 0 0 0 0 B 0 3 0 1SCI-C1 F 28 52 L2 Trauma 1 3 C 2 2 0 0 D 1 6 19 5SCI-C2 M 67 64 T9 Neoplastic 4 1.5 C 4 0 2 0 D 1 15 19 6SCI-C3 M 58 60 C7 Trauma 5 1.5 C 1 0 0 0 C 1 9 19 5SCI-C4 F 59 59 T6 Neoplastic 5 2 C 2 2 0 0 C 2 4 0 1

Lesion level indicates the clinical neurological level, lesion time the time interval between lesion diagnosis and training onset, trainingtime the time interval between training onset and end. RMI = the Rivermead Mobility index; Gar = the Garrett score for deambulation.

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suggested that SCI patients may execute the swing phase of stepping

more independently at faster than at slower speeds (Harkema et al.,

2000). However, the initial training condition (75% BWS, 0.7 km/h)

was included in each session till the end of training. Kinematic and

EMG data were collected during stepping attempts 1 day before and

then every 15 days after the start of training. The recording session

after 15 days could not be performed in patient SCI-C1. Patients were

tested at all previously trained values of BWS/speed. The default

condition (75% BWS, 0.7 km/h) was always included. During the

recording sessions, patients stepped by themselves, being helped by

the physiotherapists only when they stumbled. The speci®c strategies

used to step differed widely among patients, and will be described

under Results. Patients performed several such trials, each comprised

eight to 15 consecutive step cycles, and paused between trials when

fatigued. During the pauses, the unloading system was released and

the patient sat on a chair. Training and recording sessions were

interrupted at subjects' request or when heart rate or blood pressure

(constantly monitored) reached attention levels (this occurred very

rarely). All control subjects were tested in one experimental session

at 0.7, 1, 1.5, 2 and 3 km/h, and at 0, 35, 50 and 75% BWS. Controls

did not undergo daily training with BWST but, to verify repeatability

of results, seven of them were tested in additional experiments

separated by 1 or more months.

Clinical evaluationWe measured mobility by means of Rivermead Mobility Index

(Collen et al., 1991, 0±15 score), and ambulation by means of the

Walking Index for Spinal Cord Injury (WISCI) (Ditunno et al., 2000,

2001; 0±20 scale) and the Garrett scale (Garrett et al., 1987; 0±6

scale). Leg spasticity was evaluated by the Modi®ed Ashworth Scale

(MAS) modi®ed by Bohannon and Smith (1987). In MAS, f denotes

¯accidity, 0 denotes `no increase in muscle tone', whereas 1±5

denote increasing levels of spasticity. MAS scores 0, 1, 2, 3, 4, 5 are

sometimes also indicated as 0, 1, 1+, 2, 3, 4, respectively, and MAS

has one score (1+) intermediate between scores 1 and 2 of the

original Ashworth scale. Here we summarize the general trend,

while individual data are reported in Table 1.

In motor-complete SCI patients, the mean Rivermead score was

0.3 6 0.7 (n = 7), and both WISCI and Garrett scores were 0 before

training (meaning that patients could not ambulate outside the

BWST apparatus). After training, stepping remained non-functional

outside the laboratory conditions in ®ve patients; their mean

Rivermead score was 3.2 6 0.4, Garrett 0.4 6 0.5 and WISCI 0.

Two patients could ambulate with support, their scores being:

Rivermead 4, Garrett 1 and WISCI 9 (meaning that they could

ambulate for 10 m with walker, braces and no physical assistance)

(Ditunno et al., 2000, 2001).

In motor-incomplete SCI patients, the mean Rivermead score was

1 6 1.1 (n = 4), WISCI 0.5 6 1 and Garrett 0 before training. After

training, community walking became possible in three of these

patients, their mean scores being: Rivermead 10 6 4.6, WISCI 19,

and Garrett 5.3 6 0.6. In one patient (SCI-C4), stepping remained

non-functional outside the laboratory (WISCI 0).

Patients had a variable degree of spasticity, and no patient was

¯accid.

Data analysisThe body was modelled as an interconnected chain of rigid

segments: IL-GT for the pelvis, GT-LE for the thigh, LE-LM for

the shank and LM-VM for the foot. The elevation angle of each

segment in the sagittal plane corresponds to the angle between the

projected segment and the vertical. These angles are positive in the

forward direction (i.e. when the distal marker is located anterior to

the proximal marker). The limb axis was de®ned as GT-LM. Gait

cycle was de®ned as the time between two successive maxima of the

elevation angle of the limb axis. The time of maximum and

minimum elevation of the limb axis corresponds to heel-contact and

toe-off (stance to swing transition), respectively, in healthy subjects

(Bianchi et al., 1998). These time markers were used to identify

stance and swing phases. In previous experiments in which a force

platform (Kistler 9281B) was used to monitor the contact forces

during ground walking, we found that this kinematic criterion

predicts the onset and end of stance phase with an error smaller than

2% of the gait cycle duration (Borghese et al., 1996). This

observation was con®rmed in the present experiments in four

control and two SCI-subjects by monitoring in-shoe forces (PEDAR-

mobile system, Novel, Germany). The insole contains 99 capacitive

sensors interposed between the subject's foot and the shoe to

measure the external vertical contact forces. Before each trial, the

mean level of each sensor was measured while the foot was unloaded

for a few seconds and this value was used as a zero level. Pressure

threshold was 2 N/cm2. We found that the resultant vertical force

derived from the pressure sensors went above threshold (corres-

ponding to foot contact) and below threshold (foot take-off) in

coincidence with the maximum and minimum elevation of the limb

axis, respectively (with a precision of about 2% of the gait cycle).

For some gait cycles, notably some of those performed during the

®rst recording session in SCI patients, the swing phase could not be

separated reliably from the stance phase. These cycles were

excluded from further quantitative analysis. Analyses were carried

out on the pooled data of all gait cycles of a given trial. To this end,

the data were time-interpolated over individual gait cycles on a time

base with 200 points. Averages were constructed over all gait cycles

of a trial. Ensemble control averages were constructed by pooling the

data recorded under comparable BWS/speed conditions from all

healthy subjects. Comparisons between patients and controls were

performed for the default condition of 75% BWS, 0.7 km/h

(recorded in every session of all patients and controls) and the

illustrations refer to this condition except when explicitly indicated.

When available, trials at higher speeds and lower BWS were also

analysed.

Reference frames for end-point trajectoryWe analysed the trajectory of the distal-most marker (VM) of the

foot relative to: (i) a moving intrinsic frame attached to the IL; and

(ii) a ®xed extrinsic frame attached to the treadmill. In the former

case, foot trajectory appears as though the IL was ®xed in space. In

the latter case, foot trajectory appears as recorded, except that

marker's position was corrected by subtracting the mean horizontal

IL position cycle by cycle in order to account for possible drifts of

the subject along the treadmill during the recording epoch. All

results, except those of Fig. 2, will be illustrated using this latter

method.

End-point pathThe mean area described by foot trajectory was derived by

computing the surface of the polygon de®ned by the x, y coordinates

of VM marker (in the ®xed extrinsic frame) for each cycle and by

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taking the average value over all cycles of each trial. In order to

compare foot trajectories between patients and controls, we

represented each VM trajectory as a time series of vectors and

then compared the two resulting vector ®elds through a correlation

measure. The directional correlation coef®cient is given by the ratio

of the co-variance of the times series and the product of their

standard deviations.

End-point variabilityFoot-trajectory variability was quanti®ed in terms of instantaneous

spatial density and normalized tolerance area of VM (VMA),

computed separately over the swing and the stance phase (Ivanenko

et al., 2002a). VM trajectories were ®rst re-sampled in the space

domain by means of linear interpolation of the x, y time series

(1.5-mm steps). Spatial density was calculated for each trial as the

number of points falling in 0.5 3 0.5 cm2 cells of the spatial grid

divided by the number of gait cycles. The density of each cell was

depicted graphically by means of a colour scale (empty cells were

excluded in the plot). Normalized tolerance area was derived as

follows. The mean length of foot trajectory over the swing phase (or

over the stance phase) was calculated over each trial as the

corresponding path integral. Every 10% of the horizontal excursion

we computed the 2D 95% tolerance ellipsis of the points within the

interval. The typical number of points in each interval ranged from

300 to 1000 (depending on the stepping speed and the number of gait

cycles). The areas of all tolerance ellipses were summed and

normalized by the mean length of foot trajectory. This total area

provides an estimate of the mean area covered by the points per 1 cm

of path (VMA) and is measured in cm2/cm.

Velocity±curvature power lawTo study the velocity±curvature relationship of VM trajectory, all

samples corresponding to the swing phase of a trial were pooled

together (Ivanenko et al., 2002b). Then we performed a linear

regression analysis in log-log scales of equation w(t) = K´C(t)b,

where w(t) and C(t) are the instantaneous values of the angular

velocity and path curvature of VM, respectively, K is a velocity gain

factor that depends on overall movement duration, and b is the

power exponent. In logarithmic scales, a power function becomes a

straight line whose slope corresponds to the exponent.

Inter-segmental coordinationThe inter-segmental coordination was evaluated as described

previously (Borghese et al., 1996; Bianchi et al., 1998; Grasso

et al., 1998). Brie¯y, the changes of the elevation angles of the thigh,

shank and foot co-vary linearly throughout the gait cycle. When

these angles are plotted one versus the others in a 3D graph, they

describe a gait loop that can be ®tted by a plane computed by means

of orthogonal linear regression. The 3D orientation of the covariance

plane is directly related to the phase-relationship of inter-segmental

coordination, and is measured by the plane normal, i.e. the vector

orthogonal to the plane (Bianchi et al., 1998). As reference data, we

computed the mean normal and its 95% con®dence cone from all

healthy subjects (Mardia, 1972).

EMG analysisRaw data were numerically recti®ed, low-pass ®ltered with a zero-

lag Butterworth ®lter with cut-off at 15 Hz, time-interpolated over a

time base with 200 points for individual gait cycles and averaged.

Factor analysis of a subset of these data has been previously reported

(Ivanenko et al., 2003).

Spatiotemporal patterns of MN activity in the spinal

cordThe recorded patterns of EMG activity were mapped onto the

rostrocaudal location of ipsilateral MN pools in the human spinal

cord (for a related application to cat locomotion data see Yakovenko

et al., 2002). This reconstruction is based on the approximate

rostrocaudal location of MN pools innervating different muscles in

the human spinal cord based on published charts of segmental

localization. In general, each muscle is innervated by several spinal

segments. Kendall et al. (1993) compiled reference segmental charts

for all body muscles by integrating the anatomical and clinical data

of several different sources. A capital X in Kendall's chart denotes a

localization agreed upon by ®ve or more sources, a lower-case x

denotes agreement of three to four sources, and an x in brackets (x)

denotes agreement of only two sources. In our maps, X and x were

weighted 1 and 0.5, respectively, whereas we discarded (x). On the

whole, 0.5 weights were 19% of the total. We assumed that our

population of subjects has the same spinal topography as this

reference population. To reconstruct the output pattern of any given

spinal segment, all recti®ed EMG waveforms corresponding to that

segment were averaged and normalized to the maximum during the

gait cycle after subtraction of the minimum. For this analysis three

sets of ensemble averages were constructed from the pooled data of

all controls, ASIA-C/D patients and ASIA-A/B patients.

StatisticsStatistical comparisons between patients and controls were per-

formed at matched values of treadmill speed and BWS, using

t-statistics. Analysis of variance designs were used when appropriate

to test for the effect of different conditions on locomotor parameters.

Reported results are considered signi®cant for P < 0.05. Statistics on

correlation coef®cients were performed on the normally distributed,

Z-transformed values. Spherical statistics of directional data were

used to compare the normal to the covariance plane (see above)

between patients and controls (Mardia, 1972).

ResultsPrior to training, some SCI patients were completely unable

to step. However, all patients could be trained to step with

BWST in laboratory conditions. The speci®c strategies used

to step differed widely among patients. Most incomplete

paraplegics recovered independent control of leg muscles

suf®cient to propel the limbs in swing and to support body

weight in stance. Complete paraplegics instead used their

arms and body to assist the leg movements via the

biomechanical coupling of limb and body segments. In the

following we detail kinematic and EMG changes associated

with training in both sets of patients.

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Foot kinematicsIn those patients who could step in the ®rst recording session

prior to training, the spatial path of the foot was highly

irregular, jerky and variable from step to step (Fig. 1A). With

training, foot path tended to regain gradually the shape typical

of normal stepping, with reduced step-by-step variability. The

foot traced a loop in the sagittal plane whose extent and

regularity was assessed by computing the mean area of VM,

the distal-most marker: the larger the surface, the longer is the

step length and the higher is the foot clearance from ground,

for any given treadmill speed. VM surface increased with

training in eight out of 11 patients. On average in this group,

VM surface in the last session was signi®cantly higher than in

the ®rst session (by 3.37 6 1.28 times, P < 0.01 paired t-test).

In three patients, VM surface did not differ signi®cantly

between these two sessions (mean ratio = 0.96 6 0.04).

Fig. 1 (A) Effects of training on shape and variability of foot trajectory in one ASIA-C patient compared with a typical control. Thetrajectories of the IL and the distal-most marker (VM) of the foot over consecutive unassisted step cycles have been superimposed. Firstsession was 1 day before training, last session 90 days later. (B) Spatial density of VM path in different training sessions in one ASIA-Bpatient. Spatial density was integrated over the swing phase: the lower the density (toward the blue in the colour-cued scale), the greaterthe variability. Plots are anisotropic, vertical scale being expanded relative to horizontal scale. (C) Total VMA path integrated over theswing phase for all patients (connected symbols in different colours for each patient) as a function of training session. First-session dataare missing for two patients because of their complete inability to step. Green area denotes mean 6 SD of the controls. (D) Time courseof curvature and angular velocity of VM trajectory, averaged over a trial in one ASIA-A patient at the end of training, superimposed on atypical control. (E) Relationship (in logarithmic scales) between angular velocity and curvature of VM trajectory in the same patient. Allsamples corresponding to the swing phases of a trial were pooled together. Linear regression analysis was performed to estimate theexponent b from the slope. (F) Exponent (b) and r2 of the angular velocity±curvature relationship in all patients compared with mean 6SD values of controls. Values are plotted as a function of BWS.

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The time course of foot trajectory was compared between

patients and controls by computing the correlation coef®cient

between the time series of VM position vectors in each

patient and the ensemble average in controls. At the end of

training, the mean correlation coef®cient was 0.98 6 0.03

across all patients. Foot trajectory also was analysed separ-

ately in the vertical direction (foot lift, VMy) and in the

horizontal direction (foot translation, VMx). In ASIA-C/D

patients, the mean correlation coef®cient was 0.94 6 0.04 and

0.99 6 0.01 for VMy and VMx, respectively. In ASIA-A/B

patients, the mean correlation was 0.74 6 0.07 and 0.95 60.04 for VMy and VMx, respectively. (These values have

been previously reported in Ivanenko et al., 2003.) The lower

correlation in the vertical direction is due to foot-drop in

paraplegics.

Foot-trajectory variability was quanti®ed in terms of the

instantaneous spatial density and the normalized tolerance

area of VM (VMA), computed separately over the swing and

stance phase (Fig. 1B and C). All patients exhibited a

signi®cant reduction of the variability during both phases (P <

0.005, paired t-test between ®rst and last session), although

the degree of improvement differed markedly among patients

(from 5 to 100% of the ®rst session, on average 76 6 42%).

Changes of performance in patients with training can be

contrasted with the stereotypical and stable performance of

control subjects. Thus, VMA exhibited limited variability

both across subjects (green area in Fig. 1C denotes mean 6SD over all controls), and within subjects. Seven controls

were tested several times over a period of 1 to several months

between sessions. On average, VMA was 2.3 6 0.6 cm2/cm

over all subjects. VMA was 2.4 6 0.3 cm2/cm across eight

different sessions performed by one subject over a time span

of 4.8 years.

At the end of training, foot trajectory also obeyed the two-

thirds power relationship between instantaneous curvature

and angular velocity that characterizes normal gait (Ivanenko

et al., 2002b). Figure 1D shows the time course of VM

angular velocity (w) and curvature (C) in one patient and one

control. These variables are widely modulated but they co-

vary throughout the gait cycle: w increases (decreases) with

increasing (decreasing) C. The w±C relationship obtained in

the patient is plotted in Fig. 1E. In all patients, the correlation

was high (r = 0.95 6 0.03) and the exponent close to the

nominal two-thirds value (b = 0.68 6 0.05). Neither b nor r

differed signi®cantly from the control values at any tested

value of BWS (Fig. 1F).

Patients often restored foot kinematics by implementing

new coordinative strategies that involved the trunk in addition

to the lower limbs. In Fig. 2 foot trajectories are plotted

relative to the extrinsic space (as in Fig. 1) or relative to the

intrinsic frame attached to the IL. The former trajectories

appear as recorded, except for the correction of subject's

drifts along the treadmill. The latter trajectories, instead,

describe the foot motion that would occur without any

contribution by trunk and pelvis motion. Therefore extrinsic

and intrinsic trajectories almost coincide when subjects step

with limited excursion of the trunk and pelvis; this was the

case in normal subjects and in less severe SCI patients. By

contrast, motor-complete paraplegics stepped with consider-

able excursion of the pelvis that shifted in synchrony with leg

motion (notice the wide excursion of IL in Fig. 2). Thus, the

vertical IL-displacement in ASIA-A/B patients was 8.7 6 2.3

times higher than in controls. As a result, only the extrinsic

trajectory of the foot resembled the normal one, whereas the

intrinsic trajectory differed substantially. In some cases the

trajectory was actually reversed between swing and stance,

foot position being higher during stance than during swing

(see patient SCI-B2 in Fig. 2). These results indicate that

control of foot trajectory in severe SCI patients was not

accomplished by means of the normal pattern of coordination

of the lower limb segments. This point is taken up in the

following section.

Inter-segmental kinematic coordinationIn healthy subjects, the main segments of the lower limbs

oscillate back and forth with a stereotypical waveform and a

progressive phase-shift from the thigh to the shank to the foot

(Fig. 3D). This pattern of inter-segmental coordination was

altered in SCI patients. Prior to training, angular oscillations

were often of small amplitude, especially at the thigh, with

considerable step-by-step variability (Fig. 3A±C, left panels).

With training, the amplitude increased and the variability

decreased, but some important features of the waveform were

never restored during our observation period. The minimum

value of each segment angle occurred later in the gait cycle

Fig. 2 The trajectories of the ilium (IL) and foot (VM) in a typical control, two ASIA-C patients and two ASIA-B patients at the end oftraining. In each panel the data are plotted relative to external space or relative to the instantaneous ilium position in the leftmost andrightmost stick diagrams, respectively. Swing-phase and stance-phase data are plotted in red and blue, respectively.

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than in controls. Moreover, the phase-relationship between

limb segments remained abnormal. This can be best appre-

ciated by considering the 3D gait loops obtained by plotting

the elevation angles one versus the others (cubes of Fig. 3).

The loops evolve close to a plane in both controls and patients

indicating a strong linear covariance between the temporal

changes of the segment angles. The planar regression

accounts for 99.1 6 0.3% of the variance in controls and

98.4 6 1.1% in SCI patients. The 3D orientation of the

covariance plane (given by the plane normal) measures the

phase-relationship of inter-segmental coordination (Bianchi

et al., 1998). The plane orientation varies very little among

normal subjects (compare the controls of Fig. 3D): the angle

of the 95% con®dence cone (denoting the angular dispersion)

for the mean reference normal is only 68° over all controls.

By contrast, the plane orientation in patients systematically

Fig. 3 Patterns of inter-segmental kinematic coordination. The mean (6 SD) waveforms of the elevation angles of thigh, shank and footwere computed from all step cycles of a trial and are plotted versus the normalized gait cycle. Angles are positive in the forwarddirection. The inset in each panel shows the 3D gait loop obtained by plotting the elevation angles one versus the others. The loop resultsby superimposing the step cycles of the corresponding panel. Mean value of each angular coordinate has been subtracted. Paths progressin time in the counter-clockwise direction, foot-contact and lift-off phases corresponding to the top and bottom of the loops, respectively.Grids correspond to the best-®tting planes and their intersection with the cubic wire frame of the angular coordinates. Each side of thecube corresponds to 660°. Data recorded from three SCI patients at the indicated days of training are plotted in A±C, and data from threehealthy subjects are plotted in D.

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differed from this reference. Thus, at the end of training, it

deviated by 54 6 22° (range 20 4 86°) in ASIA-A/B patients,

and by 31 6 26° (range 15 4 71°) in ASIA-C/D patients.

Muscle activity patternsThe extent of modulation of activity of limb and body

muscles during the gait cycle increased with training (Fig. 4).

The ratio of maximum to minimum of recti®ed EMG in the

last session was signi®cantly higher than in the ®rst session

(by 3.24 6 1.13, P < 0.01). In ASIA-C/D patients, the mean

amplitude of activity of leg muscles (TA, GCL, BF, RF, VL,

GM) over the gait cycle in the last session did not differ

signi®cantly from controls, whereas the mean activity of axial

muscles (RAM, RAS, OE, OI, LD, ES, TRAP) was signi®-

cantly greater than in controls (by 3.19 6 1.59, P < 0.01). In

ASIA-A/B, instead, the mean activity of leg muscles was

signi®cantly smaller than in controls (mean ratio = 0.29 60.24, P < 0.001), and the mean activity of axial muscles was

signi®cantly greater (4.86 6 1.87, P < 0.005).

In ASIA-C/D patients, activity in ankle extensors (GCL)

regained a quasi-normal waveform (Fig. 4C). However, the

pattern of activity of most other recorded muscles often

remained altered in SCI patients during our observation

period. Thus, whereas controls activated reciprocally knee

¯exors (BF) and extensors (RF, VL) (Fig. 5A), patients co-

activated knee ¯exors and extensors throughout stance

(Fig. 5B), or activated knee ¯exors briskly only in early

stance and late swing with little modulation of knee extensors

(Fig. 4A and 5C). SCI patients largely relied on proximal and

axial muscles to lift the foot and to project the limb forward

(Fig. 5). The correlation coef®cient between the time series of

activation of each muscle in a patient and the corresponding

ensemble average in controls varied widely among patients

but was generally low (Fig. 6; r = 0.13 6 0.36, range ±0.63 40.89).

In association with the abnormal patterns of leg muscle

activity in ASIA-A/B patients, also the time course of

changes of the limb joint angles often remained poorly related

to that of normal subjects (Fig. 5). The hip normally extends

during stance and ¯exes during swing in healthy subjects

(Fig. 5A; Winter, 1991; Borghese et al., 1996). In motor-

complete paraplegics, instead, the hip extended little during

stance; it initially extended during swing due to inertial

coupling with trunk translation and rotation and then ¯exed

(Fig. 5C). Also, knee ¯exion in mid-stance was faster and

more prolonged than in controls. Finally, the ankle extended

in stance later than in controls and ¯exed less during swing.

On average, the correlation coef®cient between the time

series of joint angles in ASIA-A/B patients and the corres-

ponding ensemble average in controls was ±0.47 6 0.24,

0.82 6 0.15 and ±0.01 6 0.42 for hip, knee and ankle,

respectively (Fig. 6). In ASIA-C/D patients, the correlation

coef®cients were higher (mean r = 0.90 6 0.30, 0.96 6 0.03,

and 0.56 6 0.32 for hip, knee and ankle, respectively).

The data of Fig. 6 summarize the trend previously

described: at the end of training, the time series of foot

position in space is roughly comparable to that of the controls

(high correlation coef®cients), but the corresponding time

series of changes of limb segment angles, joint angles, and

Fig. 4 Patterns of EMG activity of lower limb muscles. Mean waveforms were computed from the same patients of Fig. 3A±C and areplotted versus the normalized gait cycle.

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EMG activities deviate further and further from those of the

controls (low correlation coef®cients).

Spatiotemporal patterns of MN activity in thespinal cordThe approximate map of activity of MN pools during

locomotion was reconstructed by mapping the recorded

EMG waveforms on the published charts of segmental

localization. The derived spatiotemporal patterns of MN

activity along the rostrocaudal axis of the spinal cord are

plotted in Fig. 7. The map is limited to levels between C3 and

S2 in relation to the set of recorded muscles. It re¯ects the

relative amplitude of activity in each given spinal segment as

a function of the gait cycle, because it is based on averaged,

normalized EMG waveforms (see Methods). This map does

not provide any information about the absolute amount of

activity in the spinal cord.

In the lumbosacral spinal cord of healthy subjects, a brief

burst of activity occurs just prior to and during heel strike in

Fig. 5 Locomotor patterns in a typical control (A), ASIA-C-patient (B) and ASIA-B-patient (C) at the end of training. The horizontal(VMx) and vertical (VMy) foot coordinates (mean 6 SD), the elevation angles of foot, thigh and shank, the joint angles of knee, hip andankle, and the EMG patterns of the indicated muscles (see Abbreviations) are plotted from top to bottom. Hip, knee and ankle angles arepositive in extension, ¯exion and plantar-¯exion, respectively. Speed was 0.7 km/h, and BWS 75%.

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L1±L4 segments. This burst is associated with EMG activity

in hip ¯exors, knee extensors and ankle dorsi¯exors. It is

responsible for extending the leg and foot prior to heel strike,

and for weight acceptance at the beginning of stance. The

focus of activity then shifts to L5±S2 segments resulting in a

prolonged burst of activity with a peak in mid-stance. This

focus is mainly associated with activity in hip extensors and

ankle plantar-¯exors, providing support moment and forward

thrust. A lower amplitude focus appears at the time of

transition between stance and swing, and is responsible for

pulling the swinging limb forward. This focus starts with

relatively low intensity in more caudal segments of the

lumbosacral spinal cord, and then jumps to cranial segments

(where it has higher intensity). At cervical and thoracic levels

of the spinal cord, a burst of activity occurs in late stance and

stance-to-swing transition in T1±T4 segments, and another

burst occurs in late swing in C3±C8 segments and in T5±T12

segments. These bursts are related to the trunk stabilization

activity of different trunk muscles. Notice, however, that

the cervical-thoracic foci would appear of much smaller

amplitude than those in the lumbosacral segments on an

absolute scale of activity.

The corresponding maps of MN activity are very different

in SCI patients. In ASIA-C/D patients, the focus of activity

over L2±L4 segments starts later and is much more prolonged

than in controls: it begins at foot strike and extends through

mid-stance. This focus partially overlaps in time with the

following burst in L5±S2 segments, whereas the latter lasts

less than in controls. Extensive regions of the spinal cord

(C3±L1 and L4±L5) become very active at the transition

between stance and swing, contributing to pulling the

swinging limb forward.

In ASIA-A/B patients the pattern is still different. There are

four very brief, almost impulsive bursts of activity. A ®rst burst

centred in L5±S2 segments is responsible for weight accept-

ance after foot strike and involves the activation of hip

extensors and ankle plantar-¯exors. A second burst occurs at

mid-stance to provide support moment and forward propulsion;

it involves C3±C6 and, with smaller relative amplitude, C8±L3

segments. A third burst occurs in extensive regions of the spinal

Fig. 6 Correlation analysis between patients and controls. We computed the correlation coef®cient between the time series of the indicatedkinematic and EMG variables over the normalized gait cycle in each patient and the corresponding time series from the ensemble averageof all controls. Data from patients were obtained at the end of training. Values of different patients are plotted with different symbols,grouped from top to bottom according to ASIA-scale.

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cord (C3±L5) at the transition between stance and swing, as in

ASIA-C/D patients. Finally, a relatively smaller burst occurs at

late swing, prior to foot strike, and involves L5±S2 segments.

DiscussionThere are three main points in this study: (i) locomotor

responses in SCI patients mainly depend on learning new

motor strategies to replace lost function; (ii) these new

strategies are motor equivalents of the normal ones in so far as

they produce roughly normal kinematics of the foot; (iii) the

reconstructed maps of activity of MN pools show major

spatiotemporal changes involving a plastic redistribution of

activity across most of the rostrocaudal extent of the spinal

cord.

Control hierarchyThe patients could be trained to step with BWST, but they

used new coordinative strategies. Patients with more severe

lesions stepped with considerable excursion of the pelvis

position in synchrony with leg motion. In all patients, the

phase-relationship (in some also the waveform) of the angular

motion of the different lower limb segments was very

different from the control, as was the pattern of activity of

most recorded muscles. Surprisingly, however, the new motor

strategies were quite effective in generating foot motion that

closely matched the normal in the laboratory conditions. With

training, foot motion of SCI patients tended to regain the

shape, the step-by-step reproducibility, and the two-thirds

power relationship between curvature and velocity that

characterize normal gait (Ivanenko et al., 2002a, b). A

correlation analysis between the gait waveforms of the

patients and those of the controls yielded the following

ranking (from high to low correlation): foot position, limb

segment angles, joint angles and EMG patterns (Fig. 6). This

ranking is congruent with current ideas on the hierarchy of

control in locomotion (Lacquaniti et al., 1999, 2002; Poppele

and Bosco, 2003). A control hierarchy is de®ned operation-

ally by the extent to which different gait parameters vary

under different walking conditions: parameters that vary the

least are placed at the highest control level, whereas those

varying the most are placed at the lowest level. Healthy

subjects accurately regulate foot kinematics across wide

changes of body load and stepping speed (Winter, 1991;

Ivanenko et al., 2002a, b). At the following level of control,

the limb segment angles are not speci®ed independently of

each other, but they co-vary in time on a loop con®ned close

Fig. 7 Spatiotemporal patterns of MN activity along the rostrocaudal axis of the spinal cord. The outputpattern of any given segment (left vertical scale) was reconstructed by mapping the recorded EMGwaveforms onto the known charts of segmental localization (see Methods). The pattern is plotted versusthe normalized gait cycle, and its relative amplitude is denoted by a colour scale (right calibration bar). Ineach panel white dotted lines denote the stance-to-swing transition time. The ensemble averages of allcontrols, ASIA-C/D patients and ASIA-A/B patients are plotted from left to right. Speed was 0.7 km/h,and BWS 75%

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to a plane (Borghese et al., 1996; Bianchi et al., 1998). In

contrast with foot kinematics, the timing of segment angle

kinematics does change with body load and stepping speed

(Ivanenko et al., 2002a). At a still lower level of control, the

temporal patterns of muscle activity vary the most across

loads and speeds, adapting to the varying biomechanical

requirements (Winter 1991; Ivanenko et al., 2002a).

Motor equivalenceThe control hierarchy obeys the principle of motor equiva-

lence stating that an invariant task goal can be achieved with

variable means (Lashley, 1933; Hebb, 1949; Lacquaniti,

1989). Thus, our handwriting is recognizable regardless of

whether the pen is held between the ®ngers, the toes or the

teeth. Lashley (1933) introduced the concept of motor

equivalence in the context of lesion studies by showing that

monkeys with motor cortex lesions were still capable of

opening a box despite the paresis. A lesioned nervous system

might take advantage of the natural redundancy in the

neuromuscular system to accomplish a given motor goal at

the limb end-point (hand or foot) by means of new

compensatory muscle synergies (Winter, 1991; Kazennikov

et al., 1998; Cirstea and Levin, 2000). To our knowledge, the

present study is the ®rst to show quantitatively that trained

SCI patients use motor equivalence. Patients learn to produce

new temporally tuned patterns of muscle activity, resulting in

the desired kinematics of the foot via the biomechanical

coupling of the angular motion of different limb and body

segments (Bianchi et al., 1998; Lacquaniti et al., 1999).

Indeed, patients used extensively their arm and/or axial

muscles to assist the swing phase (Fig. 5). It has been noticed

that upper extremity paralysis has a restrictive effect on

independent ambulation (Maegele et al., 2002). Coupled

angular motions also generate sensory stimulation that can

entrain both supra- and sublesional segments of the cord and

result in appropriately patterned activity of muscles (Pearson,

2001). The speci®c procedures used to train SCI patients

might be instrumental in leading to restoration of foot

kinematics. Thus, physiotherapists (or robotic orthoses; see

de Leon et al., 2002; Dietz et al., 2002) act as external

teachers by minimizing the output error of foot trajectory

relative to a prede®ned template. Patients might then try to

reproduce the learnt template during unassisted stepping.

This procedure is equivalent to supervised learning in

recurrent networks (Doya, 2003).

Locomotor pattern generationWe computed the spatiotemporal maps of spinal MN

activation (Fig. 7) by combining two data sets: (i) averaged,

recti®ed EMG waveforms were derived from the simultan-

eous recordings of EMG activity of several limb and trunk

muscles during many step cycles; (ii) the approximate

rostrocaudal location of MN pools innervating the corres-

ponding muscles was derived from published charts of

segmental localization. First we discuss methodological

issues.

Locomotor pattern generators output command signals

directed to MN pools. Each action potential in a MN

propagates along the efferent axon and gives rise to a motor

unit action potential in the innervated muscle. All motor unit

action potentials generated by all active MNs sum to produce

the recordable EMG signal. The recti®ed EMG then provides

an indirect measure of the net ®ring of MNs of that muscle in

the spinal cord at any given moment during locomotion. The

exact quantitative relationship between the net motor unit

action potential rate and the amplitude of EMG waveforms

cannot be established uniquely. However, this was not a

serious drawback for the current application. Because we

were comparing subjects with very different levels of muscle

activity, our interest was in the temporal pattern of relative

activation at a given segmental level, rather than in the

absolute intensity of the signal. Therefore averaged EMG

waveforms were normalized to the maximum during the gait

cycle. Finally, the maps were constructed based on a large but

incomplete sample of muscles. In particular, no foot muscle

was recorded. Further studies will be needed to ®ll in the

gaps.

The rostrocaudal location of MN pools was derived from

the charts of spinal segmental localization complied by

Kendall et al. (1993), under the assumption that our

population of subjects has the same spinal topography.

Kendall et al. (1993) compiled reference segmental charts for

all body muscles by integrating the anatomical and clinical

data of several different sources. Functional MR imaging of

the human cervical spinal cord has con®rmed so far the

anatomical localization of published segments (Stroman et al.,

2001). Despite likely anatomical variability, the data of these

charts appear suf®ciently robust for the spatial resolution

currently available in our reconstruction technique.

As this is the ®rst study to report spatiotemporal maps of

spinal MN activation in man, we can only compare the results

between our different groups of subjects, and with the map

obtained by Yakovenko et al. (2002) for the cat lumbosacral

spinal cord. They constructed the map from anatomical data

on MN localization obtained by Vanderhorst and Holstege

(1997) and from a compilation of published records of EMG

activity during locomotion of intact cats. Interestingly, the

lumbosacral map we obtained in healthy subjects roughly

agrees with that of Yakovenko et al. (2002), taking into

account the inter-species anatomical differences. In both sets

of maps, the focus of activity oscillates rostrocaudally during

the gait cycle. Rostral and caudal parts of the lumbosacral

enlargement are active during swing and stance, respectively,

and activity jumps from one region to the other at the

transition times. The foci of activity could correspond to

waves of activation propagating back and forth along the

spinal cord or to abrupt switching between distinct burst

generators (Kiehn et al., 1998; Orlovsky et al., 1999;

Yakovenko et al., 2002). We have also been able to detect

patterned activity at cervical and thoracic levels, presumably

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related to trunk stabilization in different phases of the gait

cycle. This ®nding cannot be compared with Yakovenko et al.

(2002), as they did not investigate trunk muscles.

The corresponding spatiotemporal maps of MN activity are

very different in SCI patients. The main foci of activity in

lumbosacral cord seen in healthy subjects are also present in

motor-incomplete paraplegics. However, activity switches

between the foci in the former, whereas the foci are co-active

for extended periods of stance in the latter. The map in motor-

complete paraplegics departs even more radically from the

control map. It shows regions of complete silence or low-

amplitude activity (black or blue in Fig. 7) much more

extensive than in the other groups of subjects. Silence is

interrupted by impulsive bursts of activity appearing sparsely

during the gait cycle, with a location and timing quite

different from the normal. In both motor-complete and motor-

incomplete paraplegics, extensive regions of the spinal cord

including cervical, thoracic and lumbar segments are briskly

active at the stance-to-swing transition.

The term central pattern generator (CPG) designates a

spinal network that can generate patterns of rhythmic activity

for locomotion even in the absence of external feedback or

supraspinal control (Grillner and Wallen, 1985). Normally,

however, the spinal network is modulated by peripheral and

supraspinal inputs (Orlovsky et al., 1999). The exact organ-

ization of CPGs is still largely unknown in mammals (Kiehn

et al., 1998; Barbeau et al., 1999b). They are thought to

comprise a network of inter-neurons linked to the output stage

of a-motor neurons, but opinions diverge as to whether the

vertebrate CPGs are localized or distributed (Kiehn et al.,

1998; Orlovsky et al., 1999; Yakovenko et al., 2002). The

present data do not provide any information about the activity

of inter-neurons, but they show the organization of the output

stage. The network of a-motor neurons actively oscillating

during the step cycle appears widely spaced over extensive

regions of the spinal cord. This implies that individual burst

generators are coupled by long propriospinal neurons

projecting to different pools of a-motor neurons across

several spinal segments (Nathan et al., 1996).

Distributed plasticityWe showed that, after spinal lesion, the locomotor network

can reorganize to an extent not previously reported. The

reorganization involved all investigated segments, both

supralesional and sublesional ones, extending from the

cervical to the sacral cord. Lesion- and training-induced

plasticity might be responsible for changes in the connections

of the network. These changes are probably adaptive and

learnt (being speci®c to the trained task; de Leon et al., 1998)

and involve a major redistribution of activity to different limb

and body muscles (Pearson, 2001), creating new muscle

synergies (Barbeau et al., 1999b). The speci®c re-organ-

ization of the network might also depend on the level of the

lesion (Dietz et al., 1999), but here the limited number of

patients did not allow a correlation between the lesion level

and the motor patterns.

Spinal lesions probably trigger multiple forms of plasticity.

Synaptic strength could be modi®ed in pre-existing circuits

(synaptic plasticity), and new circuits might develop through

sprouting and anatomical reorganization, including growth of

axonal branches and dendrites (anatomical plasticity;

Raineteau and Schwab, 2001). Evidence for plastic reorgan-

ization caudal to the level of injury has been recently

provided by Calancie et al. (2002). They demonstrated novel

upper limb re¯exes evoked by lower limb stimulation,

emerging more than 6 months after a high cervical spinal

cord lesion. In addition to plasticity of intrinsic spinal

networks, plasticity of unlesioned descending pathways can

also contribute (Giszter et al., 1998). Bene®cial plasticity

often involves undamaged neural areas that may take over the

function of damaged ones (Raineteau and Schwab, 2001).

This is the case for plasticity induced by sensory stimuli at the

cortical level after cerebral injury (Fraser et al., 2002). In the

case of spinal lesion, the CNS should be capable of

substantial reorganization because cortical, sub-cortical and

much of the intrinsic spinal cord circuitry remain largely

intact and still partially interconnected by unlesioned ®bres.

Cortical reorganization in SCI patients may result in

enhanced excitability of motor pathways targeting muscles

rostral to the level of a spinal lesion, re¯ecting reorganization

of motor pathways either within cortical motor areas or at the

level of the spinal cord (Topka et al., 1991; Dobkin, 2000;

Curt et al., 2002). In particular, it can be hypothesized that

stepping after a severe spinal lesion depends on cortical (and

voluntary) control much more heavily than it does in healthy

subjects (where locomotion is more automatic). The cortical

motor areas, that encode distal leg movements and become

disconnected from their target pools of motor neurons in the

spinal cord after spinal injury, might re-direct their command

signals to the adjacent cortical motor areas that control more

proximal body segments. Thus, the plastic reorganization of

pattern generation in the spinal cord we demonstrated might

mirror a similar reorganization of the control centres in the

motor cortex.

ConclusionsWe argued that locomotor improvement in SCI patients may

not be subserved by changes localized to limited regions of

the spinal cord, but may depend on a plastic redistribution of

activity across most of the rostrocaudal extent of the spinal

cord. Distributed plasticity underlies recovery of foot

kinematics by generating new patterns of muscle activity

that are motor equivalent of the normal ones. The locomotor

programmes encrypted in the reorganized networks allowed

functional recovery of unsupported gait in most incomplete

paraplegics, whereas they remained non-functional in most

complete paraplegics outside laboratory conditions, as they

could not walk without body support. Lack of functional

recovery in these patients is due to, among other factors, the

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low level of activity in leg muscles and lack of adequate

postural control. However, the demonstration of extensive

distributed plasticity may prove relevant also for future

rehabilitation of the latter patients.

AcknowledgementsWe dedicate this paper to the memory of Dr Renato Grasso

who devoted his best energies to the success of the project.

We thank the therapists D. Angelini, B. Morganti and

M. Piccioni for training the patients, Dr L. Ercolani and

D. Prissinotti for help with experiments, Dr J. F. Ditunno and

Dr J. Fung for advice on the project. The ®nancial support of

Italian Health Ministry, Italian University Ministry (MIUR),

Italian Space Agency (ASI), and C.N.R. (Progetto Strategico

Neuroscienze) is gratefully acknowledged.

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