Influence of Spinal Cord Integrity on Gait Control in Human Spinal Cord Injury

Abstract

Background Clinical trials in spinal cord injury (SCI) primarily rely on simplified outcome metrics (ie, speed, distance) to obtain a global surrogate for the complex alterations of gait control. However, these assessments lack sufficient sensitivity to identify specific patterns of underlying impairment and to target more specific treatment interventions. Objective To disentangle the differential control of gait patterns following SCI beyond measures of time and distance. Methods The gait of 22 individuals with motor-incomplete SCI and 21 healthy controls was assessed using a high-resolution 3-dimensional motion tracking system and complemented by clinical and electrophysiological evaluations applying unbiased multivariate analysis. Results Motor-incomplete SCI patients showed varying degrees of spinal cord integrity (spinal conductivity) with severe limitations in walking speed and altered gait patterns. Principal component (PC) analysis applied on all the collected data uncovered robust coherence between parameters related to walking speed, distortion of intralimb coordination, and spinal cord integrity, explaining 45% of outcome variance (PC 1). Distinct from the first PC, the modulation of gait-cycle variables (step length, gait-cycle phases, cadence; PC 2) remained normal with respect to regained walking speed, whereas hip and knee ranges of motion were distinctly altered with respect to walking speed (PC 3). Conclusions In motor-incomplete SCI, distinct clusters of discretely controlled gait parameters can be discerned that refine the evaluation of gait impairment beyond outcomes of walking speed and distance. These findings are specifically different from that in other neurological disorders (stroke, Parkinson) and are more discrete at targeting and disentangling the complex effects of interventions to improve walking outcome following motor-incomplete SCI.

Keywords: gait; human; motor control; spinal cord injury.

Conflict of interest statement

Conflict of interest disclosures: None.

© The Author(s) 2015.

Figures

Figure 1. Multivariate analysis of gait-related parameters

Principal components (PCs) 1–3 explain 74.2% of the total variance. PCs show clustered variance of multiple parameters with high loadings on the corresponding PC. Variable loadings are depicted in numbers next to the arrows whose color reflect magnitude and relationship of loading (positive relationship = red, negative = blue) (A). Transformed data is plotted in the 3D space determined by the orthogonal PCs 1 to 3 and grouped according to neurological condition (B). AH = abductor hallucis, MEP = motor evoked potential, ROM = range of motion, SSEP = somatosensory evoked potential, TA = tibialis anterior, TP = tibialis posterior.

Figure 2. Speed modulation of parameters contributing to PC1

(A) Absolute speed and speed modulation is limited in iSCI patients. Theoretical linear increase is indicated by the red dotted line. Error bars indicate ± 1SD. (B) The diagram illustrates the modulation of the hip-knee angular velocity at toe-off. A 3rd degree polynomial fit (thin dotted line) was used to interpolate control subjects’ data to obtain their angular velocity at patients’ fast speed. Error bars indicate 1SD. (C) Hip-knee cyclograms of control subjects (top 2 rows) and patients (bottom 2 rows) both at 0.5 km/h and at preferred (pref) walking speed are shown. 20 gait cycles are plotted, black color indicates stance phase, grey color indicates swing phase. (D) The cycle-to-cycle reproducibility of the hip-knee cyclogram (ACC) improved with increasing speed in both groups but was lower in patients at preferred speed. (E) The shape difference of the cyclogram compared to normal (SSD) improved in patients at preferred speed but remained different from controls at all speeds. ACC = angular component of coefficient of correspondence, AngVel = angular velocity at toe-off, SD = standard deviation, SSD = square root of sum of squared distances (cyclogram shape difference). * p < 0.05.

Figure 3. Speed modulation of parameters contributing to PC2

(A through D) Modulation of step length, cadence, single support and stance phase of control subjects (grey area, ± 2SD interval) and patients (large dots = group mean values, small dots = individual patient’s data) at different walking speeds (0.5 km/h to 7.0 km/h in controls, 0.5 km/h to 5.5 km/h in patients). The two groups did not differ.

Figure 4. Relation of gait-related parameters to preferred speed

(A and B) Angular velocity at toe-off and SSD (shape difference to normal) showed linear relations to walking capacity and separated the two groups. (C and D) The degree of spinal cord integrity did not correlate with walking capacity but clearly distinguished the two groups. (E and F) Ranges of motion showed weak to moderate relation to walking capacity and does not distinguish between groups. A linear fit was added to the graphs (dotted lines) to illustrate trends although Spearman’s correlation does not assume linearity of data. AngVel = angular velocity at toe-off, MEP = motor evoked potential, ROM = range of motion, SSD = sum of squared distances (cyclogram shape difference), SSEP = somatosensory evoked potential, TA = tibialis anterior, TP = tibialis posterior.