Neuromuscular adjustments of gait associated with unstable conditions

Abstract

A compact description of coordinated muscle activity is provided by the factorization of electromyographic (EMG) signals. With the use of this approach, it has consistently been shown that multimuscle activity during human locomotion can be accounted for by four to five modules, each one comprised of a basic pattern timed at a different phase of gait cycle and the weighting coefficients of synergistic muscle activations. These modules are flexible, in so far as the timing of patterns and the amplitude of weightings can change as a function of gait speed and mode. Here we consider the adjustments of the locomotor modules related to unstable walking conditions. We compared three different conditions, i.e., locomotion of healthy subjects on slippery ground (SL) and on narrow beam (NB) and of cerebellar ataxic (CA) patients on normal ground. Motor modules were computed from the EMG signals of 12 muscles of the right lower limb using non-negative matrix factorization. The unstable gait of SL, NB, and CA showed significant changes compared with controls in the stride length, stride width, range of angular motion, and trunk oscillations. In most subjects of all three unstable conditions, >70% of the overall variation of EMG waveforms was accounted for by four modules that were characterized by a widening of muscle activity patterns. This suggests that the nervous system adopts the strategy of prolonging the duration of basic muscle activity patterns to cope with unstable conditions resulting from either slippery ground, reduced support surface, or pathology.

Keywords: central pattern generator; cerebellar ataxia; muscle synergies; slippery surface; unstable conditions.

Fig. 1.

Kinematics gait parameters. A: ensemble-averaged (means ± SD) hip, knee, and ankle joint and trunk roll orientation angles in 20 healthy subjects (control 1 group), 19 cerebellar ataxic patients [International Cooperative Ataxia Rating Scale (ICARS) ≤30], and healthy subjects walking along a slippery walkway and on beam. B: comparison of general gait parameters for ataxic patients and healthy subjects during unstable walking (slippery, beam), relative to respective control groups at matched walking speeds. Data were normalized to the cycle duration and represented in percent of gait cycle (from touchdown to successive touchdown). RoM, range of motion. *P < 0.05, significant differences.

Fig. 2.

Example of EMG traces in one representative control subject h3 (A), 1 CA patient p9 (B), 1 subject on a slippery surface s4 (C), and 1 subject walking on a beam b1 (D) during 2 consecutive strides. The stance phase is evidenced by a shaded region in each case and EMGs were normalized to their max value across all trials.

Fig. 3.

Ensemble averaged (±SD) EMG activity patterns of 12 ipsilateral leg muscles recorded from control' group (A), ataxic patients (B), and healthy subjects during slippery (C) and beam (D) walking. EMGs were normalized to their max value across all trials. EMG data are plotted vs. normalized gait cycle. GM, gluteus medius; TFL, tensor fascia latae; RF, rectus femoris; VL, vastus lateralis; VM, vastus medialis; ST, semitendinosus; BF, biceps femoris; TA, tibialis anterior; PL, peroneus longus; MG, gastrocnemius medialis; LG, gastrocnemius lateralis; SOL, soleus.

Fig. 4.

Full width at half maximum (FWHM) of leg muscle EMGs (means ± SD) for ataxic patients (B) and healthy subjects during slippery (C) and beam (D) walking conditions compared with respective control groups. FWHM was calculated as the duration of the interval (in percent of gait cycle) in which EMG activity exceeded half of its maximum (A). *Significant differences.

Fig. 5.

Statistical analysis of EMG patterns in cerebellar ataxic (CA) using non-negative matrix factorization (NNMF). A: cumulative percent of variance (VAF; ±SD) explained by basic EMG components in ataxic patients (left) and healthy controls (right). The data for the main group of ataxic patients (n = 19, ICARS ≤30) and severe ataxic patients (ICARS >30, n = 4) are shown separately. The VAF value is calculated as 1 − SSE/SST, where SEE (sum of squared errors) is the unexplained variation and SST (total sum of squares) is the total variation of the data. B: number of modules needed to account for cycle-by-cycle variability of muscle activity estimated using the best linear fit method. The majority of healthy subjects and ataxic patients needed 4 modules. C: ensemble-averaged basic temporal patterns (±SD) of ataxic patients and controls with four modules assumed for each group. Basic patterns were plotted in a “chronological” order (with respect to the timing of the main peak). D: corresponding muscle weights. E: FWHM (means ± SD) of the basic temporal patterns. *Significant group differences.

Fig. 6.

Clinical correlations. Significant kinematics electromyographic parameters in ataxic patients (n = 19) as a function of the ICARS total score. Each point represents the mean value for an individual patient. Linear regression lines with corresponding r and p values are reported. B: averaged FWHM of all patterns vs. the ICARS total score.

Fig. 7.

Statistical analysis of EMG patterns during walking on a slippery surface using NNMF. A: cumulative percent of variance (VAF; ±SD) explained by basic EMG components in walking on slippery surface (left) and normal floor (right). B: number of modules needed to account for cycle-by-cycle variability of muscle activity estimated using the best linear fit method. C: ensemble-averaged basic temporal patterns (±SD) during walking on a slippery surface and normal walking with four modules assumed for each group. D: corresponding muscle weights. E: FWHM (means ± SD) of the patterns. *Significant differences. Note significant differences in components P1, P2, and P4.

Fig. 8.

Statistical analysis of EMG patterns during walking on a beam. A: cumulative percent of variance (VAF; ±SD) explained by basic EMG components in walking on a beam (left) and normal floor (right). B: number of modules needed to account for cycle-by-cycle variability of muscle activity estimated using the best linear fit method. C: ensemble-averaged basic temporal patterns (±SD) with 4 modules assumed for each group. D: corresponding muscle weights. E: FWHM (means ± SD) of the patterns. *Significant differences. Note significantly wider basic EMG components during beam walking.

Fig. 9.

Comparison of muscle module structure across different groups. A: group mean weights (synergies). The data for all 3 groups of control subjects were pooled together (avg control). The number of modules for each subject was not constrained to 4 but was selected based on the best linear fit method. Modules were ranked based on their best similarities (see Methods). W1, W2, W4, and W6 (and corresponding basic patterns P) were identified in each group, while W3, W5, and W7 were only identified in some groups. The numbers on the top of each plot represent the mean scalar product of weights of individual subjects with the group mean weight. The numbers on the right represent percent of subjects showing that particular synergy. Note that low values correspond to low structural consistency across subjects, and these synergies were plotted in toned-down colors. B: corresponding basic temporal patterns. Each curve represents the mean (across strides) pattern for an individual subject. Common (across subjects) basic patterns were plotted in a “chronological” order (with respect to the timing of the main peak), whereas inconsistent synergies were plotted separately on the bottom. The numbers on the top represent the mean FWHM. C: center of activity (CoA) of consistent basic components. The CoA vector was calculated as the 1st trigonometric moment of the circular distribution (Batschelet 1981). Polar direction denotes the relative time over the gait cycle (time progresses clockwise), and the width of the sector represents angular SD across subjects. *Significant differences compared with respective control groups.