Plasticity and modular control of locomotor patterns in neurological disorders with motor deficits

Abstract

Human locomotor movements exhibit considerable variability and are highly complex in terms of both neural activation and biomechanical output. The building blocks with which the central nervous system constructs these motor patterns can be preserved in patients with various sensory-motor disorders. In particular, several studies highlighted a modular burst-like organization of the muscle activity. Here we review and discuss this issue with a particular emphasis on the various examples of adaptation of locomotor patterns in patients (with large fiber neuropathy, amputees, stroke and spinal cord injury). The results highlight plasticity and different solutions to reorganize muscle patterns in both peripheral and central nervous system lesions. The findings are discussed in a general context of compensatory gait mechanisms, spatiotemporal architecture and modularity of the locomotor program.

Keywords: EMG activity; compensation; locomotor pattern generation; modularity; plantarflexor weakness; spinal cord injury; stroke.

Figure 1

Motor patterns in healthy volunteers. (A) Ensemble-averaged EMGs (n = 8 subjects) recorded from 10 ipsilateral leg muscles during walking on a treadmill at 5, 7, and 9 km/h. At 9 km/h, there is an “atypical” burst of activity in several thigh muscles that is synchronous with the peak activity in the calf muscles [the data are illustrated from Ivanenko et al. (2008)]. ST, semitendinosus; BF, biceps femoris; TFL, tensor fascia latae; SART, Sartorius; Gmed, RF, rectus femoris; VM, vastus medialis; VL, vastus lateralis; MG, gastrocnemius medialis; LG, gastrocnemius lateralis; SOL, soleus; TA, tibialis anterior. (B) ensemble-averaged (±SD) ankle, knee and hip moments of force (normalized to the subject's weight) of the right leg during overground walking at about the same speeds (as in panel A) in one representative healthy subject.

Figure 2

Ensemble-averaged bilateral EMG activity of leg muscles during overground walking at slow and fast speeds in four patients (panels A–D) with unilateral sciatic nerve compression. Note an “atypical” activation of proximal extensors during late stance (marked in red) and its variability across patients and depending on the affected side.

Figure 3

Motor patterns in a patient with sciatic nerve compression [the same patient as in Figure 2A but recorded 1 year later (session 2)]. (A) ensemble-averaged (across 12 consecutive steps) joint angular displacements (left panel, mean ± SD) and amplitudes of angular joint motion (right panel) during walking at 3 km/h. Asterisks denote significant differences. Note significantly smaller distal joint oscillations on the affected (left) side. (B) ensemble-averaged (n = 5 steps) vertical (F_z) and horizontal anterior-posterior (F_x) ground reaction forces, and ankle, knee and hip joint moments of force normalized to the patient's weight during overground walking at ~5 and 7 km/h in session 2 (left panels). The patterns are plotted vs. normalized stance. On the right—peak-to-peak amplitudes. (C) Ensemble-averaged bilateral EMG activity of leg muscles during walking on a treadmill at different speeds. In session 1 (Figure 2A), the patient could walk only at speeds up to 5 km/h due to plantarflexor weakness, while in session 2 we recorded walking in a wide range of speeds (3–9 km/h). Adapted from Ivanenko et al. (2013a). Note a prominent burst of activity (marked in red) in the proximal extensors during late stance on the affected (left) side at low speeds in the session 1 (Figure 2A) and only at higher speeds (>5 km/h) in session 2. Furthermore, at 9 km/h the “atypical” burst of activity was present in both legs, as in healthy subjects (see Figure 1A).

Figure 4

Ensemble-averaged EMG activity of leg muscles during walking at different speeds in the control group (upper panels) and seven amputee subjects. Adapted from Huang and Ferris (2012) with permission of the authors. Vertical lines show average toe-off events for the fastest and slowest walking speeds.

Figure 5

Surface electromyogram (means and SD), motor modules (bottom, left), and activation signals (bottom, right) for a representative healthy control subject (A) and for the unaffected (B) and affected side of a stroke patient. (C) Adapted from Gizzi et al. (2011).

Figure 6

Motor patterns in SCI patients. (A) An example of weight-bearing stepping in a clinically complete (at 0.22 m/s, left panel) and incomplete (at 0.89 m/s, right panel) SCI individuals [modified from Beres-Jones and Harkema (2004) and Maegele et al. (2002) with permission of the authors]. The stance phase in the right panel is evidenced by the elevation in the ground reaction force trace and indicated by the shaded region. MH, medial hamstring; load, vertical ground reaction force. (B) Ensemble-averaged (across 5 strides) EMG patterns in the SCI-C patient during walking at a natural speed (~3.1 km/h). Note variable and weaker muscle activity on the most affected side (marked in red). (C) Examples of spatiotemporal patterns of α-motorneuron activity in the lumbosacral enlargement in controls and three SCI-C patients during walking on a treadmill at 1 km/h. Output pattern for each segment was reconstructed by mapping the recorded EMG waveforms (normalized method, see Ivanenko et al., 2006) onto the known charts of segmental localization. White vertical lines denote stance-to-swing transition time. (D) Time course of the temporal components in controls and patients for stepping at 2 km/h, 0–75% body weight support. The components extracted by factor analysis from individual subjects. Right panel illustrates weighting coefficients of the temporal components in individual activity patterns of 12 muscles for all groups of subjects in a color coded scale. Adapted from Ivanenko et al. (2003). Note similar basic EMG components in controls and patients as opposed to quite different EMG patterns and weighting coefficients.