Muscle synergy patterns as physiological markers of motor cortical damage

Abstract

The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination-including preservation, merging, and fractionation of muscle synergies-that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

A schematic illustrating how muscle synergies are linearly combined to generate muscle patterns recorded as electromyographic signals (EMGs). Each of the two muscle synergies shown (red and green bars) is represented as an activation balance profile across muscles (muscles 1–5) and activated, through multiplication, by a time-dependent coefficient. The EMG waveforms resulting from the activations of individual synergies are then summed together to reconstruct the recorded EMGs (black lines). In the schematic, each color in the EMG reconstruction reflects how the waveforms from the synergy coded by the same color contribute to the reconstruction.

Fig. 2.

Preservation of muscle synergies in a mildly impaired patient with stroke but not in a severely impaired patient. (A) The muscle synergies extracted from the EMGs of the stroke-affected (red) and unaffected (blue) arms of a mildly impaired patient with stroke with a Fugl–Meyer (FM) score of 50. For both arms, seven muscle synergies were sufficient for an EMG reconstruction R2 of ∼80%. Each affected-arm synergy was matched to an unaffected-arm synergy giving the highest scalar-product similarity value, shown in between each synergy pair. All seven pairs had high similarity values (0.89–0.98), suggesting preservation of the normal muscle synergies in the stroke-affected arm of this patient. (B) The muscle synergies of a severely impaired stroke patient with FM score of 29. For this subject only four synergies were needed for reconstructing the affected-arm EMGs (synergies A1–A4) whereas six were needed for the unaffected arm (synergies U1–U6). The similarity values between the synergies of the two arms were lower (0.67–0.85), but the affected-arm synergies tended to have activation components in more muscles than the unaffected-arm synergies (e.g., compare A3 with U3 and A4 with U4). This observation suggests that the apparent dissimilarity between the synergies of the two arms may be due to merging of the unaffected-arm synergies in the stroke-affected arm. Infrasp, infraspinatus; RhombMaj, rhomboid major; TrapSup, trapezius superior; PectClav, clavicular head of pectoralis major; DeltA, anterior part of deltoid; DeltM, medial part of deltoid; DeltP, posterior part of deltoid; TrLat, lateral head of triceps brachii; TrMed, medial head of triceps brachii; BicLong, long head of biceps brachii; Brac, brachialis; BrRad, brachioradialis; Supin, supinator; PronTer, pronator teres.

Fig. 3.

The stroke-affected arm muscle synergies of severely impaired patients explained as merging of unaffected-arm synergies. (A) We devised a computational procedure that systematically finds the subset of unaffected-arm synergies that can be linearly combined to reconstruct each affected-arm muscle synergy. Each affected-arm synergy from the example shown in Fig. 2B (red, A1–A4) was reconstructed by merging two to three unaffected-arm synergies (blue, U1–U6). Synergy A1, for instance, could be reconstructed by combining U1 and U5; synergy A2, by combining U2 and U5; and so on. The reconstructed affected-arm synergies (pink), when compared with the synergies extracted from the affected-arm EMGs (red), yielded high similarity values (0.89–0.95, shown between each extracted and reconstructed synergy pair for the affected arm). (B) We derived an index quantifying the degree of synergy merging by calculating the mean number of unaffected-arm synergies found merging into each affected-arm synergy. When calculating this average, we included in our statistic only those affected-arm synergies that could be well reconstructed as a combination of unaffected-arm synergies (scalar product >0.75). Across all patients (n = 31), we found a significant, negative correlation between our index of synergy merging (in log scale) and the FM score (r = −0.512, P = 0.0032). Thus, the more impaired the patient with stroke, the more likely that an affected-arm synergy represents a merging of multiple unaffected-arm synergies.

Fig. 4.

The stroke-affected arm muscle synergies of chronic patients explained as fractionations of unaffected-arm synergies. (A) Some of the affected-arm synergies from the chronic patients in our dataset could neither be directly matched to an unaffected-arm synergy nor be explained as a merging of multiple unaffected-arm synergies. In fact, the median scalar-product similarity value between the extracted and reconstructed (via the merging model) affected-arm synergies decreased with the subjects’ poststroke duration (in log scale) (r = −0.610, P = 2.7 × 10−4). (B) The muscle synergies of a very chronic patient (poststroke duration = 450.7 mo) extracted from the affected-arm EMGs (red, A1–A6) and their corresponding reconstructions by merging unaffected-arm synergies (pink). Of these six synergies, only one could be well reconstructed (A1). (C) The affected-arm synergies that could not be well reconstructed by the merging model in B (A2–A6) could instead be explained as fractionations of unaffected-arm synergies. Our computational procedure identified synergies A2 and A3 to be fractionations of synergy U1 for the unaffected arm, and A4–A6 as fractionations of synergy U3. The affected-arm synergies identified as fractionations (red) could in fact be linearly combined to result in reconstructions of their corresponding unaffected-arm synergies (cyan) that matched very well to the synergies extracted from the unaffected-arm EMGs (blue) (scalar product = 0.93–0.94). (D) Within the group of subjects in whose affected-arm synergies fractionations were observed (n = 18), the percentage of affected-arm synergies identified to be fractionations of unaffected-arm synergies increased with the subjects’ poststroke duration (in log scale) (r = 0.66, P = 0.0031).