Alterations in Muscle Networks in the Upper Extremity of Chronic Stroke Survivors

Muscle Synergy Plasticity in Motor Function Recovery After Stroke

In certain neurological disorders such as stroke, the impairment of upper limb function significantly impacts daily life quality and necessitates enhanced neurological control. This poses a formidable challenge in the realm of rehabilitation due to its intricate nature. Moreover, the plasticity of muscle synergy proves advantageous in assessing the enhancement of motor function among stroke patients pre and post rehabilitation training intervention, owing to the modular control strategy of central nervous system. It also facilitates the investigation of long-term alterations in remodeling of muscle functional performance among patients undergoing clinical rehabilitation, aiming to establish correlations between changes in muscle synergies and stroke characteristics such as type, stage, and sites. In this study, a three-week rehabilitation monitoring experiment was conducted to assess the motor function of stroke patients at different stages of rehabilitation based on muscle synergy performance. Additionally, we aimed to investigate the correlation between clinical scale scores, rehabilitation stages, and synergy performance in order to provide a more comprehensive understanding of stroke patient recovery. The results of 7 healthy controls and 16 stroke patients showed that high-functioning patients were superior to low-functioning patients in terms of motor function plasticity towards healthy individuals. Moreover, there was a high positive correlation between muscle synergies and clinical scale scores in high-functioning patients, and the significance gradually emerged with treatment, highlighting the potential of muscle synergy plasticity as a valuable tool for monitoring rehabilitation progress. The potential of this study was also demonstrated for elucidating the physiological mechanisms underlying motor function reconstruction within the central nervous system, which is expected to promote the further application of muscle synergy in clinical assessment.

Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.

3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Expanding the repertoire of intermuscular coordination patterns and modulating intermuscular connectivity in stroke-affected upper extremity through electromyogram-guided training: a pilot study

Abnormal intermuscular coordination is a major stroke-induced functional motor impairment in the upper extremity (UE). Previous studies have computationally identified the abnormalities in the intermuscular coordination in the stroke-affected UE and their negative impacts on motor outputs. Therefore, targeting the aberrant muscle synergies has the potential as an effective approach for stroke rehabilitation. Recently, we verified the modifiability of the naturally expressed muscle synergies of young able-bodied adults in UE through an electromyographic (EMG) signal-guided exercise protocol. This study tested if an EMG-guided exercise will induce new muscle synergies, alter the associated intermuscular connectivity, and improve UE motor outcome in stroke-affected UE with moderate-to-severe motor impairment. The study used the six-week isometric EMG signal-guided exercise protocol that focused on independently activating two specific muscles, the biceps and brachioradialis, to develop new muscle activation groups. The study found that both the stroke and age-matched, able-bodied groups were able to develop new muscle coordination patterns through the exercise while habitual muscle activation was still available, which led to improvements in the motor control of the trained arm. In addition, the results provided preliminary evidence of increased intermuscular connectivity between targeted muscles in the beta-band frequencies for stroke patients after training, suggesting a modulation of the common neural drive. These findings suggest that our isometric exercise protocol has the potential to improve stroke survivors' performance of UE in their activities in daily lives (ADLs) and, ultimately, their quality of life through expanding their repertoire of intermuscular coordination.Clinical Relevance- This study shows the feasibility of expanding the intermuscular coordination pattern in stroke-affected UE through an isometric EMG-guided exercise which positively affects task performance and intermuscular connectivity.

Corticomuscular and intermuscular coherence are correlated after stroke: a simplified motor control?

During movement, corticomuscular coherence is a measure of central-peripheral communication, while intermuscular coherence is a measure of the amount of common central drive to the muscles. Although these two measures are modified in stroke subjects, no author has explored a correlation between them, neither in stroke subjects nor in healthy subjects. Twenty-four chronic stroke subjects and 22 healthy control subjects were included in this cohort study, and they performed 20 active elbow extension movements. The electroencephalographic and electromyographic activity of the elbow flexors and extensors were recorded. Corticomuscular and intermuscular coherence were calculated in the time-frequency domain for each limb of stroke and control subjects. Partial rank correlations were performed to study the link between these two variables. Our results showed a positive correlation between corticomuscular and intermuscular coherence only for stroke subjects, for their paretic and non-paretic limbs (P < 0.022; Rho > 0.50). These results suggest, beyond the cortical and spinal hypotheses to explain them, that stroke subjects present a form of simplification of motor control. When central-peripheral communication increases, it is less modulated and more common to the muscles involved in the active movement. This motor control simplification suggests a new way of understanding the plasticity of the neuromuscular system after stroke.

Changes in intermuscular connectivity during active elbow extension reveal a functional simplification of motor control after stroke

Background

Stroke alters muscle co-activation and notably leads to exaggerated antagonist co-contraction responsible for impaired motor function. However, the mechanisms underlying this exaggerated antagonist co-contraction remain unclear. To fill this gap, the analysis of oscillatory synchronicity in electromyographic signals from synergistic muscles, also called intermuscular coherence, was a relevant tool.

Objective

This study compares functional intermuscular connectivity between muscle pairs of the paretic and non-paretic upper limbs of stroke subjects and the dominant limb of control subjects, concomitantly between two muscle pairs with a different functional role, through an intermuscular coherence analysis.

Methods

Twenty-four chronic stroke subjects and twenty-four healthy control subjects were included. Subjects performed twenty elbow extensions while kinematic data and electromyographic activity of both flexor and extensor elbow muscles were recorded. Intermuscular coherence was analyzed in the beta frequency band compared to the assessment of antagonist co-contraction.

Results

Intermuscular coherence was higher in the stroke subjects’ paretic limbs compared to control subjects. For stroke subjects, the intermuscular coherence of the antagonist-antagonist muscle pair (biceps brachii—brachioradialis) was higher than that of the agonist-antagonist muscle pair (triceps brachii—brachioradialis). For the paretic limb, intermuscular coherence of the antagonist-antagonist muscle pair presented a negative relationship with antagonist co-contraction.

Conclusion

Differences in intermuscular coherence between the paretic limbs of stroke subjects and control subjects suggest a higher common central drive during movement. Furthermore, results highlight the association between stroke-related alteration of intermuscular functional connectivity and the alteration of motor function.

Alterations in motor modules and their contribution to limitations in force control in the upper extremity after stroke

The generation of isometric force at the hand can be mediated by activating a few motor modules. Stroke induces alterations in motor modules underlying steady-state isometric force generation in the human upper extremity (UE). However, how the altered motor modules impact task performance (force production) remains unclear as stroke survivors develop and converge to the three-dimensional (3D) target force. Thus, we tested whether stroke-specific motor modules would be activated from the onset of force generation and also examined how alterations in motor modules would induce changes in force representation. During 3D isometric force development, electromyographic (EMG) signals were recorded from eight major elbow and shoulder muscles in the paretic arm of 10 chronic hemispheric stroke survivors and both arms of six age-matched control participants. A non-negative matrix factorization algorithm identified motor modules in four different time windows: three “exploratory” force ramping phases (Ramps 1–3; 0–33%, 33–67%, and 67–100% of target force magnitude, respectively) and the stable force match phase (Hold). Motor module similarity and between-force coupling were examined by calculating the scalar product and Pearson correlation across the phases. To investigate the association between the end-point force representation and the activation of the motor modules, principal component analysis (PCA) and multivariate multiple linear regression analyses were applied. In addition, the force components regressed on the activation profiles of motor modules were utilized to model the feasible force direction. Both stroke and control groups developed exploratory isometric forces with a non-linear relationship between EMG and force. During the force matching, only the stroke group showed abnormal between-force coupling in medial-lateral and backward-forward and medial-lateral and downward-upward directions. In each group, the same motor modules, including the abnormal deltoid module in stroke survivors, were expressed from the beginning of force development instead of emerging during the force exploration. The PCA and the multivariate multiple linear regression analyses showed that alterations in motor modules were associated with abnormal between-force coupling and limited feasible force direction after stroke. Overall, these results suggest that alterations in intermuscular coordination contribute to the abnormal end-point force control under isometric conditions in the UE after stroke.

Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks

Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.

Myoelectric interface training enables targeted reduction in abnormal muscle co-activation

BACKGROUND

Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training.

METHODS

We used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy.

RESULTS

There was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training-that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training.

CONCLUSIONS

These findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do-enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1 ).

Synergistic Upper-limb Functional Muscle Connectivity using Acoustic Mechanomyography

Functional connectivity is a critical concept in describing synergistic muscle synchronization for the execution of complex motor tasks. Muscle synchronization is typically derived from the decomposition of intermuscular coherence (IMC) at different frequency bands through electromyography (EMG) signal analysis with limited out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the coordination and functional connectivity of muscles based on mechanomyography (MMG), focused on a targeted group of muscles that are typically active in the conduction of activities of daily living using the upper limb. In this regard, functional muscle networks are evaluated in this paper for ten able-bodied participants and three amputees. MMG activity was acquired from a custom-made wearable MMG armband placed over four superficial muscles around the forearm (i.e., flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. The results of connectivity analysis at multiple frequency bands showed significant topographical differences across gestures for low (< 5Hz) and high (> 12 Hz) frequencies and observable differences between able-bodied and amputee subjects. These findings show evidence that MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic synchronization of upper-limb muscles in complex upper-limb tasks. The new physiological modality further provides key insights into the neural circuitry of motor coordination and offers the concomitant outcomes of demonstrating the feasibility of MMG to map muscle coherence from a neurophysiological perspective as well as providing the mechanistic basis for its translation into human-robot interfaces.

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.