Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke

Global synchronization of functional corticomuscular coupling under precise grip tasks using multichannel EEG and EMG signals

Functional corticomuscular coupling (FCMC), a phenomenon describing the information interaction between the cortex and muscles, plays an important role in assessing hand movements. However, related studies mainly focused on specific actions by one-to-one mapping between the brain and muscles, ignoring the global synchronization across the motor system. Little research has been done on the FCMC difference between the brain and different muscle groups in terms of precise grip tasks. This study combined the maximum information coefficient (MIC) and the S estimation method and constructed a multivariate global synchronization index (MGSI) to measure the FCMC by analyzing the multichannel electroencephalogram (EEG) and electromyogram (EMG) during precise grip tasks. Both signals were collected from 12 healthy subjects while performing different weight object tasks. Our results on Hilbert-Huang spectral entropy (HHSE) of signals showed differences in task stages in both β (13-30 Hz) and γ (31-45 Hz) bands. The weight difference was reflected in the HHSE of channel CP5 and muscles at both ends of the upper limb. The one-to-one mapping with MIC between EEG and the muscle pair AD-FDI showed larger MIC values than the muscle pair B-CED; the same trend was seen on the MGSI values. However, the difference in weight of static tasks was not significant. Both MGSI values and the connect ratio of EEG were related to HHSE values. This work investigated the changes in the cortex and muscles during precise grip tasks from different perspectives, contributing to a better understanding of human motor control.

Neuromuscular conditions in post-stroke ankle-foot dysfunction reflected by surface electromyography

BACKGROUND

Rating scales and linear indices of surface electromyography (sEMG) cannot quantify all neuromuscular conditions associated with ankle-foot dysfunction in hemiplegic patients. This study aimed to reveal potential neuromuscular conditions of ankle-foot dysfunction in hemiplegic patients by nonlinear network indices of sEMG.

METHODS

Fourteen male patients with hemiplegia and 10 age- and sex-matched healthy male adults were recruited and tested in static standing position. The characteristics of the root mean square (RMS), median frequency (MF), and three nonlinear indices, the clustering coefficient (C), the average shortest path length (L), and the degree centrality (DC), of eight groups of muscles in bilateral calves were observed.

RESULTS

Compared to those of the control group, the RMS of the medial gastrocnemius (MG), flexor digitorum longus (FDL), and extensor digitorum longus (EDL) on the affected side were significantly lower (P < 0.05), and the RMS of the tibial anterior (TA) and EDL on the unaffected side were significantly higher (P < 0.05). The MF of the EDL on the affected side was significantly higher than that on the control side (P < 0.05). The C of the unaffected side was significantly higher than that of the control group, whereas the L was lower (P < 0.05). Compared to those of the control group, the DC of the TA, EDL, and soleus (SOL) on the unaffected sides were higher (P < 0.05), and the DC of the MG on the affected sides was lower (P < 0.05).

CONCLUSION

The change trends and clinical significance of these three network indices, including C, L, and DC, are not in line with those of the traditional linear indices, the RMS and the MF. The C and L may reflect the degree of synchronous activation of muscles during a certain motor task. The DC might be able to quantitatively assess the degree of muscle involvement and reflect the degree of involvement of a single muscle. Linear and nonlinear indices may reveal more neuromuscular conditions in hemiplegic ankle-foot dysfunction from different aspects.

TRIAL REGISTRATION

ChiCTR2100055090.

Top-Down and Bottom-Up Mechanisms of Motor Recovery Poststroke

Stroke remains a leading cause of disability. Motor recovery requires the interaction of top-down and bottom-up mechanisms, which reinforce each other. Injury to the brain initiates a biphasic neuroimmune process, which opens a window for spontaneous recovery during which the brain is particularly sensitive to activity. Physical activity during this sensitive period can lead to rapid recovery by potentiating anti-inflammatory and neuroplastic processes. On the other hand, lack of physical activity can lead to early closure of the sensitive period and downstream changes in muscles, such as sarcopenia, muscle stiffness, and reduced cardiovascular capacity, and blood flow that impede recovery.

Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue

Characterization of fatigue using surface electromyography (sEMG) data has been motivated for rehabilitation and injury-preventative technologies. Current sEMG-based models of fatigue are limited due to (a) linear and parametric assumptions, (b) lack of a holistic neurophysiological view, and (c) complex and heterogeneous responses. This paper proposes and validates a data-driven non-parametric functional muscle network analysis to reliably characterize fatigue-related changes in synergistic muscle coordination and distribution of neural drive at the peripheral level. The proposed approach was tested on data collected in this study from the lower extremities of 26 asymptomatic volunteers (13 subjects were assigned to the fatigue intervention group, and 13 age/gender-matched subjects were assigned to the control group). Volitional fatigue was induced in the intervention group by moderate-intensity unilateral leg press exercises. The proposed non-parametric functional muscle network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. For the first time, this paper proposed a non-parametric functional muscle network and highlighted the corresponding potential as a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

Objective

Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects.

Method

Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated.

Results

The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures.

Conclusion and Significance

This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.

A Unified Model for Stroke Recovery and Rehabilitation: Why Now?

ABSTRACT

The current model of stroke care delivery in the United States and in many parts of the world is fragmented, resulting in lack of continuity of care, inability to track recovery meaningfully across the continuum, and lack of access to the frequency, intensity, and duration of high-quality rehabilitation necessary to optimally harness recovery processes. The process of recovery itself has been overshadowed by a focus on length of stay and the movement of patients across levels of care. Here, we describe the rationale behind the recent efforts at the Johns Hopkins Sheikh Khalifa Stroke Institute to define and coordinate an intensive, strategic effort to develop effective stroke systems of care across the continuum through the development of a unified Sheikh Khalifa Stroke Institute model of recovery and rehabilitation.

Linear versus Nonlinear Muscle Networks: A Case Study to Decode Hidden Synergistic Patterns During Dynamic Lower-limb Tasks

This paper, for the first time, compares the behaviors of nonlinear versus linear muscle networks in decoding hidden peripheral synergistic neural patterns during dynamic functional tasks. In this paper, we report a case study during which one healthy subject conducts a series of four lower limb repetitive tasks. Specifically, the paper focuses on tasks that involve the right knee joint, including walking, sit-tostand, stepping, and drop-jump. Twelve muscles were recorded using the Delsys Trigno system. The linear muscle network was generated using coherence analysis, and the nonlinear network was generated using Spearman's correlation. The results show that the degree, clustering coefficient, and global efficiency of the muscle network have the highest value among tasks in the linear domain for the walking task, while a low linear synergistic network behavior for the sit-to-stand is observed. On the other hand, the results show that the nonlinear functional muscle network decodes high connectivity (degree) and clustering coefficient and efficiency for the sit-tostand when compared with other tasks. We have also developed a two-dimensional functional connectivity plane composed of linear and nonlinear features and shown that it can span the lower-limb dynamic task space. The results of this paper for the first time highlight the importance of observing both linear and nonlinear connectivity patterns, especially for complex dynamic tasks. It should also be noted that through a simultaneous EEG recording (using BrainVision System), we have shown that, indeed, cortical activity may indirectly explain highly-connected nonlinear muscle network for the sit-to-stand task, highlighting the importance of nonlinear muscle network as a neurophysiological window of observation beyond the periphery.