Effect of muscle fatigue on the cortical-muscle network: A combined electroencephalogram and electromyogram study

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

BACKGROUND AND OBJECTIVE

Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue).

METHODS

A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications).

RESULTS

The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients.

CONCLUSION

The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.

Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker

Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.

Predicting individual muscle fatigue tolerance by resting-state EEG brain network

OBJECTIVE

Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored.

APPROACH

Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance.

MAIN RESULTS

Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network.

SIGNIFICANCE

Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis

OBJECTIVE

A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action.

APPROACH

In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions and divide the movement duration into different stages by the time-varying corticomuscular network patterns.

MAIN RESULTS

The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally, the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved.

SIGNIFICANCE

The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

A framework for quantifying the effects of transcranial magnetic stimulation on motor recovery from hemiparesis: Corticomuscular Network

OBJECTIVE

Transcranial magnetic stimulation (TMS) is an experimental therapy for promoting motor recovery from hemiparesis. At present, hemiparesis patients' responses to TMS are variable. To maximize its therapeutic potential, we need an approach that relates the electrophysiology of motor recovery and TMS. To this end, we propose Corticomuscular Network (CMN) representing the holistic motor system, including the cortico-cortical pathway, corticospinal tract, and muscle co-activation.

METHODS

CMN is made up of coherence between pairs of electrode signals and spatial locations of the electrodes. We associated coherence and graph features of CMN with Fugl-Meyer Assessment (FMA) for the upper extremity. Besides, we compared CMN between 8 patients with hemiparesis and 6 healthy controls and contrasted CMN of patients before and after a 1Hz TMS.

MAIN RESULTS

Corticomuscular coherence (CMC) correlated positively with FMA. The regression model between FMA and CMC between 5 pairs of channels had 0.99 adjusted R^2 and a p-value less than 0.01. Compared to healthy controls, CMN of patients tended to be a small-world network and was more interconnected with higher CMC. CMC between cortex and triceps brachii long head was higher in patients. 15-minute 1Hz TMS protocol induced coherence changes beyond the stimulation side and had a limited impact on CMN parameters that are related to motor recovery.

SIGNIFICANCE

CMN is a potential clinical approach to quantify rehabilitating progress. It also sheds light on the desirable electrophysiological effects of TMS based on which rehabilitating strategies can be optimized.

Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task

The present study examined the effects of transcranial direct current stimulation (tDCS) using Halo Sport on the time to exhaustion (TTE) in relation with muscle activities and corticomuscular coupling of agonist and antagonist muscles during a sustained isometric fatiguing contraction performed with the elbow flexors. Twenty healthy male college students were randomly assigned to tDCS group and control group. The two group participants performed two experimental sessions which consisted of pre-fatigue isometric maximal voluntary contraction (MVC), sustained submaximal voluntary contractions (30% maximal torque) performed to exhaustion, and post-fatigue MVC with the right elbow flexor muscles. Sham stimulation (90 s) and tDCS (20 min) were applied for control and tDCS group participants 20 min prior to the second session test, respectively. MVC strength in pre- and post-fatigue test, TTE, electroencephalogram (EEG), and electromyography (EMG) of biceps brachii (BB) and triceps brachii (TB) were recorded during the tests. It was found that tDCS using the Halo Sport device significantly increased TTE and thus improved muscular endurance performance. The improvement may be partly related to the improvement of neuromuscular efficiency as reflected by decrease of antagonistic muscle coactivation activities, which may be related to cortical originated central processing mechanism of neuromuscular activities.