High-density surface electromyography improves the identification of oscillatory synaptic inputs to motoneurons

MR elastography-based slip interface imaging (SII) for functional assessment of myofascial interfaces: A feasibility study

PURPOSE

Abnormal adherence at functional myofascial interfaces is hypothesized as an important phenomenon in myofascial pain syndrome. This study aimed to investigate the feasibility of MR elastography (MRE)-based slip interface imaging (SII) to visualize and assess myofascial mobility in healthy volunteers.

METHODS

SII was used to assess local shear strain at functional myofascial interfaces in the flexor digitorum profundus (FDP) and thighs. In the FDP, MRE was performed at 90 Hz vibration to each index, middle, ring, and little finger. Two thigh MRE scans were performed at 40 Hz with knees flexed and extended. The normalized octahedral shear strain (NOSS) maps were calculated to visualize myofascial slip interfaces. The entropy of the probability distribution of the gradient NOSS was computed for the two knee positions at the intermuscular interface between vastus lateralis and vastus intermedius, around rectus femoris, and between vastus intermedius and vastus medialis.

RESULTS

NOSS map depicted distinct functional slip interfaces in the FDP for each finger. Compared to knee flexion, clearer slip interfaces and larger gradient NOSS entropy at the vastus lateralis-vastus intermedius interface were observed during knee extension, where the quadriceps are not passively stretched. This suggests the optimal position for using SII to visualize myofascial slip interface in skeletal muscles is when muscles are not subjected to any additional force.

CONCLUSION

The study demonstrated that MRE-based SII can visualize and assess myofascial interface mobility in extremities. The results provide a foundation for investigating the hypothesis that myofascial pain syndrome is characterized by changes in the mobility of myofascial interfaces.

Temporally stable beta sensorimotor oscillations and cortico-muscular coupling underlie force steadiness

As humans, we seamlessly hold objects in our hands, and may even lose consciousness of these objects. This phenomenon raises the unsettled question of the involvement of the cerebral cortex, the core area for voluntary motor control, in dynamically maintaining steady muscle force. To address this issue, we measured magnetoencephalographic brain activity from healthy adults who maintained a steady pinch grip. Using a novel analysis approach, we uncovered fine-grained temporal modulations in the beta sensorimotor brain rhythm and its coupling with muscle activity, with respect to several aspects of muscle force (rate of increase/decrease or plateauing high/low). These modulations preceded changes in force features by ∼40 ms and possessed behavioral relevance, as less salient or absent modulation predicted a more stable force output. These findings have consequences for the existing theories regarding the functional role of cortico-muscular coupling, and suggest that steady muscle contractions are characterized by a stable rather than fluttering involvement of the sensorimotor cortex.

Design and validation of a wireless Body Sensor Network for integrated EEG and HD-sEMG acquisitions

Sensorimotor integration is the process through which the human brain plans the motor program execution according to external sources. Within this context, corticomuscular and corticokinematic coherence analyses are common methods to investigate the mechanism underlying the central control of muscle activation. This requires the synchronous acquisition of several physiological signals, including EEG and sEMG. Nevertheless, physical constraints of the current, mostly wired, technologies limit their application in dynamic and naturalistic contexts. In fact, although many efforts were made in the development of biomedical instrumentation for EEG and HD-sEMG signal acquisition, the need for an integrated wireless system is emerging. We hereby describe the design and validation of a new fully wireless body sensor network for the integrated acquisition of EEG and HD-sEMG signals. This Body Sensor Network is composed of wireless bio-signal acquisition modules, named sensor units, and a set of synchronization modules used as a general-purpose system for time-locked recordings. The system was characterized in terms of accuracy of the synchronization and quality of the collected signals. An in-depth characterization of the entire system and an end-to-end comparison of the wireless EEG sensor unit with a wired benchmark EEG device were performed. The proposed device represents an advancement of the State-of-the-Art technology allowing the integrated acquisition of EEG and HD-sEMG signals for the study of sensorimotor integration.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

A Modular, Smart, and Wearable System for High Density sEMG Detection

OBJECTIVE

The use of linear or bi-dimensional electrode arrays for surface EMG detection (HD-sEMG) is gaining attention as it increases the amount and reliability of information extracted from the surface EMG. However, the complexity of the setup and the encumbrance of HD-sEMG hardware currently limits its use in dynamic conditions. The aim of this paper was to develop a miniaturized, wireless, and modular HD-sEMG acquisition system for applications requiring high portability and robustness to movement artifacts.

METHODS

A system with modular architecture was designed. Its core is a miniaturized 32-channel amplifier (Sensor Unit - SU) sampling at 2048 sps/ch with 16 bit resolution and wirelessly transmitting data to a PC or a mobile device. Each SU is a node of a Body Sensor Network for the synchronous signal acquisition from different muscles.

RESULTS

A prototype with two SUs was developed and tested. Each SU is small (3.4 cm × 3 cm × 1.5 cm), light (16.7 g), and can be connected directly to the electrodes; thus, avoiding the need for customary, wired setup. It allows to detect HD-sEMG signals with an average noise of 1.8 μV_RMS and high performance in terms of rejection of power-line interference and motion artefacts. Tests performed on two SUs showed no data loss in a 22 m range and a ±500 μs maximum synchronization delay.

CONCLUSIONS

Data collected in a wide spectrum of experimental conditions confirmed the functionality of the designed architecture and the quality of the acquired signals.

SIGNIFICANCE

By simplifying the experimental setup, reducing the hardware encumbrance, and improving signal quality during dynamic contractions, the developed system opens new perspectives in the use of HD-sEMG in applied and clinical settings.

Functional connectivity analysis of multiplex muscle network across frequencies

Physiological networks reveal information about the interaction between subsystems of the human body. Here we investigated the interaction between the central nervous system and the musculoskeletal system by mapping functional muscle networks. Muscle networks were extracted using coherence analysis of muscle activity assessed using surface electromyography (EMG). Surface EMG was acquired from 36 muscles distributed throughout the body while participants were standing upright and performing a bimanual pointing task. Non-negative matrix factorization revealed functional connectivity in four frequency bands. The spatial arrangement differed considerably across frequencies supporting a multiplex network organisation. Graph-theory analysis of layer-specific network revealed a consistent fat-tail distribution of the edges weights, distinct efficiency values, and core-periphery properties. These frequency bands may be spectral fingerprints of different neural pathways that innervate the spinal motor neurons to control the musculoskeletal system.

Nonlinear Frequency-Domain Analysis of the Transformation of Cortical Inputs by a Motoneuron Pool-Muscle Complex

Corticomotor coherence in the beta and/or gamma bands has been described in different motor tasks, but the role of descending brain oscillations on force control has been elusive. Large-scale computational models of a motoneuron pool and the muscle it innervates have been used as tools to advance the knowledge of how neural elements may influence force control. Here, we present a frequency domain analysis of a NARX model fitted to a large-scale neuromuscular model by the means of generalized frequency response functions (GFRF). The results of such procedures indicated that the computational neuromuscular model was capable of transforming an oscillatory synaptic input (e.g., at 20 Hz) into a constant mean muscle force output. The nonlinearity uncovered by the GFRFs of the NARX model was responsible for the demodulation of an oscillatory input (e.g., a beta band oscillation coming from the brain and forming the input to the motoneuron pool). This suggests a manner by which brain rhythms descending as command signals to the spinal cord and acting on a motoneuron pool can regulate a maintained muscle force. In addition to the scientific aspects of these results, they provide new interpretations that may further neural engineering applications associated with quantitative neurological diagnoses and robotic systems for artificial limbs.

Age and Sex Effects on the Active Stiffness of Vastus Intermedius under Isometric Contraction

Previously, a novel technique was proposed to quantify the relationship between the muscle stiffness and its nonfatigue contraction intensity. The method extended the measured range of isometric contraction to 100% maximum voluntary contraction (MVC) using an ultrasonic shear wave measurement setup. Yet, it has not been revealed how this relationship could be affected by factors like age or sex. To clarify these questions, vastus intermedius (VI) stiffness of 40 healthy subjects was assessed under 11 step levels of isometric contraction. The subjects were divided into four groups: young males, young females, elderly males, and elderly females (n = 10 for each). In a relaxed state, no significant difference was observed between the male and female subjects (p = 0.156) nor between the young and elderly subjects (p = 0.221). However, when performing isometric contraction, the VI stiffness of males was found to be significantly higher than that of females at the same level (p < 0.001), and that of the young was higher than the elderly (p < 0.001). Meanwhile, for two knee joint angles used, the stiffness measured at a 90° knee joint angle was always significantly larger than that measured at 60° (p < 0.001). Recognizing the active muscle stiffness of VI contributes to body stability, and these results may provide insight into the age and sex bias in musculoskeletal studies, such as those on fall risks.

Functional connectivity in the neuromuscular system underlying bimanual coordination

Neural synchrony has been suggested as a mechanism for integrating distributed sensorimotor systems involved in coordinated movement. To test the role of corticomuscular and intermuscular coherence in bimanual coordination, we experimentally manipulated the degree of coordination between hand muscles by varying the sensitivity of the visual feedback to differences in bilateral force. In 16 healthy participants, cortical activity was measured using EEG and muscle activity of the flexor pollicis brevis of both hands using high-density electromyography (HDsEMG). Using the uncontrolled manifold framework, coordination between bilateral forces was quantified by the synergy index R_V in the time and frequency domain. Functional connectivity was assessed using corticomuscular coherence between muscle activity and cortical source activity and intermuscular coherence between bilateral EMG activity. The synergy index increased in the high coordination condition. R_V was higher in the high coordination condition in frequencies between 0 and 0.5 Hz; for the 0.5- to 2-Hz frequency band, this pattern was inverted. Corticomuscular coherence in the beta band (16-30 Hz) was maximal in the contralateral motor cortex and was reduced in the high coordination condition. In contrast, intermuscular coherence was observed at 5-12 Hz and increased with bimanual coordination. Within-subject comparisons revealed a negative correlation between R_V and corticomuscular coherence and a positive correlation between R_V and intermuscular coherence. Our findings suggest two distinct neural pathways: 1) corticomuscular coherence reflects direct corticospinal projections involved in controlling individual muscles; and 2) intermuscular coherence reflects diverging pathways involved in the coordination of multiple muscles.

Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography

Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic "macroscopic" monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91-95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19-27% stronger for HD-sEMG than for "macroscopic" monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.