Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Abstract

Functional connectivity is a critical concept in describing synergistic muscle synchronization for the execution of complex motor tasks. Muscle synchronization is typically derived from decomposition of intermuscular coherence (IMC) at different frequency bands through electromyography (EMG) signal analysis, which potentially limits 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). We focus on a targeted group of muscles vital for activities of daily living (ADLs) in the upper-limb. Functional muscle networks are evaluated for ten able-bodied participants and three upper-limb amputees. Muscle activity was acquired from a custom-made wearable armband of MMG sensors placed over four superficial muscles around the forearm (flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. Muscle connectivity analysis at multiple frequency bands shows significant topographical differences across gestures for low ( < 5Hz) and high (> 12 Hz) muscle activation frequencies as well as observable differences in coherence between amputee and non-amputee subjects. Results demonstrate MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic synchronization of upper-limb muscles in complex movement tasks. The new physiological modality provides key insights into neural circuitry of motor coordination. Findings further offer the concomitant outcomes of demonstrating feasibility of MMG to map muscle coherence from a neurophysiological perspective and providing a mechanistic basis for its translation in humanrobot interface.

3D muscle networks based on vibrational mechanomyography

“…IMC based on MMG is a new area of study initially proposed by the authors. In [21] we found coupling strengths as high as 0.55 by the use of acoustic MMG. In line with these previous findings, results here show similar values of coupling strengths for vMMG.…”

“…Information was recorded while participants performed different hand gesture tasks. The experimental protocol followed the one described in [21]. For this study, gestures and target muscles were maintained.…”

“…The higher the value of IMC, the more coupling of the muscles in the frequency domain. IMC was extracted between each muscle pair following the calculation described in [21] for the computation of modified magnitude squared coherence. In order to consider the entire spectral space of the samples and to avoid the presence of spurious coherence caused by edge effects, each 3 s trial of data was zero-padded at both ends by a number of samples equal to half the samples needed to account for 1024 segments for the calculation of coherence.…”

“…In this way, NNMF was applied to all coherence matrices c x m corresponding to each muscle pair, gesture, and direction. A multiplicative update rule was used to minimize the objective function and constraint the scope of each component to that of the frequency bands, as described in [21]. IMC of all muscle pairs was decomposed over the intervals specified above into non-negative factors using an alternating least squares algorithm, thus resulting in two non-negative matrices reflecting the spectral signatures (basis vectors) and the corresponding coupling strengths between all muscle pairs for each gesture and each direction.…”

“…out of laboratory/clinic). Muscle network analysis by the use of acoustic MMG (aMMG) was previously introduced by the authors in [21] for the evaluation of functional intermuscular connectivity alterations linked to upper-limb loss and differences in muscle network topologies derived from different hand gesture tasks. The results showed evidence that the spectral features of the aMMG signal can be used to construct muscle connectivity network modelS reflecting the common spectral patterns shared by upper-limb muscle groups when performing different motor tasks.…”