High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study

Abstract

Objective: 
Studying motor units (MUs) is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body’s properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored.
Approach:
In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy.
Main results:
It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units.
Significance:
The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

Magnetomyography: A novel modality for non-invasive muscle sensing

“…We have demonstrated the similarities of MMG to sEMG and highlighted some differences which may allow for better localization using MMG compared to sEMG. Though advantages in localization have been shown in models (Arekhloo et al, 2022; Klotz et al, 2022, 2023), there has yet to be empirical evidence. Magnetic sensing of the brain (magnetoencephalography (MEG)) has been demonstrated to provide higher localization accuracy compared to electrical sensing (electroencephalography (EEG)) in both a phantom as well as an implanted dipole in humans (Cohen et al, 1990; Cohen & Cuffin, 1991; Leahy et al, 1998), but the results cannot be extended to muscle sensing.…”

“…Several studies have demonstrated robust recordings of MMG and have applied analyses including stimulus triggered averages of muscle activity (Broser et al, 2018), decoding of individual motor units (Broser et al, 2023), and even discrimination between movements that rival results obtained with sEMG (Greco et al, 2023). Models and simulations have also shown MMG has higher specificity and better localization compared to sEMG, furthering excitement for the field (Arekhloo et al, 2022; Klotz et al, 2023).…”

Voluntary co-contraction of ankle muscles alters motor unit discharge characteristics and reduces estimates of persistent inward currents

“…The majority of evidence on the inhibitory control of PICs arises from animal studies and computer models (Hultborn et al ., 2003; Kuo et al ., 2003; Hyngstrom et al ., 2007; Bui et al ., 2008) but the magnitude of PICs in human motoneurons can be estimated quite easily due to recent advances in technology and analysis techniques (Klotz et al ., 2023; Möck & Del Vecchio, 2023). There remains a scarcity of research in humans, however, that establishes a relationship between reciprocal inhibition and PIC magnitude (Thorstensen, 2022).…”