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

Objective.Studying motor units is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identifiedin vivoby 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 performin silicotrials 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 andin vivothat would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

CMR-derived myocardial strain analysis differentiates ischemic and dilated cardiomyopathy-a propensity score-matched study.

Left ventricular (LV) longitudinal, circumferential, and radial motion can be measured using feature tracking of cardiac magnetic resonance (CMR) images. The aim of our study was to detect differences in LV mechanics between patients with dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) who were matched using a propensity score-based model. Between April 2017 and October 2019, 1224 patients were included in our CMR registry, among them 141 with ICM and 77 with DCM. Propensity score matching was used to pair patients based on their indexed end-diastolic volume (EDVi), ejection fraction (EF), and septal T1 relaxation time (psmatch2 module L Feature tracking provided six parameters for global longitudinal, circumferential, and radial strain with corresponding strain rates in each group. Strain parameters were compared between matched pairs of ICM and DCM patients using paired t tests. Propensity score matching yielded 72 patients in each group (DCM mean age 58.6 ± 11.6 years, 15 females; ICM mean age 62.6 ± 13.2 years, 11 females, p = 0.084 and 0.44 respectively; LV-EF 32.2 ± 13.5% vs. 33.8 ± 12.1%, p = 0.356; EDVi 127.2 ± 30.7 ml/m2 vs. 121.1 ± 41.8 ml/m2, p = 0.251; native T1 values 1165 ± 58 ms vs. 1167 ± 70 ms, p = 0.862). There was no difference in global longitudinal strain between DCM and ICM patients (- 10.9 ± 5.5% vs. - 11.2 ± 4.7%, p = 0.72), whereas in DCM patients there was a significant reduction in global circumferential strain (- 10.0 ± 4.5% vs. - 12.2 ± 4.7%, p = 0.002) and radial strain (17.1 ± 8.51 vs. 21.2 ± 9.7%, p = 0.039). Our data suggest that ICM and DCM patients have inherently different myocardial mechanics, even if phenotypes are similar. Our data show that GCS is significantly more impaired in DCM patients. This feature may help in more thoroughly characterizing cardiomyopathy patients.