Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions

Abstract

AbstractObjectiveUltrafast ultrasound imaging has been used to measure intramuscular mechanical dynamics associated with single motor unit (MU) activations. Detecting MU activity from ultrasound sequences requires decomposing a displacement velocity field into components consisting of spatial maps and temporal displacement signals. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the temporal displacement signals with MU firings obtained from needle EMG. Here, we examined whether the repeatability of the spatial maps over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions.ApproachIn five healthy subjects, ultrafast ultrasound images and high-density surface EMG (HDsEMG) were recorded simultaneously from biceps brachii. MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based decomposition. For each contraction, displacement velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. The Jaccard Similarity Coefficient (JSC) was employed to evaluate the repeatability of components’ spatial maps across epochs. Finally, the association between the ultrasound components and the MUs decomposed from HDsEMG was assessed.Main resultsAll the MU-matched components had JSC > 0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8 ± 1.6 matches over 4.9 ± 1.8 MUs). The repeatable components (with JSC over the empirical threshold of 0.38) represented 14% of the total components (6.5 ± 3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from spurious components and can be used for data reduction.SignificanceThe results of our study provide the foundation for developing stand-alone methods to identify MU in ultrafast ultrasound sequences and represent a step forward towards real-time imaging of active MU territories. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.