The control and training of single motor units in isometric tasks are constrained by a common input signal

Abstract

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces could provide a promising basis for human motor augmentation by extracting potentially high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurons to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron's intrinsic excitability.

Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation

“…In addition, synchronisation of motor neurons has been reported across synergist muscles; for example, between the vastus lateralis and medialis (Mellor & Hodges, 2005 ), extensor carpi radialis longus and extensor carpi ulnaris (De Luca & Erim, 2002 ), and medial gastrocnemius and soleus (Gibbs et al., 1995 ). The presence of such common input probably explains why, in a study where participants were provided with real‐time feedback of the activity of pairs of motor neurons, they failed to volitionally control individual motor neurons (Bracklein et al., 2022 ).…”

Myomatrix arrays for high-definition muscle recording

“…3 ). Although fine wire electrodes have been used to isolate individual motor units in both humans and monkeys (Loeb and Gans 1986; Marshall et al 2021), and skin-surface electrode arrays robustly record motor unit populations in human subjects (Bracklein et al 2022; Farina and Merletti 2000), this resolution is limited to isometric tasks – that is, muscle contraction without movement – due to the sensitivity of both fine-wire and surface array electrodes to electrical artifacts caused by body movement. For ease of insertion into larger muscles, we modified the “thread” design used in our mouse arrays so that each Myomatrix array could be loaded into a standard hypodermic syringe and injected into the muscle ( Supplemental Fig.…”

“…These methods can resolve the activity of individual motor units in only a limited range of settings. First, to prevent measurement artifacts, traditional EMG methods require that a subject’s movements be highly restricted, typically in “isometric” force tasks where subjects contract their muscles without moving their bodies (Bracklein et al 2022; Farina and Holobar 2016; Marshall et al 2022; Negro et al 2016). Moreover, fine wire electrodes typically cannot detect single motor unit activity in small muscles, including the muscles of widely used model systems such as mice or songbirds (Pearson, Acharya, and Fouad 2005; Srivastava, Elemans, and Sober 2015; Pack et al 2023), and surface electrode arrays are poorly tolerated by freely behaving animal subjects.…”

I-Spin live: An open-source software based on blind-source separation for real-time decoding of motor unit activity in humans

“…Recent studies have reported real-time capabilities of motor unit identification by adapting the offline blind source separation algorithm (20)(21)(22)(23)(24). These studies used a two-step approach: 1) the separation vector for each motor unit is identified with offline decomposition during the training phase; and 2) the same vectors are applied in real-time to new EMG recordings.…”

“…In addition, the accuracy and boundary capabilities of online decomposition have not been systematically tested. Such information is necessary to better design experimental paradigms demonstrating, for example, the neural constrains on human movement generation and control (21).…”