Detection of Motor-Evoked Potentials Below the Noise Floor: Rethinking the Motor Threshold

Background: Motor-evoked potentials (MEP) are one of the most prominent responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. Understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smaller responses, e.g., from single motor units. However, available detection and quantization methods suffer from a large noise floor. Objective: This paper develops a detection method that extracts MEPs hidden below the noise floor. With this method, we aim to estimate excitatory activations of the corticospinal pathways well below the conventional detection level. Methods: The presented MEP detection method presents a self-learning matched-filter approach for improved robustness against noise. The filter is adaptively generated per subject through iterative learning. For responses that are reliably detected by conventional detection, the new approach is fully compatible with established peak-to-peak readings and provides the same results but extends the dynamic range below the conventional noise floor. Results: In contrast to the conventional peak-to-peak measure, the proposed method increases the signal-to-noise ratio by more than a factor of 5. The first detectable responses appear to be substantially lower than the conventional threshold definition of 50 μV median peak-to-peak amplitude. Conclusion: The proposed method shows that stimuli well below the conventional 50 μV threshold definition can consistently and repeatably evoke muscular responses and thus activate excitable neuron populations in the brain. As a consequence, the IO curve is extended at the lower end, and the noise cut-off is shifted. Importantly, the IO curve extends so far that the 50 μV point turns out to be closer to the center of the logarithmic sigmoid curve rather than close to the first detectable responses. The underlying method is applicable to a wide range of evoked potentials and other biosignals, such as in electroencephalography.

An adjoint approach to identification in Electromyography: Modeling and first order optimality conditions

In medical treatment, it can be necessary to know the position of a motor unit in a muscle. Recent advances in high-density surface Electromyography (EMG) measurement have opened the possibility of extracting information about single motor units. We present a mathematical approach to identify these motor units. On the base of an electrostatic forward model, we introduce an adjoint approach to efficiently simulate a surface EMG measurement and an optimal control approach to identify these motor units. We show basic results on existence of solutions and first-order optimality conditions.

O037 Genioglossus motor control following the return to sleep after brief arousal

Rationale Arousal from sleep has been shown to elicit a prolonged increase in genioglossus muscle activity that persists following the return to sleep and may protect against airway collapse. We hypothesised that this increased genioglossal activity following return to sleep after an arousal is due to persistent firing of inspiratory single motor units (SMUs) recruited during the arousal. Methods 34 healthy participants were studied overnight while wearing a nasal mask/pneumotachograph to measure ventilation and with 4 intramuscular genioglossus SMU electrodes. During stable N2 and N3 sleep, auditory tones were played to induce brief (3-15s) AASM arousals. Ventilation and genioglossus SMUs were quantified for 5 breaths before the tone, during the arousal and for 10 breaths after the return to sleep. Results A total of 1089 tones were played and gave rise to 236 SMUs recorded across arousal and the return to sleep in 20 participants (age 23±4.2 years and BMI 22.5±2.2kg/m2). Ventilation was elevated above baseline during arousal and the first post-arousal breath (p<0.001). The peak firing frequency of expiratory and tonic SMUs was unchanged during arousal and return to sleep, whereas inspiratory modulated SMUs were increased during the arousal and for 4 breaths following the return to sleep (p<0.001). Conclusions The prolonged increase in genioglossus activity that occurs on return to sleep after arousal is a result of persistent activity of inspiratory SMUs. Strategies to elevate inspiratory genioglossus SMU activity may be beneficial in preventing/treating obstructive sleep apnea.

P005 The effect of alcohol on the motor control of the genioglossus muscle

Rationale Alcohol is recognised to worsen snoring and obstructive sleep apnea (OSA). This effect is thought to be due to alcohol’s depressant effect on upper airway dilator muscles such as the genioglossus, but how alcohol reduces genioglossus activity is unknown. The aim of this study was to investigate alcohol’s effect on genioglossus single motor units (SMUs). Methods Healthy individuals visited the lab on two days (Alcohol: breath alcohol concentration ~0.08% or Placebo). They were instrumented with a nasal mask, 4 intramuscular genioglossus SMU EMG wires and an ear oximeter. They were exposed to 8–12 hypoxia trials (45-60s of 10%O2 followed by one breath of 100%O2) while awake. The SMUs were sorted according to their firing patterns with respect to respiration and were quantified during baseline, hypoxia, hyperoxia and recovery. Results The total number of SMUs recorded at baseline (68 and 67 respectively) and their distribution (ET: 29 vs 22, IP: 5 vs 10, IT: 8 vs 20 and TT: 26 vs 15 respectively) was similar between conditions. The discharge frequency did not differ between conditions (21Hz vs 22.4Hz, p>0.08). There was no difference between placebo and alcohol in the number (101 vs 88 respectively) and distribution (ET: 35 vs 32, IP: 22 vs 16, IT: 14 vs 22 and TT: 30 vs 17 respectively, p<0.05) of SMUs during hypoxia. Afterdischarge following hypoxia was also not different between conditions. Conclusion Alcohol has little effect on genioglossus SMUs and afterdischarge. OSA following alcohol may be related to increased upper airway resistance/nasal congestion.

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

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones 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. 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 control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.

After-Discharge in the Upper Airway Muscle Genioglossus Following Brief Hypoxia

Study Objectives Genioglossus after-discharge is thought to protect against pharyngeal collapse by minimising periods of low upper airway muscle activity. How genioglossus after-discharge occurs and which single motor units (SMUs) are responsible for the phenomenon are unknown. The aim of this study was to investigate genioglossal after-discharge. Methods During wakefulness, after-discharge was elicited 8-12 times in healthy individuals with brief isocapnic hypoxia (45-60s of 10%O2 in N2) terminated by a single breath of 100% O2. Genioglossus SMUs were designated as firing solely, or at increased rate, during inspiration (Inspiratory phasic [IP] and inspiratory tonic [IT] respectively); solely, or at increased rate, during expiration (Expiratory phasic [EP] or expiratory tonic [ET] respectively) or firing constantly without respiratory modulation (Tonic). SMUs were quantified at baseline, the end of hypoxia, the hyperoxic breath and the following 8 normoxic breaths. Results 210 SMU’s were identified in 17 participants. Genioglossus muscle activity was elevated above baseline for 7 breaths after hyperoxia (p<0.001), indicating a strong after-discharge effect. After-discharge occurred due to persistent firing of IP and IT units that were recruited during hypoxia, with minimal changes in ET, EP or Tonic SMUs. The firing frequency of units that were already active changed minimally during hypoxia or the afterdischarge period (P>0.05). Conclusion That genioglossal after-discharge is almost entirely due to persistent firing of previously silent inspiratory SMUs provides insight into the mechanisms responsible for the phenomenon and supports the hypothesis that the inspiratory and expiratory/tonic motor units within the muscle have idiosyncratic functions.

Identification of single motor units in skeletal muscle under low force isometric voluntary contractions using ultrafast ultrasound

The central nervous system (CNS) controls skeletal muscles by the recruitment of motor units (MUs). Understanding MU function is critical in the diagnosis of neuromuscular diseases, exercise physiology and sports, and rehabilitation medicine. Recording and analyzing the MUs’ electrical depolarization is the basis for state-of-the-art methods. Ultrafast ultrasound is a method that has the potential to study MUs because of the electrical depolarizations and consequent mechanical twitches. In this study, we evaluate if single MUs and their mechanical twitches can be identified using ultrafast ultrasound imaging of voluntary contractions. We compared decomposed spatio-temporal components of ultrasound image sequences against the gold standard needle electromyography. We found that 31% of the MUs could be successfully located and their firing pattern extracted. This method allows new non-invasive opportunities to study mechanical properties of MUs and the CNS control in neuromuscular physiology.