Christopher Laine PhD

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Keywords: biofeedback; stroke; brain–computer interface; neurorehabilitation; corticomuscular coherence; electromyography; co-contraction; virtual reality

Objective. In this study, we examined if 6–9 Hz jaw tremor, an indirect indicator of Periodontal Mechanoreceptor (PMR) activity, is different in bruxists compared to healthy participants during production of a low-level constant bite force.

Methods. Bite force and surface EMG from the masseter muscle were recorded simultaneously as participants (13 patients, 15 controls) held a force transducer between the upper and lower incisors very gently.

Results. Tremor in 6–9 Hz band for bruxists was greater on average compared to controls, but the difference was not significant, both for force recordings and EMG activity.

Conclusions. The low effect sizes measured with the current protocol contrast highly with those of our previous study, where larger, dynamic bite forces were used, and where jaw tremor was markedly different in bruxists compared with controls.

Significance. We have now gained important insight into the conditions under which abnormal jaw tremor can be elicited in bruxism. From a scientific standpoint, this is critical for understanding the ‘abnormality’ of PMR feedback in bruxism. From a clinical perspective, our results represent progress towards the development of an optimal protocol in which jaw tremor can serve as a biological marker of bruxism.

Keywords. Tremor; Jaw; Bruxism; Periodontal mechanoreceptors; Bite force

Involuntary force variability below 15 Hz arises from, and is influenced by, many factors including descending neural drive, proprioceptive feedback, and mechanical properties of muscles and tendons. However, their potential interactions that give rise to the well-structured spectrum of involuntary force variability are not well understood due to a lack of experimental techniques. Here, we investigated the generation, modulation, and interactions among different sources of force variability using a physiologically-grounded closed-loop simulation of an afferented muscle model. The closed-loop simulation included a musculotendon model, muscle spindle, Golgi tendon organ (GTO), and a tracking controller which enabled target-guided force tracking. We demonstrate that closed-loop control of an afferented musculotendon suffices to replicate and explain surprisingly many cardinal features of involuntary force variability. Specifically, we present 1) a potential origin of low-frequency force variability associated with co-modulation of motor unit firing rates (i.e.,‘common drive’), 2) an in-depth characterization of how proprioceptive feedback pathways suffice to generate 5-12 Hz physiological tremor, and 3) evidence that modulation of those feedback pathways (i.e., presynaptic inhibition of Ia and Ib afferents, and spindle sensitivity via fusimotor drive) influence the full spectrum of force variability. These results highlight the previously underestimated importance of closed-loop neuromechanical interactions in explaining involuntary force variability during voluntary ‘isometric’ force control. Furthermore, these results provide the basis for a unifying theory that relates spinal circuitry to various manifestations of altered involuntary force variability in fatigue, aging and neurological disease.

The involuntary force fluctuations associated with physiological (as distinct from pathological) tremor are an unavoidable component of human motor control. While the origins of physiological tremor are known to depend on muscle afferentation, it is possible that the mechanical properties of muscle–tendon systems also affect its generation, amplification and maintenance. In this paper, we investigated the dependence of physiological tremor on muscle length in healthy individuals. We measured physiological tremor during tonic, isometric plantarflexion torque at 30% of maximum at three ankle angles. The amplitude of physiological tremor increased as calf muscles shortened in contrast to the stretch reflex whose amplitude decreases as muscle shortens. We used a published closed‐loop simulation model of afferented muscle to explore the mechanisms responsible for this behaviour. We demonstrate that changing muscle lengths does not suffice to explain our experimental findings. Rather, the model consistently required the modulation of γ‐static fusimotor drive to produce increases in physiological tremor with muscle shortening – while successfully replicating the concomitant reduction in stretch reflex amplitude. This need to control γ‐static fusimotor drive explicitly as a function of muscle length has important implications. First, it permits the amplitudes of physiological tremor and stretch reflex to be decoupled. Second, it postulates neuromechanical interactions that require length‐dependent γ drive modulation to be independent from α drive to the parent muscle. Lastly, it suggests that physiological tremor can be used as a simple, non‐invasive measure of the afferent mechanisms underlying healthy motor function, and their disruption in neurological conditions.

Coherence analysis has the ability to identify the presence of common descending drive shared by motor unit pools and reveals its spectral properties. However, the link between spectral properties of shared neural drive and functional interactions among muscles remains unclear. We assessed shared neural drive between muscles of the thumb and index finger while participants executed two mechanically distinct precision pinch tasks, each requiring distinct functional coordination among muscles. We found that shared neural drive was systematically reduced or enhanced at specific frequencies of interest (~10 and ~40 Hz). While amplitude correlations between surface EMG signals also exhibited changes across tasks, only their coherence has strong physiological underpinnings indicative of neural binding. Our results support the use of intermuscular coherence as a tool to detect when coactivated muscles are members of a functional group or synergy of neural origin. Furthermore, our results demonstrate the advantages of considering neural binding at 10, ~20, and >30 Hz, as indicators of task-dependent neural coordination strategies.

Pathological tremors are involuntary oscillatory movements which cannot be fully attenuated using conventional treatments. For this reason, several studies have investigated the use of neuromuscular electrical stimulation for tremor suppression. In a recent study, however, we found that electrical stimulation below the motor threshold also suppressed tremor, indicating involvement of afferent pathways. In this study, we further explored this possibility by systematically investigating how tremor suppression by afferent stimulation depends on the stimulation settings. In this way, we aimed at identifying the optimal stimulation strategy, as well as to elucidate the underlying physiological mechanisms of tremor suppression. Stimulation strategies varying the stimulation intensity and pulse timing were tested in nine tremor patients using either intramuscular or surface stimulation. Significant tremor suppression was observed in six patients (tremor suppression > 75% was observed in three patients) and the average optimal suppression level observed across all subjects was 52%. The efficiency for each stimulation setting, however, varied substantially across patients and it was not possible to identify a single set of stimulation parameters that yielded positive results in all patients. For example, tremor suppression was achieved both with stimulation delivered in an out-of-phase pattern with respect to the tremor, and with random timing of the stimulation. Overall, these results indicate that low-current stimulation of afferent fibers is a promising approach for tremor suppression, but that further research is required to identify how the effect can be maximized in the individual patient.

During force production, hand muscle activity is known to be coherent with activity in primary motor cortex, specifically in the beta-band (15–30 Hz) frequency range. It is not clear, however, if this coherence reflects the control strategy selected by the nervous system for a given task, or if it instead reflects an intrinsic property of cortico-spinal communication. Here, we measured corticomuscular and intermuscular coherence between muscles of index finger and thumb while a two-finger pinch grip of identical net force was applied to objects which were either stable (allowing synergistic activation of finger muscles) or unstable (requiring individuated finger control). We found that beta-band corticomuscular coherence with the first dorsal interosseous (FDI) and abductor pollicis brevis (APB) muscles, as well as their beta-band coherence with each other, was significantly reduced when individuated control of the thumb and index finger was required. We interpret these findings to show that beta-band coherence is reflective of a synergistic control strategy in which the cortex binds task-related motor neurons into functional units.

A new method is proposed for tracking individual motor units (MUs) across multiple experimental sessions on different days. The technique is based on a novel decomposition approach for high‐density surface electromyography and was tested with two experimental studies for reliability and sensitivity. Experiment I (reliability): ten participants performed isometric knee extensions at 10, 30, 50 and 70% of their maximum voluntary contraction (MVC) force in three sessions, each separated by 1 week. Experiment II (sensitivity): seven participants performed 2 weeks of endurance training (cycling) and were tested pre–post intervention during isometric knee extensions at 10 and 30% MVC. The reliability (Experiment I) and sensitivity (Experiment II) of the measured MU properties were compared for the MUs tracked across sessions, with respect to all MUs identified in each session. In Experiment I, on average 38.3% and 40.1% of the identified MUs could be tracked across two sessions (1 and 2 weeks apart), for the vastus medialis and vastus lateralis, respectively. Moreover, the properties of the tracked MUs were more reliable across sessions than those of the full set of identified MUs (intra‐class correlation coefficients ranged between 0.63—0.99 and 0.39–0.95, respectively). In Experiment II, ∼40% of the MUs could be tracked before and after the training intervention and training‐induced changes in MU conduction velocity had an effect size of 2.1 (tracked MUs) and 1.5 (group of all identified motor units). These results show the possibility of monitoring MU properties longitudinally to document the effect of interventions or the progression of neuromuscular disorders.

We investigated the possibility to identify motor units (MUs) with high-density surface electromyography (HDEMG) over experimental sessions in different days. 10 subjects performed submaximal knee extensions across three sessions in three days separated by one week, while EMG was recorded from the vastus medialis muscle with high-density electrode grids. The shapes of the MU action potentials (MUAPs) over multiple channels extracted from HDEMG decomposition were matched across sessions by cross-correlation. Forty and twenty percent of the MUs decomposed could be tracked across two and three sessions, respectively (average cross correlation 0.85 ± 0.04). The estimated properties of the matched motor units were similar across the sessions. For example, mean discharge rate and recruitment thresholds were measured with an intra-class correlation coefficient (ICCs) >0.80. These results strongly suggest that the same MUs were indeed identified across sessions. This possibility will allow monitoring changes in MU properties following interventions or during the progression of neuromuscular disorders.

Voluntary control of force is always marked by some degree of error and unsteadiness. Both neural and mechanical factors contribute to these fluctuations, but how they interact to produce them is poorly understood. In this study, we identify and characterize a previously undescribed neuromechanical interaction where the dynamics of voluntary force production suffice to generate involuntary tremor. Specifically, participants were asked to produce isometric force with the index finger and use visual feedback to track a sinusoidal target spanning 5–9% of each individual's maximal voluntary force level. Force fluctuations and EMG activity over the flexor digitorum superficialis (FDS) muscle were recorded and their frequency content was analyzed as a function of target phase. Force variability in either the 1–5 or 6–15 Hz frequency ranges tended to be largest at the peaks and valleys of the target sinusoid. In those same periods, FDS EMG activity was synchronized with force fluctuations. We then constructed a physiologically-realistic computer simulation in which a muscle-tendon complex was set inside of a feedback-driven control loop. Surprisingly, the model sufficed to produce phase-dependent modulation of tremor similar to that observed in humans. Further, the gain of afferent feedback from muscle spindles was critical for appropriately amplifying and shaping this tremor. We suggest that the experimentally-induced tremor may represent the response of a viscoelastic muscle-tendon system to dynamic drive, and therefore does not fall into known categories of tremor generation, such as tremorogenic descending drive, stretch-reflex loop oscillations, motor unit behavior, or mechanical resonance. Our findings motivate future efforts to understand tremor from a perspective that considers neuromechanical coupling within the context of closed-loop control. The strategy of combining experimental recordings with physiologically-sound simulations will enable thorough exploration of neural and mechanical contributions to force control in health and disease.

Objective. To assess the intra- and inter-session reliability of estimates of motor unit behavior and muscle fiber properties derived from high-density surface electromyography (HDEMG).

Methods. Ten healthy subjects performed submaximal isometric knee extensions during three recording sessions (separate days) at 10%, 30%, 50% and 70% of their maximum voluntary effort. The discharge timings of motor units of the vastus lateralis and medialis muscles were automatically identified from HDEMG by a decomposition algorithm. We characterized the number of detected motor units, their discharge rates, the coefficient of variation of their inter-spike intervals (CoV_isi), the action potential conduction velocity and peak-to-peak amplitude. Reliability was assessed for each motor unit characteristics by intra-class correlation coefficient (ICC). Additionally, a pulse-to-noise ratio (PNR) was calculated, to verify the accuracy of the decomposition.

Results. Good to excellent reliability within and between sessions was found for all motor unit characteristics at all force levels (ICCs > 0.8), with the exception of CoV_isi that presented poor reliability (ICC < 0.6). PNR was high and similar for both muscles with values ranging between 45.1 and 47.6 dB (accuracy > 95%).

Conclusion. Motor unit features can be assessed non-invasively and reliably within and across sessions over a wide range of force levels.

Significance. These results suggest that it is possible to characterize motor units in longitudinal intervention studies.

Neural control of synergist muscles is not well understood. Presumably, each muscle in a synergistic group receives some unique neural drive and some drive that is also shared in common with other muscles in the group. In this investigation, we sought to characterize the strength, frequency spectrum, and force dependence of the neural drive to the human vastus lateralis and vastus medialis muscles during the production of isometric knee extension forces at 10 and 30% of maximum voluntary effort. High-density surface electromyography recordings were decomposed into motor unit action potentials to examine the neural drive to each muscle. Motor unit coherence analysis was used to characterize the total neural drive to each muscle and the drive shared between muscles. Using a novel approach based on partial coherence analysis, we were also able to study specifically the neural drive unique to each muscle (not shared). The results showed that the majority of neural drive to the vasti muscles was a cross-muscle drive characterized by a force-dependent strength and bandwidth. Muscle-specific neural drive was at low frequencies (<5 Hz) and relatively weak. Frequencies of neural drive associated with afferent feedback (6–12 Hz) and with descending cortical input (∼20 Hz) were almost entirely shared by the two muscles, whereas low-frequency (<5 Hz) drive comprised shared (primary) and muscle-specific (secondary) components. This study is the first to directly investigate the extent of shared versus independent control of synergist muscles at the motor neuron level.

Objective. To determine if sleep bruxism is associated with abnormal physiological tremor of the jaw during a visually-guided bite force control task.

Methods. Healthy participants and patients with sleep bruxism were given visual feedback of their bite force and asked to trace triangular target trajectories (duration = 20 s, peak force <35% maximum voluntary force). Bite force control was quantified in terms of the power spectra of force fluctuations, masseter EMG activity, and force-to-EMG coherence.

Results. Patients had greater jaw force tremor at ∼8 Hz relative to controls, along with increased masseter EMG activity and force-to-EMG coherence in the same frequency range. Patients also showed lower force-to-EMG coherence at low frequencies (<3 Hz), but greater coherence at high frequencies (20–40 Hz). Finally, patients had greater 6–10 Hz force tremor during periods of descending vs. ascending force, while controls showed no difference in tremor with respect to force dynamics.

Conclusion. Patients with bruxism have abnormal jaw tremor when engaged in a visually-guided bite force task.

Significance. Measurement of jaw tremor may aid in the detection/evaluation of bruxism. In light of previous literature, our results also suggest that bruxism is marked by abnormal or mishandled peripheral feedback from the teeth.

Parkinson’s disease (PD) is a progressive neurodegenerative disease affecting about 1–2% of the population over the age of 65. Individuals with PD experience gradual deterioration of dexterous manipulation for activities of daily living; however, current clinical evaluations are mostly subjective and do not quantify changes in dynamic control of fingertip force that is critical for manual dexterity. Thus, there is a need to develop clinical measures to quantify those changes with aging and disease progression. We investigated the dynamic control of fingertip forces in both hands of 20 individuals with PD (69.0 ± 7.4 years) using the Strength–Dexterity test. The test requires low forces (<3 N) to compress a compliant and slender spring prone to buckling. A maximal level of sustained compression is informative of the greatest instability the person can control, and thus is indicative of the integrity of the neuromuscular system for dexterous manipulation. Miniature sensors recorded fingertip force (F) during maximal sustained compressions. The force variability during sustained compression was quantified in two frequency bands: low (<4 Hz, F_LF) and high (4–12 Hz, F_HF). F_LF characterizes variability in voluntary fluctuations, while F_HF characterizes variability in involuntary fluctuations including tremor. The more-affected hand exhibited significantly lower F and lower F_LF than those in the less-affected hand. The more-affected hand showed significant negative correlations between F_LF and the Unified Parkinson’s Disease Rating Scale motor scores for both total and hand-only, suggesting that greater force variability in the voluntary range was associated with less clinical motor impairment. We conclude the nature of force variability in the voluntary range during this dynamic and dexterous task may be a biomarker of greater motor capability/flexibility/adaptability in PD. This approach may provide a more quantitative clinical assessment of changes of sensorimotor control in individuals with PD.

Neuromuscular electrical stimulation (NMES) is commonly used in rehabilitation, but electrically evoked muscle activation is in several ways different from voluntary muscle contractions. These differences lead to challenges in the use of NMES for restoring muscle function. We investigated the use of low-current, high-frequency nerve stimulation to activate the muscle via the spinal motoneuron (MN) pool to achieve more natural activation patterns. Using a novel stimulation protocol, the H-reflex responses to individual stimuli in a train of stimulation pulses at 100 Hz were reliably estimated with surface EMG during low-level contractions. Furthermore, single motor unit recruitment by afferent stimulation was analyzed with intramuscular EMG. The results showed that substantially elevated H-reflex responses were obtained during 100-Hz stimulation with respect to a lower stimulation frequency. Furthermore, motor unit recruitment using 100-Hz stimulation was not fully synchronized, as it occurs in classic NMES, and the discharge rates differed among motor units because each unit was activated only after a specific number of stimuli. The most likely mechanism behind these observations is the temporal summation of subthreshold excitatory postsynaptic potentials from Ia fibers to the MNs. These findings and their interpretation were also verified by a realistic simulation model of afferent stimulation of a MN population. These results suggest that the proposed stimulation strategy may allow generation of considerable levels of muscle activation by motor unit recruitment that resembles the physiological conditions.

Aim. The purpose of this investigation was to understand how visual information, when used to guide muscle activity, influences the frequency content of the neural drive to muscles and the gain of afferent feedback.

Methods. Subjects maintained static, isometric contractions of the tibialis anterior muscle by matching a visual display of their ankle dorsiflexion force to a target set at 10% of their maximum voluntary contraction level. Two visual feedback conditions were studied. The first was a high‐sensitivity feedback, in which small changes in force were of large on‐screen visual magnitude. The second was a low‐sensitivity feedback, in which the on‐screen scaling of feedback was reduced by a factor of 10, making small force fluctuations difficult to perceive. Force tremor and Hoffmann reflex (H‐reflex) amplitudes were compared between the two conditions, as well as coherence among single motor unit spike trains derived from high‐density EMG recordings.

Results. The high‐sensitivity feedback condition was associated with lower error, larger force tremor (4–12 Hz) and larger H‐reflex amplitudes relative to the low‐sensitivity feedback condition. In addition, the use of high‐sensitivity feedback was associated with lower 1–5 Hz coherence among pairs of motor units, but larger coherence at high frequencies (6–12, approx. 20, >30 Hz).

Conclusion. Alteration of visual feedback influences nearly the entire frequency spectrum of common input to motor neurones, as well the gain of afferent feedback. We speculate that task‐related modulation of afferent feedback could be the origin of many of the observed changes in the neural drive to muscles.

Appropriate control of muscle contraction requires integration of command signals with sensory feedback. Sensorimotor integration is often studied under conditions in which muscle force is controlled with visual feedback. While it is known that alteration of visual feedback can influence task performance, the underlying changes in neural drive to the muscles are not well understood. In this study, we characterize the frequency content of force fluctuations and neural drive when production of muscle force is target guided versus self guided. In the self-guided condition, subjects performed isometric contractions of the first dorsal interosseous (FDI) muscle while slowly and randomly varying their force level. Subjects received visual feedback of their own force in order to keep contractions between 6% and 10% of maximum voluntary contraction (MVC). In the target-guided condition, subjects used a display of their previously generated force as a target to track over time. During target tracking, force tremor increased significantly in the 3–5 and 7–9 Hz ranges, compared with self-guided contractions. The underlying changes in neural drive were assessed by coherence analysis of FDI motor unit activity. During target-guided force production, pairs of simultaneously recorded motor units showed less coherent activity in the 3–5 Hz frequency range but greater coherence in the 7–9 Hz range than in the self-guided contractions. These results show that the frequency content of common synaptic input to motoneurons is altered when force production is visually guided. We propose that a change in stretch-reflex gain could provide a potential mechanism for the observed changes in force tremor and motor unit coherence.

The genioglossus (GG) muscle is considered the principal protruder muscle of the tongue that dilates and stiffens the pharyngeal airway. We recorded whole muscle and single motor unit (MU) activities in healthy adults performing progressive intensity exercise on a cycle ergometer. Tungsten microelectrodes were inserted percutaneously into the GG of 11 subjects (20–40 years) to record electromyographic (EMG) activities and pulmonary ventilation (V_I) at rest and at workload increments up to 300 W. Increases in respiratory drive were associated with increases in V_I, mean inspiratory flow (V_t/T_i) and tonic and phasic components of the GG EMG activity. In contrast, individual MUs typically showed expiration‐related decreases in firing as exercise intensity increased. We suggest the decrease in MU activity may occur secondary to afferent feedback from lungs/chest wall and that compensation for more negative inspiratory airway pressures generated during heavy exercise occurs primarily via recruitment of previously silent MUs.

Output from the primary motor cortex contains oscillations that can have frequency-specific effects on the firing of motoneurons (MNs). Whereas much is known about the effects of oscillatory cortical drive on the output of spinal MN pools, considerably less is known about the effects on cranial motor nuclei, which govern speech/oromotor control. Here, we investigated cortical input to one such motor pool, the hypoglossal motor nucleus (HMN), which controls muscles of the tongue. We recorded intramuscular genioglossus electromyogram (EMG) and scalp EEG from healthy adult subjects performing a tongue protrusion task. Cortical entrainment of HMN population activity was assessed by measuring coherence between EEG and multiunit EMG activity. In addition, cortical entrainment of individual MN firing activity was assessed by measuring phase locking between single motor unit (SMU) action potentials and EEG oscillations. We found that cortical entrainment of multiunit activity was detectable within the 15- to 40-Hz frequency range but was inconsistent across recordings. By comparison, cortical entrainment of SMU spike timing was reliable within the same frequency range. Furthermore, this effect was found to be intermittent over time. Our study represents an important step in understanding corticomuscular synchronization in the context of human oromotor control and is the first study to document SMU entrainment by cortical oscillations in vivo.

The respiratory central pattern generator distributes rhythmic excitatory input to phrenic, intercostal, and hypoglossal premotor neurons. The degree to which this input shapes motor neuron activity can vary across respiratory muscles and motor neuron pools. We evaluated the extent to which respiratory drive synchronizes the activation of motor unit pairs in tongue (genioglossus, hyoglossus) and chest-wall (diaphragm, external intercostals) muscles using coherence analysis. This is a frequency domain technique, which characterizes the frequency and relative strength of neural inputs that are common to each of the recorded motor units. We also examined coherence across the two tongue muscles, as our previous work shows that, despite being antagonists, they are strongly coactivated during the inspiratory phase, suggesting that excitatory input from the premotor neurons is distributed broadly throughout the hypoglossal motoneuron pool. All motor unit pairs showed highly correlated activity in the low-frequency range (1–8 Hz), reflecting the fundamental respiratory frequency and its harmonics. Coherence of motor unit pairs recorded either within or across the tongue muscles was similar, consistent with broadly distributed premotor input to the hypoglossal motoneuron pool. Interestingly, motor units from diaphragm and external intercostal muscles showed significantly higher coherence across the 10–20-Hz bandwidth than tongue-muscle units. We propose that the lower coherence in tongue-muscle motor units over this range reflects a larger constellation of presynaptic inputs, which collectively lead to a reduction in the coherence between hypoglossal motoneurons in this frequency band. This, in turn, may reflect the relative simplicity of the respiratory drive to the diaphragm and intercostal muscles, compared with the greater diversity of functions fulfilled by muscles of the tongue.

The tongue plays a key role in various volitional and automatic functions such as swallowing, maintenance of airway patency, and speech. Precisely how hypoglossal motor neurons, which control the tongue, receive and process their often concurrent input drives is a subject of ongoing research. We investigated common synaptic input to the hypoglossal motor nucleus by measuring the coordination of spike timing, firing rate, and oscillatory activity across motor units recorded from unilateral (i.e., within a belly) or bilateral (i.e., across both bellies) locations within the genioglossus (GG), the primary protruder muscle of the tongue. Simultaneously recorded pairs of motor units were obtained from 14 healthy adult volunteers using tungsten microelectrodes inserted percutaneously into the GG while the subjects were engaged in volitional tongue protrusion or rest breathing. Bilateral motor unit pairs showed concurrent low frequency alterations in firing rate (common drive) with no significant difference between tasks. Unilateral motor unit pairs showed significantly stronger common drive in the protrusion task compared with rest breathing, as well as higher indices of synchronous spiking (short-term synchrony). Common oscillatory input was assessed using coherence analysis and was observed in all conditions for frequencies up to ∼5 Hz. Coherence at frequencies up to ∼10 Hz was strongest in motor unit pairs recorded from the same GG belly in tongue protrusion. Taken together, our results suggest that cortical drive increases motor unit coordination within but not across GG bellies, while input drive during rest breathing is distributed uniformly to both bellies of the muscle.

The amygdala plays a crucial role in evaluating the emotional significance of stimuli and in transforming the results of this evaluation into appropriate autonomic responses. Lesion and stimulation studies suggest involvement of the amygdala in the generation of the skin conductance response (SCR), which is an indirect measure of autonomic activity that has been associated with both emotion and attention. It is unclear if this involvement marks an emotional reaction to an external stimulus or sympathetic arousal regardless of its origin. We recorded skin conductance in parallel with single-unit activity from the right amygdala of two rhesus monkeys during a rewarded image viewing task and while the monkeys sat alone in a dimly lit room, drifting in and out of sleep. In both experimental conditions, we found similar SCR-related modulation of activity at the single-unit and neural population level. This suggests that the amygdala contributes to the production or modulation of SCRs regardless of the source of sympathetic arousal.