Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner.

The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, whose activation we refer to as 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFPs). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

Estimating muscle activation from EMG using deep learning-based dynamical systems models.

Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation.Main results. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches.Significance.This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas, and for improving brain-machine interfaces that rely on myoelectric control signals.

Mechanical vibration does not systematically reduce the tremor in essential tremor patients.

Essential tremor (ET) is a major cause of disability and is not effectively managed in half of the patients. We investigated whether mechanical vibration could reduce tremor in ET by selectively recruiting afferent pathways. We used piezoelectric actuators to deliver vibratory stimuli to the hand and forearm during long trials (4 min), while we monitored the tremor using inertial sensors. We analyzed the effect of four stimulation strategies, including different constant and variable vibration frequencies, in 18 ET patients. Although there was not a clear homogeneous response to vibration across patients and strategies, in most cases (50-72%) mechanical vibration was associated with an increase in the amplitude of their tremor. In contrast, the tremor was reduced in 5-22% of the patients, depending on the strategy. However, these results are hard to interpret given the intrinsic variability of the tremor: during equally long trials without vibration, the tremor changed significantly in 67% of the patients (increased in 45%; decreased in 22%). We conclude that mechanical vibration of the limb does not have a systematic effect on tremor in ET. Moreover, the observed intrinsic variability of the tremor should be taken into account when designing future experiments to assess tremor in ET and how it responds to any intervention.

Voluntary and tremorogenic inputs to motor neuron pools of agonist/antagonist muscles in essential tremor patients.

Pathological tremor is an oscillation of body parts at 3-10 Hz, determined by the output of spinal motor neurons (MNs), which receive synaptic inputs from supraspinal centers and muscle afferents. The behavior of spinal MNs during tremor is not well understood, especially in relation to the activation of the multiple muscles involved. Recent studies on patients with essential tremor have shown that antagonist MN pools receive shared input at the tremor frequency. In this study, we investigated the synaptic inputs related to tremor and voluntary movement, and their coordination across antagonist muscles. We analyzed the spike trains of motor units (MUs) identified from high-density surface electromyography from the forearm extensor and flexor muscles in 15 patients with essential tremor during postural tremor. The shared synaptic input was quantified by coherence and phase difference analysis of the spike trains. All pairs of spike trains in each muscle showed coherence peaks at the voluntary drive frequency (1-3 Hz, 0.2 ± 0.2, mean ± SD) and tremor frequency (3-10 Hz, 0.6 ± 0.3) and were synchronized with small phase differences (3.3 ± 25.2° and 3.9 ± 22.0° for the voluntary drive and tremor frequencies, respectively). The coherence between MN spike trains of antagonist muscle groups at the tremor frequency was significantly smaller than intramuscular coherence. We predominantly observed in-phase activation of MUs between agonist/antagonist muscles at the voluntary frequency band (0.6 ± 48.8°) and out-of-phase activation at the tremor frequency band (126.9 ± 75.6°). Thus MNs innervating agonist/antagonist muscles concurrently receive synaptic inputs with different phase shifts in the voluntary and tremor frequency bands.NEW & NOTEWORTHY Although the mechanical characteristics of tremor have been widely studied, the activation of the affected muscles is still poorly understood. We analyzed the behavior of motor units of pairs of antagonistic wrist muscle groups in patients with essential tremor and studied their activity at voluntary movement- and tremor-related frequencies. We found that the phase relation between inputs to antagonistic muscles is different at the voluntary and tremor frequency bands.

A neuroprosthesis for tremor management through the control of muscle co-contraction.

BACKGROUND: Pathological tremor is the most prevalent movement disorder. Current treatments do not attain a significant tremor reduction in a large proportion of patients, which makes tremor a major cause of loss of quality of life. For instance, according to some estimates, 65% of those suffering from upper limb tremor report serious difficulties during daily living. Therefore, novel forms for tremor management are required. Since muscles intrinsically behave as a low pass filter, and tremor frequency is above that of volitional movements, the authors envisioned the exploitation of these properties as a means of developing a novel treatment alternative. This treatment would rely on muscle co-contraction for tremor management, similarly to the strategy employed by the intact central nervous system to stabilize a limb during certain tasks. METHODS: We implemented a neuroprosthesis that regulated the level of muscle co-contraction by injecting current at a pair of antagonists through transcutaneous neurostimulation. Co-contraction was adapted to the instantaneous parameters of tremor, which were estimated from the raw recordings of a pair of solid state gyroscopes with a purposely designed adaptive algorithm. For the experimental validation, we enrolled six patients suffering from parkinsonian or essential tremor of different severity, and evaluated the effect of the neuroprosthesis during standard tasks employed for neurological examination. RESULTS: The neuroprosthesis attained significant attenuation of tremor (p<0.001), and reduced its amplitude up to a 52.33±25.48%. Furthermore, it alleviated both essential and parkinsonian tremor in spite of their different etiology and symptomatology. Tremor severity was not a limiting factor on the performance of the neuroprosthesis, although there was a subtle trend towards larger attenuation of more severe tremors. Tremor frequency was not altered during neurostimulation, as expected from the central origin of Parkinson's disease and essential tremor. All patients showed a good tolerance to neurostimulation in terms of comfort and absence of pain, and some spontaneously reported that they felt that tremor was reduced when the neuroprosthesis was activated. CONCLUSIONS: The results presented herein demonstrate that the neuroprosthesis provides systematic attenuation of the two major types of tremor, irrespectively from their severity. This study sets the basis for the validation of the neuroprosthesis as an alternative, non-invasive means for tremor management.

Biomechanical loading as an alternative treatment for tremor: a review of two approaches.

BACKGROUND: Tremor is the most common movement disorder and strongly increases in incidence and prevalence with aging. Although not life threatening, upper-limb tremors hamper the independence of 65% of people suffering from them affected persons, greatly impacting their quality of life. Current treatments include pharmacotherapy and surgery (thalamotomy and deep brain stimulation). However, these options are not sufficient for approximately 25% of patients. Therefore, further research and new therapeutic options are required to effectively manage pathological tremor. METHODS: This paper presents findings of two research projects in which two different wearable robots for tremor management were developed based on force loading and validated. The first consisted of a robotic exoskeleton that applied forces to tremulous limbs and consistently attenuated mild and severe tremors. The second was a neuroprosthesis based on transcutaneous neurostimulation. A total of 22 patients suffering from parkinsonian or essential tremor (ET) of different severities were recruited for experimental validation, and both systems were evaluated using standard tasks employed for neurological examination. The inclusion criterion was a postural and/or kinetic pathological upper-limb tremor resistant to medication. RESULTS: The results demonstrate that both approaches effectively suppressed tremor in most patients, although further research is required. The work presented here is based on clinical evidence from a small number of patients (n = 10 for robotic exoskeleton and n = 12 for the neuroprosthesis), but most had a positive response to the approaches. In summary, biomechanical loading is non-invasive and painless. It may be effective in patients who are insufficiently responsive (or have adverse reactions) to drugs or in whom surgery is contraindicated. DISCUSSION: This paper identifies and evaluates biomechanical loading approaches to tremor management and discusses their potential.