Relationships between neck muscle electromyography and three-dimensional head kinematics during centrally induced torsional head perturbations

Neck muscle biomechanics and neural control

The mechanics, morphometry, and geometry of our joints, segments, and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate whether the biomechanical actions of individual neck muscles predict their neural control. Specifically, we compared the moment direction and variability produced by electrical stimulation of a neck muscle (biomechanics) to the preferred activation direction and variability (neural control). Subjects sat upright with their head fixed to a six-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19 mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from root mean squared electromyogram when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 three-dimensional directions. The spatial tuning curves at 15% MVC were well defined (unimodal, P < 0.05), and their preferred directions were 23°, 39°, and 21° different from their electrically stimulated directions for the SCM, SPL, and SSC, respectively ( P < 0.05). Intrasubject variability was smaller in electrically stimulated moment directions compared with voluntary preferred directions, and intrasubject variability decreased with increased activation levels. Our findings show that the neural control of neck muscles is not based solely on optimizing individual muscle biomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles. NEW & NOTEWORTHY Biomechanics are an intrinsic part of human neural control. In this study, we found that the biomechanics of individual neck muscles cannot fully predict their neural control. Consequently, physiologically based computational neck muscle controllers cannot calculate muscle activation schemes based on the isolated biomechanics of muscles. Furthermore, by measuring biomechanics we showed that the intrasubject variability of the neural control was lower for electrical vs. voluntary activation of the neck muscles.

Physiology of midbrain head movement neurons in cervical dystonia

BACKGROUND

Early theories for cervical dystonia, as promoted by Hassler, emphasized the role of the midbrain interstitial nucleus of Cajal. Focus then shifted to the basal ganglia, and it was further supported with the success of deep brain stimulation. Contemporary theories suggested the role of the cerebellum, but even more recent hypotheses renewed interest in the midbrain. Although the pretectum was visited on several occasions, we still do not know about the physiology of midbrain neurons in cervical dystonia.

METHODS

We analyzed the unique database of pretectal neurons collected in the 1970s and 1980s during historic stereotactic surgeries aimed to treat cervical dystonia. This database is valuable because such recordings could otherwise never be obtained from humans.

RESULTS

We found the following 3 types of eye or neck movement sensitivity: eye-only neurons responded to pure vertical eye movements, neck-only neurons were sensitive to pure neck movements, and the combined eye-neck neurons responded to eye and neck movements. There were the 2 neuronal subtypes: burst-tonic and tonic. The eye-neck or eye-only neurons sustained their activity during eccentric gaze holding. In contrast, the response of neck-only and eye-neck neurons exponentially decayed during neck movements.

CONCLUSIONS

Modern quantitative analysis of a historic database of midbrain single units from patients with cervical dystonia might support novel hypotheses for normal and abnormal head movements. This data, collected almost 4 decades ago, must be carefully viewed, especially because it was acquired using a less sophisticated technology available at that time and the aim was not to address specific hypothesis, but to make an accurate lesion providing optimal relief from dystonia. © 2017 International Parkinson and Movement Disorder Society.

Cervical dystonia: a neural integrator disorder

Ocular motor neural integrators ensure that eyes are held steady in straight-ahead and eccentric positions of gaze. Abnormal function of the ocular motor neural integrator leads to centripetal drifts of the eyes with consequent gaze-evoked nystagmus. In 2002 a neural integrator, analogous to that in the ocular motor system, was proposed for the control of head movements. Recently, a counterpart of gaze-evoked eye nystagmus was identified for head movements; in which the head could not be held steady in eccentric positions on the trunk. These findings lead to a novel pathophysiological explanation in cervical dystonia, which proposed that the abnormalities of head movements stem from a malfunctioning head neural integrator, either intrinsically or as a result of impaired cerebellar, basal ganglia, or peripheral feedback. Here we briefly recapitulate the history of the neural integrator for eye movements, then further develop the idea of a neural integrator for head movements, and finally discuss its putative role in cervical dystonia. We hypothesize that changing the activity in an impaired head neural integrator, by modulating feedback, could treat dystonia.

A kinematic model for 3-D head-free gaze-shifts

Rotations of the line of sight are mainly implemented by coordinated motion of the eyes and head. Here, we propose a model for the kinematics of three-dimensional (3-D) head-unrestrained gaze-shifts. The model was designed to account for major principles in the known behavior, such as gaze accuracy, spatiotemporal coordination of saccades with vestibulo-ocular reflex (VOR), relative eye and head contributions, the non-commutativity of rotations, and Listing's and Fick constraints for the eyes and head, respectively. The internal design of the model was inspired by known and hypothesized elements of gaze control physiology. Inputs included retinocentric location of the visual target and internal representations of initial 3-D eye and head orientation, whereas outputs were 3-D displacements of eye relative to the head and head relative to shoulder. Internal transformations decomposed the 2-D gaze command into 3-D eye and head commands with the use of three coordinated circuits: (1) a saccade generator, (2) a head rotation generator, (3) a VOR predictor. Simulations illustrate that the model can implement: (1) the correct 3-D reference frame transformations to generate accurate gaze shifts (despite variability in other parameters), (2) the experimentally verified constraints on static eye and head orientations during fixation, and (3) the experimentally observed 3-D trajectories of eye and head motion during gaze-shifts. We then use this model to simulate how 2-D eye-head coordination strategies interact with 3-D constraints to influence 3-D orientations of the eye-in-space, and the implications of this for spatial vision.