Genetic updates on paroxysmal dyskinesias.

The paroxysmal dyskinesias are a diverse group of genetic disorders that manifest as episodic movements, with specific triggers, attack frequency, and duration. With recent advances in genetic sequencing, the number of genetic variants associated with paroxysmal dyskinesia has dramatically increased, and it is now evident that there is significant genotype-phenotype overlap, reduced (or incomplete) penetrance, and phenotypic variability. In addition, a variety of genetic conditions can present with paroxysmal dyskinesia as the initial symptom. This review will cover the 34 genes implicated to date and propose a diagnostic workflow featuring judicious use of whole-exome or -genome sequencing. The goal of this review is to provide a common understanding of paroxysmal dyskinesias so basic scientists, geneticists, and clinicians can collaborate effectively to provide diagnoses and treatments for patients.

Velocity neurons improve performance more than goal or position neurons do in a simulated closed-loop BCI arm-reaching task.

Brain-Computer Interfaces (BCIs) that convert brain-recorded neural signals into intended movement commands could eventually be combined with Functional Electrical Stimulation to allow individuals with Spinal Cord Injury to regain effective and intuitive control of their paralyzed limbs. To accelerate the development of such an approach, we developed a model of closed-loop BCI control of arm movements that (1) generates realistic arm movements (based on experimentally measured, visually-guided movements with real-time error correction), (2) simulates cortical neurons with firing properties consistent with literature reports, and (3) decodes intended movements from the noisy neural ensemble. With this model we explored (1) the relative utility of neurons tuned for different movement parameters (position, velocity, and goal) and (2) the utility of recording from larger numbers of neurons-critical issues for technology development and for determining appropriate brain areas for recording. We simulated arm movements that could be practically restored to individuals with severe paralysis, i.e., movements from an armrest to a volume in front of the person. Performance was evaluated by calculating the smallest movement endpoint target radius within which the decoded cursor position could dwell for 1 s. Our results show that goal, position, and velocity neurons all contribute to improve performance. However, velocity neurons enabled smaller targets to be reached in shorter amounts of time than goal or position neurons. Increasing the number of neurons also improved performance, although performance saturated at 30-50 neurons for most neuron types. Overall, our work presents a closed-loop BCI simulator that models error corrections and the firing properties of various movement-related neurons that can be easily modified to incorporate different neural properties. We anticipate that this kind of tool will be important for development of future BCIs.

Characterizing and predicting submovements during human three-dimensional arm reaches.

We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs) to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration) of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.

Standard task set for evaluating rehabilitation interventions for individuals with arm paralysis.

We have developed a set of upper-limb functional tasks to guide the design and test the performance of rehabilitation technologies that restore arm motion in people with high tetraplegia. Our goal was to develop a short set of tasks that would be representative of a much larger set of activities of daily living (ADLs), while also being feasible for a user of a unilateral, implanted functional electrical stimulation (FES) system. To compile this list of tasks, we reviewed existing clinical outcome measures related to arm and hand function and were further informed by surveys of patient desires. We ultimately selected a set of five tasks that captured the most common components of movement seen in ADLs and is therefore highly relevant for assessing FES-restored unilateral arm function in individuals with high cervical spinal cord injury. The tasks are intended to be used when setting design specifications and for evaluating and standardizing rehabilitation technologies under development. While not unique, this set of tasks will provide a common basis for comparing different interventions (e.g., FES, powered orthoses, robotic assistants) and testing different user command interfaces (e.g., sip-and-puff, head joysticks, brain-computer interfaces).

Predicting the initiation of minimum-jerk submovements in three-dimensional target-oriented human arm trajectories.

Target-oriented human arm trajectories can be represented as a series of summed minimum-jerk submovements. Under this framework, corrections for errors in reaching trajectories could be implemented by adding another submovement to the ongoing trajectory. It has been proposed that a feedback-feedforward error-detection process continuously evaluates trajectory error, but this process initiates corrections at discrete points in time. The present study demonstrates the ability of a feed-forward Artificial Neural Network (ANN) to learn the function of this error-detection process. Experimentally recorded human target-oriented arm trajectories were decomposed into submovements. It was assumed that the parameters of each submovement are known at their onset. Trained on these parameters, for each of three participants, an ANN can predict presence of corrections with sensitivity and specificity > 80%, and can predict their timing with R(2) > 40%.