Quantitative Digitography Measures Motor Symptoms and Disease Progression in Parkinson's Disease.

BACKGROUND: Assessment of motor signs in Parkinson's disease (PD) requires an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous monitoring for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic. OBJECTIVE: Investigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PD. METHODS: Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years. RESULTS: QDG-RAFT metrics showed differences between PD and controls and provided correlated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment, and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy and showed differences between akinetic-rigid and tremor-dominant phenotypes, as well as people with and without FOG. CONCLUSIONS: QDG is a reliable technology, which could be used in the clinic or remotely. This could improve access to care, allow complex remote disease management based on data received in real time, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive motor metrics needed for therapeutic trials.

Effect of Tactile Sensory Substitution on the Proprioceptive Error Map of the Arm.

Proprioceptive error of estimated fingertip position in two-dimensional space is reduced with the addition of tactile stimulation to the fingertip. This tactile input does not disrupt the subjects' estimation strategy, as the individual error vector maps maintain their overall geometric structure. This relationship suggests an integration of proprioception and tactile sensory information to enhance proprioceptive estimation. To better understand this multisensory integration, we explored the effect of electrotactile and vibrotactile stimulation to the fingertips in place of actual contact, thus limiting interaction forces. This allowed us to discern any proprioceptive estimation improvement that arose from purely tactile stimulation. Ten right-handed and ten left-handed subjects performed a simple right-handed proprioceptive estimation task under four tactile feedback conditions: hover, touch, electrotactile, and vibrotactile. Target sets were generated for each subject, persisted across all feedback modalities, and targets were presented in randomized orders. Error maps across the workspace were generated using polynomial models of the subjects' responses. Error maps did not change shape between conditions for any right-handed subjects and changed for a single condition for two left-handed subjects. Non-parametric statistical analysis of the error magnitude shows that both modes of sensory substitution significantly reduce error for right-handed subjects, but not to the level of actual touch. Left-handed subjects demonstrated increased error for all feedback conditions compared to hover. Compared to right-handed subjects, left-handed subjects demonstrated more error in each condition except the hover condition. This is consistent with the hypothesis that the non-dominant hand is specialized for position control, while the dominant is specialized for velocity. Notably, our results suggest that non-dominant hand estimation strategies are hindered by stimuli to the fingertip. We conclude that electrotactile and vibrotactile sensory substitution only succeed in multisensory integration when applied to the dominant hand. These feedback modalities do not disrupt established dominate hand proprioceptive error maps, and existing strategies adapt to the novel input and minimize error. Since actual touch provides the best error reduction, sensory substitution lacks some unidentified beneficial information, such as familiarity or natural sensation. This missing component could also be what confounds subjects using their non-dominant hand for positional tasks.

Ramp Rate Evaluation and Configuration for Safe and Tolerable Closed-Loop Deep Brain Stimulation.

Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or "ramp" rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson's disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.

Perspective: Evolution of Control Variables and Policies for Closed-Loop Deep Brain Stimulation for Parkinson's Disease Using Bidirectional Deep-Brain-Computer Interfaces.

A deep brain stimulation system capable of closed-loop neuromodulation is a type of bidirectional deep brain-computer interface (dBCI), in which neural signals are recorded, decoded, and then used as the input commands for neuromodulation at the same site in the brain. The challenge in assuring successful implementation of bidirectional dBCIs in Parkinson's disease (PD) is to discover and decode stable, robust and reliable neural inputs that can be tracked during stimulation, and to optimize neurostimulation patterns and parameters (control policies) for motor behaviors at the brain interface, which are customized to the individual. In this perspective, we will outline the work done in our lab regarding the evolution of the discovery of neural and behavioral control variables relevant to PD, the development of a novel personalized dual-threshold control policy relevant to the individual's therapeutic window and the application of these to investigations of closed-loop STN DBS driven by neural or kinematic inputs, using the first generation of bidirectional dBCIs.