Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework.

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

Optimal Control Perspective on Parkinson's Disease: Increased Delay Between State Estimator and Controller Produces Tremor.

Parkinson's disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson's disease. The thalamus relays signals from the cerebellum to the primary motor cortex: previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex). Simulations demonstrate realistic tremor in a neuromuscular model of the wrist. In addition, changes to effort sensitivity in the optimal controller may account for some clinical features of parkinsonian tremor, including the characteristics of re-emergent tremor and the time-varying amplitude and frequency of tremor. Contextualization of the optimal control model with physiological models and clinical observations provides insight into the potential role of the basal ganglia and cerebello-thalamo-cortical circuit and how treatments like dopaminergic medications and deep brain stimulation reduce tremor.

Drop ejection from vibrating damped, dampened wings.

The task of moisture removal from small, delicate surfaces such as sensors and flight surfaces on micro-flyers can be challenging due to remote location and small scale. Robustness is enhanced when such surfaces, of comparable scale to deposited drops, can remove deposition without external influence. At this scale, the dynamics of a solid surface responding to a mechanical input is highly-coupled to the fluid resting above. In this study, we explore highly-coupled fluid-solid mechanics using singular liquid drops of water and a glycerin solution resting on millimetric, forced cantilevers. These wing-inspired cantilevers are sinusoidally displaced at their base across 85-115 Hz, producing surface accelerations up to 45 gravities at drop release. We observe three principal drop release modes: sliding, normal-to-cantilever ejection, and drop pinch-off. Release modes are dependent on drop and cantilever properties, and cantilever motion. Predictions of ejection modes are accomplished by application of Euler elastica theory and drop adhesion forces. Lastly, we determine damping of cantilever motion imposed by sloshing drops.