Ocular Tremor in Parkinson’s Disease: Discussion, Debate, and Controversy

The identification of ocular tremor in a small cohort of patients with Parkinson’s disease (PD) had lay somewhat dormant until the recent report of a pervasive ocular tremor as a universal finding in a large PD cohort that was, however, generally absent from a cohort of age-matched healthy subjects. The reported tremor had frequency characteristics similar to those of PD limb tremor, but the amplitude and frequency of the tremor did not correlate with clinical tremor ratings. Much controversy ensued as to the origin of such a tremor, and specifically as to whether a pervasive ocular tremor was a fundamental feature of PD, or rather a compensatory eye oscillation secondary to a transmitted head tremor, and thus a measure of a normal vestibulo-ocular reflex. In this mini review, we summarize some of the evidence for and against the case for a pervasive ocular tremor in PD and suggest future experiments that may help resolve these conflicting opinions

Persistent Postural-Perceptual Dizziness (PPPD) from Brain Imaging to Behaviour and Perception

Persistent postural-perceptual dizziness (PPPD) is a common cause of chronic dizziness associated with significant morbidity, and perhaps constitutes the commonest cause of chronic dizziness across outpatient neurology settings. Patients present with altered perception of balance control, resulting in measurable changes in balance function, such as stiffening of postural muscles and increased body sway. Observed risk factors include pre-morbid anxiety and neuroticism and increased visual dependence. Following a balance-perturbing insult (such as vestibular dysfunction), patients with PPPD adopt adaptive strategies that become chronically maladaptive and impair longer-term postural behaviour. In this article, we explore the relationship between behavioural postural changes, perceptual abnormalities, and imaging correlates of such dysfunction. We argue that understanding the pathophysiological mechanisms of PPPD necessitates an integrated methodological approach that is able to concurrently measure behaviour, perception, and cortical and subcortical brain function

Provably expressive temporal graph networks

Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamental results about the representational power and limits of the two main categories of TGNs: those that aggregate temporal walks (WA-TGNs), and those that augment local message passing with recurrent memory modules (MP-TGNs). Specifically, novel constructions reveal the inadequacy of MP-TGNs and WA-TGNs, proving that neither category subsumes the other. We extend the 1-WL (Weisfeiler-Leman) test to temporal graphs, and show that the most powerful MP-TGNs should use injective updates, as in this case they become as expressive as the temporal WL. Also, we show that sufficiently deep MP-TGNs cannot benefit from memory, and MP/WA-TGNs fail to compute graph properties such as girth. These theoretical insights lead us to PINT -- a novel architecture that leverages injective temporal message passing and relative positional features. Importantly, PINT is provably more expressive than both MP-TGNs and WA-TGNs. PINT significantly outperforms existing TGNs on several real-world benchmarks.Comment: Accepted to NeurIPS 202

Federated Stochastic Gradient Langevin Dynamics

Publisher Copyright: © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying communication causes the Markov chains to diverge from the true posterior even for very simple models. To alleviate both these problems, we propose conducive gradients, a simple mechanism that combines local likelihood approximations to correct gradient updates. Notably, conducive gradients are easy to compute, and since we only calculate the approximations once, they incur negligible overhead. We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD). We demonstrate that our approach can handle delayed communication rounds, converging to the target posterior in cases where DSGLD fails. We also show that FSGLD outperforms DSGLD for non-IID federated data with experiments on metric learning and neural networks.Peer reviewe

Attention modulates adaptive motor learning in the ‘broken escalator’ paradigm

The physical stumble caused by stepping onto a stationary (broken) escalator represents a locomotor after‐effect (LAE) that attests to a process of adaptive motor learning. Whether such learning is primarily explicit (requiring attention resources) or implicit (independent of attention) is unknown. To address this question, we diverted attention in the adaptation (MOVING) and aftereffect (AFTER) phases of the LAE by loading these phases with a secondary cognitive task (sequential naming of a vegetable, fruit, and a colour). Thirty‐six healthy adults were randomly assigned to 3 equally sized groups. They performed 5 trials stepping onto a stationary sled (BEFORE), 5 with the sled moving (MOVING) and 5 with the sled stationary again (AFTER). A ‘Dual‐Task‐ MOVING (DTM)’ group performed the dual‐task in the MOVING phase and the ‘Dual‐ Task‐AFTEREFFECT (DTAE)’ group in the AFTER phase. The ‘control’ group performed no dual‐task. We recorded trunk displacement, gait velocity and gastrocnemius muscle EMG of the left (leading) leg. The DTM, but not the DTAE group, had larger trunk displacement during the MOVING phase, and a smaller trunk displacement aftereffect, compared to controls. Gait velocity was unaffected by the secondary cognitive task in either group. Thus, adaptive locomotor learning involves explicit learning, whereas, the expression of the aftereffect is automatic (implicit). During rehabilitation, patients should be actively encouraged to maintain maximal attention when learning new or challenging locomotor tasks

Parallel MCMC Without Embarrassing Failures

Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub) posteriors defined on data partitions. Then, a server combines local results. While efficient, this framework is very sensitive to the quality of subposterior sampling. Common sampling problems such as missing modes or misrepresentation of low-density regions are amplified–instead of being corrected–in the combination phase, leading to catastrophic failures. In this work, we propose a novel combination strategy to mitigate this issue. Our strategy, Parallel Active Inference (PAI), leverages Gaussian Process (GP) surrogate modeling and active learning. After fitting GPs to subposteriors, PAI (i) shares information between GP surrogates to cover missing modes; and (ii) uses active sampling to individually refine subposterior approximations. We validate PAI in challenging benchmarks, including heavy-tailed and multi-modal posteriors and a real-world application to computational neuroscience. Empirical results show that PAI succeeds where previous methods catastrophically fail, with a small communication overhead.Peer reviewe

Hyperacute assessment of vertigo in suspected stroke

The management of patients with acute vertigo is most challenging in the hyperacute phase, both due to the complexity of vertigo as a symptom, the range of possible causes, and the lack of training in neuro-otology for non-specialists. Perhaps of greatest relevance is differentiating between peripheral (usually benign, e.g., inner ear) causes and central (potentially more sinister, e.g., stroke) causes. Several diagnostic algorithms have been introduced to help detect stroke in patients with acute vertigo. However, these algorithms have been largely validated in patients with an acute vestibular syndrome (with nystagmus) for whom symptoms have been present for a minimum of 24 h. The most challenging period within the diagnostic process is the hyperacute phase that determines triage and treatment, but where none of the established algorithms have been validated. In this review, we specifically describe practical implementation considerations for evaluating patients with hyperacute vertigo, including the timing of diagnostic testing within the emergency department pathway, resource availability, and pitfalls associated with current practices