Tremor and Dysmetria in Multiple Sclerosis: A Neurophysiological Study

Validation and application of computer vision algorithms for video-based tremor analysis

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55-0.86, p≤ .01) as well as an accuracy of up to 2.60 mm (95% CI [-3.13, 8.23]) and ≤0.21 Hz (95% CI [-0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings.

Time perception impairment in multiple sclerosis patients: a survey on internal clock model

Time perception is known as a mental ability to discern time. Although relative nature of time leaves its numerous aspects undefined, several models have been developed to describe temporal information processing in the brain as well as several areas of the brain have shown to be involved. Time perception alteration has been reported in several neurological conditions; however, the effect of multiple sclerosis (MS) on time perception has yet to be explained. In this study, we aimed to investigate the domains of temporal processing involved in patients with MS and the probable factors affecting it, such as the location of brain demyelinating plaques and gender. Two groups of participants (MS: n = 27 (8 men, 19 women), mean age = 33.85; control: n = 30 (10 men, 20 women), mean age = 28.46) were asked to perform quadruplet time perception tasks (prospective time estimation, duration discrimination, temporal reproduction, and paced motor timing) designed with a software program. Patients with MS had significantly higher scores in time estimation (p < 0.01) and duration discrimination (p < 0.001, in 100-ms interval; p < 0.05, in 1000-ms interval), indicating that MS patients overestimate the time. Since a slower internal clock for MS patients was expected as a result of axonal demyelination, these results suggest the time overestimation in patients with MS which is in contrast with the internal clock model. It means that a slow internal clock causes underestimating and perceiving the time slower.

The Neuropathology of Autoimmune Ataxias

Autoimmune-mediated ataxia has been associated with paraneoplastic disease, gluten enteropathy, Hashimoto thyroiditis as well as autoimmune disorders without a known associated disease. There have been relatively few reports describing the neuropathology of these conditions. This review is an attempt to consolidate those reports and determine the ways in which autoimmune ataxias can be neuropathologically differentiated from hereditary or other sporadic ataxias. In most instances, particularly in paraneoplastic forms, the presence of inflammatory infiltrates is a strong indicator of autoimmune disease, but it was not a consistent finding in all reported cases. Therefore, clinical and laboratory findings are important for assessing an autoimmune mechanism. Such factors as rapid rate of clinical progression, presence of known autoantibodies or the presence of a malignant neoplasm or other autoimmune disease processes need to be considered, particularly in cases where inflammatory changes are minimal or absent and the pathology is largely confined to the cerebellum and its connections, where the disease can mimic hereditary or other sporadic ataxias.