Background: The COVID‐19 pandemic restricted usual healthcare management for movement‐disorders patients, with a consequent upsurge in telemedicine to bridge the gap.Objective: To assess global telemedicine usage in the context of the pandemic.Methods: The Movement Disorder Society (MDS) Telemedicine Study Group surveyed telemedicine experts from 40 countries across all continents in March–April 2020. Four domains of telemedicine were assessed: legal regulations, reimbursement, clinical use, and barriers; comparing emerging responses to the pandemic versus the baseline scenario.Results: All forms of telemedicine for movement disorders increased globally, irrespective of country income categorization, as an immediate response to the pandemic. This was aided by widespread availability of technology and updated government regulations. However, privacy concerns, lack of reimbursement, limited access, and lack of telemedicine training were barriers highlighted worldwide.Conclusions: Questions remain about the longevity and extent of changes in regulations and reimbursement regarding telemedicine in the aftermath of the pandemic. © 2020 International Parkinson and Movement Disorder Society

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.

The roles of autophagy-related proteins as diagnostic or monitoring biomarkers in Parkinson’s disease (PD) have not been clearly elucidated. We recruited 32 patients with early-stage PD and 28 control subjects, and evaluated parkinsonian motor symptoms and dopamine transporter imaging data. Cerebrospinal fluid (CSF) levels of LC3B, Beclin1, and LAMP-2 were estimated using ELISAs, and CSF levels of ATG5, ATG7, and p62 were examined by immunoblotting. Additionally, we also assessed the levels of α-synuclein, total tau, and phosphorylated tau in CSF using ELISAs. Significant differences in the levels of LC3B, LAMP-2, and Beclin1 were observed between the PD and control groups. Using 29.8 pg/mL as the cut-off value for a diagnostic biomarker of PD, CSF LC3B levels exhibited high sensitivity (96.9%) and specificity (89.3%) with an area under the curve of 0.982. Furthermore, LC3B was significantly correlated with the asymmetry index in the caudate and putamen, as estimated by a semi-quantitative analysis of [¹⁸F] N-(3-fluoropropyl)-2β-carbon ethoxy-3β-(4-iodophenyl) nortropane (FP-CIT) positron emission tomography (PET). CSF levels of LC3B represented a potential diagnostic and prognostic biomarker of early-stage PD in patients. Based on our findings, molecular biological changes in PD are associated with dysregulation of the lysosomal autophagy pathway.

Background/Aims: Previous studies indicate that patients with the parkinsonian subtype of multiple system atrophy (MSA-P) experience cognitive impairment. This study aimed to identify the existence of cognitive impairments and the different topographic patterns of morphological changes in MSA-P by means of imaging analysis, and also whether these morphological changes could be associated with cognitive dysfunctions in MSA-P. Methods: We recruited 15 nondemented probable MSA-P patients and 32 normal controls (NC) for neuropsychological testing and MRI. We analyzed morphological changes using cortical thickness analysis, voxel-based morphometry (VBM) and cerebellar volumetry. Multiple linear regression analysis was performed to evaluate the correlation of each cognitive score with the mean thickness of significant cortical-thinning clusters, mean gray-matter density of VBM clusters and cerebellar volume. Results: The scores on the Digit Span Test, the Seoul Verbal Learning Test (immediate and delayed), the phonemic Controlled Oral Word Association Test and the Stroop color test were significantly lower in the MSA-P group than in the NC group. We found two clusters exhibiting significant cortical thinning in the right paracentral lobule and parahippocampal gyrus. VBM analysis revealed significant gray-matter atrophy in the MSA-P group in the bilateral basal ganglia, cerebellum and temporal and frontal cortical areas. Multiple linear regression analysis demonstrated that cognitive dysfunction correlated significantly with thinning in the neocortex, cerebellum and striatum. Conclusions: Our data demonstrate that cortical and cerebellar atrophy and striatal degeneration are associated with cognitive impairment in patients with MSA-P.