Bayesian Regression Analysis for Dependent Data with an Elliptical Shape.

This paper proposes a parametric hierarchical model for functional data with an elliptical shape, using a Gaussian process prior to capturing the data dependencies that reflect systematic errors while modeling the underlying curved shape through a von Mises-Fisher distribution. The model definition, Bayesian inference, and MCMC algorithm are discussed. The effectiveness of the model is demonstrated through the reconstruction of curved trajectories using both simulated and real-world examples.

Remote levodopa challenge test in Parkinson's disease: Feasibility, reliability, validity and economic value.

Background And Purpose: The aim was to demonstrate the feasibility, reliability and validity of an in-home remote levodopa challenge test (LCT), as delivered through an online platform, for patients with Parkinson's disease (PwPD).

Methods: Patients with Parkinson's disease eligible for deep brain stimulation surgery screening were enrolled. Participants sequentially received an in-home remote LCT and an in-hospital standard LCT (separated by 2.

Wearable sensor-based quantitative gait analysis in Parkinson's disease patients with different motor subtypes.

Gait impairments are among the most common and disabling symptoms of Parkinson's disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes.

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.

Introduction: Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging.

Machine-learning model for the prediction of acute orthostatic hypotension after levodopa administration.

Background: Levodopa could induce orthostatic hypotension (OH) in Parkinson's disease (PD) patients. Accurate prediction of acute OH post levodopa (AOHPL) is important for rational drug use in PD patients. Here, we develop and validate a prediction model of AOHPL to facilitate physicians in identifying patients at higher probability of developing AOHPL.

Specific Distribution of Digital Gait Biomarkers in Parkinson's Disease Using Body-Worn Sensors and Machine Learning.

Gait impairment leads to reduced social activities and low quality of life in people with Parkinson's disease (PD). PD is associated with unique gait signs and distributions of gait features. The assessment of gait characteristics is crucial in the diagnosis and treatment of PD.

Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

Background And Objectives: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.

Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor.

Background: Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning.

Objective: To differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors.

Bilateral globus pallidus interna deep brain stimulation in Parkinson's disease: Therapeutic effects and motor outcomes prediction in a short-term follow up.

Objective: We aimed to compare the motor effect of bilateral globus pallidus interna (GPi) deep brain stimulation (DBS) on motor subtypes of Parkinson's disease (PD) patients and identify preoperative predictive factors of short-term motor outcome.

Methods: We retrospectively investigated bilateral GPi DBS clinical outcomes in 55 PD patients in 1 year follow up. Motor outcome was measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III before and 1 year after surgery.

Early detection of Parkinson's disease from multiple signal speech: Based on Mandarin language dataset.

Parkinson's disease (PD) is a neurodegenerative disorder that negatively affects millions of people. Early detection is of vital importance. As recent researches showed dysarthria level provides good indicators to the computer-assisted diagnosis and remote monitoring of patients at the early stages.

Recognition of Freezing of Gait in Parkinson's Disease Based on Machine Vision.

Background: Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost.

Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors.

Freezing of gait is a common gait disorder among patients with advanced Parkinson's disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients.

Effects of Unilateral Stimulation in Parkinson's Disease: A Randomized Double-Blind Crossover Trial.

Introduction: Previous studies have shown that subthalamic nucleus (STN) and unilateral globus pallidus interna (GPi) are similarly effective in the deep brain stimulation (DBS) treatment of motor symptoms. However, the counterintuitively more common clinical application of STN DBS makes us hypothesize that STN is superior to GPi in the treatment of motor symptoms.

Methods: In this prospective, double-blind, randomized crossover study, idiopathic PD patients treated with combined unilateral STN and contralateral GPi DBS (STN in one brain hemisphere and GPi in the other) for 2 to 3 years were enrolled.

Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers.

The substantial heterogeneity of clinical symptoms and lack of reliable progression markers in Parkinson's disease (PD) present a major challenge in predicting accurate progression and prognoses. Increasing evidence indicates that each component of the neurovascular unit (NVU) and blood-brain barrier (BBB) disruption may take part in many neurodegenerative diseases. Since some portions of CSF are eliminated along the neurovascular unit and across the BBB, disturbing the pathways may result in changes of these substances.