A revised formula for the conversion of UPDRS-III scores to Hoehn and Yahr stage

Social decision-making in Parkinson's disease

INTRODUCTION

Parkinson's Disease (PD) commonly affects cognition and communicative functions, including the ability to perceive socially meaningful cues from nonverbal behavior and spoken language (e.g., a speaker's tone of voice). However, we know little about how people with PD use social information to make decisions in daily interactions (e.g., decisions to trust another person) and whether this ability rests on intact cognitive functions and executive/decision-making abilities in nonsocial domains.

METHOD

Non-demented adults with and without PD were presented utterances that conveyed differences in speaker confidence or politeness based on the way that speakers formulated their statement and their tone of voice. Participants had to use these speech-related cues to make trust-related decisions about interaction partners while playing the Trust Game. Explicit measures of social perception, nonsocial decision-making, and related cognitive abilities were collected.

RESULTS

Individuals with PD displayed significant differences from control participants in social decision-making; for example, they showed greater trust in game partners whose voice sounded confident and who explicitly stated that they would cooperate with the participant. The PD patients displayed relative intact social perception (speaker confidence or politeness ratings) and were unimpaired on a nonsocial decision-making task (the Dice game). No obvious relationship emerged between measures of social perception, social decision-making, or cognitive functioning in the PD sample.

CONCLUSIONS

Results provide evidence of alterations in decision-making restricted to social contexts in PD individuals with relatively preserved cognition with minimal changes in social perception. Researchers and practitioners interested in how PD affects social perception and cognition should include assessments that emulate social interactions, as non-interactive tasks may fail to detect the full impact of the disease on those affected.

New insights into Parkinson's disease through statistical analysis of standard clinical scales quantifying symptom severity

Clinical research studies in Parkinson's Disease (PD) focusing on symptom assessment often rely on thorough time-consuming physical examinations quantified on clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). Although widely used in clinical research, realistic time constraints preclude its use in daily clinical practice. The Hoehn and Yahr (H&Y) staging is an alternative scale which is easier to administer and provides a succinct descriptor of overall PD severity. There is no universal agreement amongst neurologists on the specific PD symptoms they need to be assessing in order to prescribe treatments and optimize symptom management for their patients, and practically there are no clinical scales which are recorded in daily clinical practice. In this study, we systematically evaluate diverse symptoms (as expressed in 44 UPDRS items) and aim to provide a statistical association with UPDRS and H&Y using rank correlation and mutual information metrics. Moreover, we investigate the projection of a UPDRS item subset on a 2D plot to map onto H&Y. We report some statistically strong correlations of PD symptoms against UPDRS and H&Y (|R| ≥ 0.3), and provide an intuitively appealing visualization mapping onto H&Y. These findings may be useful to neurologists as practical guidance in their daily clinical routine.

Automatic Classification and Monitoring of Denovo Parkinson's Disease by Learning Demographic and Clinical Features

Parkinson's Disease (PD) is the second most prevalent progressive neurological disorder around the world with high incidence rates for seniors. Since most symptoms are exposed in the later stages of the disease, early diagnosis of PD is essential for more effective treatment. The motivation of this research is early automatic assessment of PD using clinical information, not only for disease diagnosis but also for monitoring progression. After preprocessing the data, feature selection is done by the Mean Decrease Impurity (MDI) method. In the classification step, Random Forest (RF) is used as a classifier model for two tasks, including (1) classifying the subjects to PD and Healthy Control (HC), and (2) determining the disease severity level by Hoehn & Yahr (H&Y) scale. The clinical data used is taken from the Parkinson's Progression Markers Initiative (PPMI) database, which is the most prominent source of data for PD. Experimental results show promising performance of the proposed model for assessment of PD by incorporating clinical properties.

Early detection of Parkinson's disease through patient questionnaire and predictive modelling

Early detection of Parkinson's disease (PD) is important which can enable early initiation of therapeutic interventions and management strategies. However, methods for early detection still remain an unmet clinical need in PD. In this study, we use the Patient Questionnaire (PQ) portion from the widely used Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to develop prediction models that can classify early PD from healthy normal using machine learning techniques that are becoming popular in biomedicine: logistic regression, random forests, boosted trees and support vector machine (SVM). We carried out both subject-wise and record-wise validation for evaluating the machine learning techniques. We observe that these techniques perform with high accuracy and high area under the ROC curve (both >95%) in classifying early PD from healthy normal. The logistic model demonstrated statistically significant fit to the data indicating its usefulness as a predictive model. It is inferred that these prediction models have the potential to aid clinicians in the diagnostic process by joining the items of a questionnaire through machine learning.