Interplay between gait and neuropsychiatric symptoms in Parkinson's Disease.

Parkinson's Disease (PD) is a neurodegenerative disease which involves both motor and non-motor symptoms. Non-motor mental symptoms are very common among patients with PD since the earliest stage. In this context, gait analysis allows to detect quantitative gait variables to distinguish patients affected by non-motor mental symptoms from patients without these symptoms. A cohort of 68 PD subjects (divided in two groups) was acquired through gait analysis (single and double task) and spatial temporal parameters were analysed; first with a statistical analysis and then with a machine learning (ML) approach. Single-task variables showed that 9 out of 16 spatial temporal features were statistically significant for the univariate statistical analysis (p-value< 0.05). Indeed, a statistically significant difference was found in stance phase (p-value=0.032), swing phase (p-value=0.042) and cycle length (p-value=0.03) of the dual task. The ML results confirmed the statistical analysis, in particular, the Decision Tree classifier showed the highest accuracy (80.9%) and also the highest scores in terms of specificity and precision. Our findings indicate that patients with non-motor mental symptoms display a worse gait pattern, mainly dominated by increased slowness and dynamic instability.

Mild Cognitive Impairment Subtypes Are Associated With Peculiar Gait Patterns in Parkinson's Disease.

Background: Mild cognitive impairment (MCI) is frequent in Parkinson's disease (PD) and represents a risk factor for the development of dementia associated with PD (PDD). Since PDD has been associated with disability, caregiver burden, and an increase in health-related costs, early detection of MCI associated with PD (PD-MCI) and its biomarkers is crucial. Objective: Given that gait is considered a surrogate marker for cognitive decline in PD, the aim of this study was to compare gait patterns in PD-MCI subtypes in order to verify the existence of an association between specific gait features and particular MCI subtypes. Methods: A total of 67 patients with PD were consecutively enrolled and assessed by an extensive clinical and cognitive examination. Based on the neuropsychological examination, patients were diagnosed as patients with MCI (PD-MCI) and without MCI (no-PD-MCI) and categorized in MCI subtypes. All patients were evaluated using a motion capture system of a BTS Bioengineering equipped with six IR digital cameras. Gait of the patients was assessed in the ON-state under three different tasks (a single task and two dual tasks). Statistical analysis included the t-test, the Kruskal-Wallis test with post hoc analysis, and the exploratory correlation analysis. Results: Gait pattern was poorer in PD-MCI vs. no-PD-MCI in all tasks. Among PD-MCI subtypes, multiple-domain PD-MCI and amnestic PD-MCI were coupled with worse gait patterns, notably in the dual task. Conclusion: Both the magnitude of cognitive impairment and the presence of memory dysfunction are associated with increased measures of dynamic unbalance, especially in dual-task conditions, likely mirroring the progressive involvement of posterior cortical networks.

Gait Analysis in Progressive Supranuclear Palsy Phenotypes.

The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD.

Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages.

Progressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait evaluation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.

Using gait analysis' parameters to classify Parkinsonism: A data mining approach.

INTRODUCTION: Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis. METHODS: A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. RESULTS: Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. CONCLUSION: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.