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© 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background. Parkinson’s disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses. Methods. We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson’s Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs. Results. The resulting progression model yields a quantitative comparison of patients’ disease progression under different usage of medications. We also obtain an algorithm to predict patients’ UPDRS III scores. Conclusions. By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year’s motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.

Details

Title
A progression analysis of motor features in Parkinson's disease based on the mapper algorithm
Author
Ma, Ling-Yan; Feng, Tao; He, Chengzhang; Li, Mujing; Ren, Kang; Tu, Junwu
Section
ORIGINAL RESEARCH article
Publication year
2023
Publication date
Feb 21, 2023
Publisher
Frontiers Research Foundation
ISSN
16634365
e-ISSN
16634365
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2778466666
Copyright
© 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.