ORIGINAL RESEARCH article
Front. Neurol.
Sec. Applied Neuroimaging
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1584226
This article is part of the Research TopicFrontier Research on Artificial Intelligence and Radiomics in Neurodegenerative DiseasesView all 17 articles
A predictive model of Parkinsonian brain aging based on brain imaging features
Provisionally accepted
- 1North Sichuan Medical College, Nanchong, Sichuan Province, China
- 2University of Electronic Science and Technology of China, Chengdu, China
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This study explores the use of imaging to evaluate brain aging to establish a model for predicting brain age in patients with Parkinson's disease. Structural brain MRI data from 345 healthy individuals were obtained from the IXI database, while data from 59 Parkinson's patients and 59 healthy controls were acquired from the PPMI database. A total of 1214 structural indicators were extracted, including information on the whole brain, cortex, subcortex, and white matter. This led to the development of a model for predicting brain age in Parkinson's patients. The model combined brain imaging features with a machine learning algorithm and the Shapley Additive Explanations (SHAP) interpretation model. Fifteen characteristic indicators most closely associated with Parkinson's brain aging were determined. The results showed that the XGBoost model + SHAP method framework, using the minimum mean absolute error for assessing brain aging within 4.21 years, was effective in predicting brain age in patients with Parkinson's disease. The superior temporal folding index and subcortical gray matter volume, left thalamus volume, and left and right vascular volumes had the most significant impact on the prediction results, suggesting their potential as clinical indicators for evaluating the extent of brain aging in Parkinson's patients. These findings provide important clues for understanding the mechanisms underlying brain aging, as well as brain imaging evidence for the early diagnosis and treatment of Parkinson's disease.
Keywords: machine learning, Structure MRI, Brain age, Parkinson's disease, Shapley additive explanations
Received: 27 Feb 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Zhou, Zhu, Wang and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaoming Wang, North Sichuan Medical College, Nanchong, Sichuan Province, China
Qing Gao, University of Electronic Science and Technology of China, Chengdu, China
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