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CORRECTION article

Front. Neurol., 07 October 2025

Sec. Applied Neuroimaging

Volume 16 - 2025 | https://doi.org/10.3389/fneur.2025.1712929

Correction: A predictive model of Parkinsonian brain aging based on brain imaging features

  • 1. First Clinical Medical College, Jinan University, Guangzhou, China

  • 2. Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China

  • 3. Health Management Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China

  • 4. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China

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In the published article, the statement “These authors have contributed equally to this work” was erroneously not added for authors Xiaoyan Zhou and Haoyong Zhu.

The original version of this article has been updated.

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Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Summary

Keywords

machine learning, structure MRI, brain age, Parkinson's disease, shapley additive explanations

Citation

Zhou X, Zhu H, Wang X and Gao Q (2025) Correction: A predictive model of Parkinsonian brain aging based on brain imaging features. Front. Neurol. 16:1712929. doi: 10.3389/fneur.2025.1712929

Received

25 September 2025

Accepted

26 September 2025

Published

07 October 2025

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Frontiers Editorial Office, Frontiers Media SA, Switzerland

Volume

16 - 2025

Updates

Copyright

*Correspondence: Xiaoming Wang Qing Gao

†These authors have contributed equally to this work

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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