ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1668993
This article is part of the Research TopicResearch on the Correlative Mechanisms and Clinical Exploration of Headache and Cerebrovascular DiseasesView all 3 articles
Accelerated Brain Age in Moyamoya Disease Patients: A Deep Learning Approach and Correlation with Disease Severity
Provisionally accepted
Wenjie Li1Suhua Chen1Xin Chen1Xiangtian Ji1Huan Zhu2Qihang Zhang2
Chenyu Zhu2
Tao Wang1
Yan Zhang2*
Jun Yang1*- 1Department of Neurosurgery, Peking University Third Hospital, Beijing, China
- 2Beijing Tiantan Hospital Department of Neurosurgery, Beijing, China
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Objective This study aims to utilize a DenseNet based deep learning framework to predict brain age in patients with Moyamoya disease (MMD), examining the relationship between brain age and disease severity to enhance diagnostic and prognostic capabilities. Methods We analyzed unenhanced MRI scans from 432 adult MMD patients and 565 normal controls collected between January 2018 and December 2022. Data preprocessing involved converting DICOM files to NIFTI format and labeling based on established diagnostic criteria. A DenseNet121 architecture, implemented using PyTorch, was employed to predict brain age. Statistical analyses included correlation assessments and comparisons between predicted brain age, chronological age, and MRA scores. Results The predicted brain age for MMD patients was significantly higher than their chronological age, averaging 37.9 years versus 35.8 years (p < 0.01). For normal controls, predicted brain age matched chronological age at 36.5 years. Delta age (difference between predicted brain age and chronological age) was significantly elevated in MMD patients (p < 0.001) and positively correlated with MRA scores, indicating a link between arterial stenosis severity and accelerated brain aging. Conclusions Accelerated Brain Aging in MMD The DenseNet based model effectively predicts brain age, revealing that MMD patients experience accelerated brain aging correlated with disease severity. These findings highlight the potential of brain age prediction as a biomarker for MMD, aiding in personalized treatment strategies and early intervention. Future research should explore multi-center datasets and longitudinal data to validate and extend these findings.
Keywords: Moyamoya Disease, Brain age, DenseNet architecture, deep learning, Magneticresonance imaging
Received: 18 Jul 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Li, Chen, Chen, Ji, Zhu, Zhang, Zhu, Wang, Zhang and Yang. 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:
Yan Zhang, Beijing Tiantan Hospital Department of Neurosurgery, Beijing, China
Jun Yang, Department of Neurosurgery, Peking University Third Hospital, Beijing, China
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.