Li Zihan, Li Jun, Li Jiahui, Wang Mengying, Xu Andi, Huang Yushu, Yu Qi, Zhang Lingzhi, Li Yingjun, Li Zilin, Wu Xifeng, Bu Jiajun, Li Wenyuan
Center for Clinical Big Data and Analytics, The Second Affiliated Hospital and Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
Department of Radiology, The First Affiliated Hospital, Zhengzhou University, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
JMIR Aging. 2025 Aug 1;8:e73004. doi: 10.2196/73004.
Precise assessment of brain aging is crucial for early detection of neurodegenerative disorders and aiding clinical practice. Existing magnetic resonance imaging (MRI)-based methods excel in this task, but they still have room for improvement in capturing local morphological variations across brain regions and preserving the inherent neurobiological topological structures.
To develop and validate a deep learning framework incorporating both connectivity and complexity for accurate brain aging estimation, facilitating early identification of neurodegenerative diseases.
We used 5889 T1-weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative dataset. We proposed a novel brain vision graph neural network (BVGN), incorporating neurobiologically informed feature extraction modules and global association mechanisms to provide a sensitive deep learning-based imaging biomarker. Model performance was evaluated using mean absolute error (MAE) against benchmark models, while generalization capability was further validated on an external UK Biobank dataset. We calculated the brain age gap across distinct cognitive states and conducted multiple logistic regressions to compare its discriminative capacity against conventional cognitive-related variables in distinguishing cognitively normal (CN) and mild cognitive impairment (MCI) states. Longitudinal track, Cox regression, and Kaplan-Meier plots were used to investigate the longitudinal performance of the brain age gap.
The BVGN model achieved an MAE of 2.39 years, surpassing current state-of-the-art approaches while obtaining an interpretable saliency map and graph theory supported by medical evidence. Furthermore, its performance was validated on the UK Biobank cohort (N=34,352) with an MAE of 2.49 years. The brain age gap derived from BVGN exhibited significant difference across cognitive states (CN vs MCI vs Alzheimer disease; P<.001), and demonstrated the highest discriminative capacity between CN and MCI than general cognitive assessments, brain volume features, and apolipoprotein E4 carriage (area under the receiver operating characteristic curve [AUC] of 0.885 vs AUC ranging from 0.646 to 0.815). Brain age gap exhibited clinical feasibility combined with Functional Activities Questionnaire, with improved discriminative capacity in models achieving lower MAEs (AUC of 0.945 vs 0.923 and 0.911; AUC of 0.935 vs 0.900 and 0.881). An increasing brain age gap identified by BVGN may indicate underlying pathological changes in the CN to MCI progression, with each unit increase linked to a 55% (hazard ratio=1.55, 95% CI 1.13-2.13; P=.006) higher risk of cognitive decline in individuals who are CN and a 29% (hazard ratio=1.29, 95% CI 1.09-1.51; P=.002) increase in individuals with MCI.
BVGN offers a precise framework for brain aging assessment, demonstrates strong generalization on an external large-scale dataset, and proposes novel interpretability strategies to elucidate multiregional cooperative aging patterns. The brain age gap derived from BVGN is validated as a sensitive biomarker for early identification of MCI and predicting cognitive decline, offering substantial potential for clinical applications.
精确评估脑老化对于神经退行性疾病的早期检测及辅助临床实践至关重要。现有的基于磁共振成像（MRI）的方法在这项任务中表现出色，但在捕捉脑区局部形态变化和保留内在神经生物学拓扑结构方面仍有改进空间。
开发并验证一个融合连通性和复杂性的深度学习框架，用于准确估计脑老化，以促进神经退行性疾病的早期识别。
我们使用了来自阿尔茨海默病神经成像计划数据集的5889例T1加权MRI扫描。我们提出了一种新型的脑视觉图神经网络（BVGN），它结合了基于神经生物学的特征提取模块和全局关联机制，以提供一种基于深度学习的敏感成像生物标志物。使用平均绝对误差（MAE）相对于基准模型评估模型性能，同时在外部英国生物银行数据集上进一步验证泛化能力。我们计算了不同认知状态下的脑年龄差距，并进行了多项逻辑回归，以比较其在区分认知正常（CN）和轻度认知障碍（MCI）状态时相对于传统认知相关变量的判别能力。使用纵向轨迹、Cox回归和Kaplan-Meier图来研究脑年龄差距的纵向表现。
BVGN模型的MAE为2.39岁，超过了当前的先进方法，同时获得了一个可解释的显著性图和得到医学证据支持的图论。此外，其性能在英国生物银行队列（N = 34,352）上得到验证，MAE为2.49岁。源自BVGN的脑年龄差距在不同认知状态（CN与MCI与阿尔茨海默病；P <.001）之间表现出显著差异，并且在区分CN和MCI方面比一般认知评估、脑容量特征和载脂蛋白E4携带情况具有更高的判别能力（受试者操作特征曲线下面积[AUC]为0.885，而AUC范围为0.646至0.815）。脑年龄差距与功能活动问卷相结合显示出临床可行性，在MAE较低的模型中判别能力有所提高（AUC为0.945，而分别为0.923和0.911；AUC为0.935，而分别为0.900和0.881）。BVGN识别出的脑年龄差距增加可能表明在从CN到MCI进展过程中的潜在病理变化，每增加一个单位与CN个体认知下降风险高55%（风险比 = 1.55，95%可信区间1.13 - 2.13；P =.006）以及MCI个体增加29%（风险比 = 1.29，95%可信区间1.09 - 1.51；P =.002）相关。
BVGN为脑老化评估提供了一个精确的框架，在外部大规模数据集上表现出强大的泛化能力，并提出了新颖的可解释性策略以阐明多区域协同老化模式。源自BVGN的脑年龄差距被验证为早期识别MCI和预测认知下降的敏感生物标志物，具有巨大的临床应用潜力。
