基于多视角MRI的可解释脑龄预测模型，跨人群表现稳定。
OpenMAP-BrainAge: Generalizable and Interpretable Brain Age Predictor
- 用Transformer融合三视图和体积信息，线性复杂度提升效率。
- 在多个数据集上误差低于3.7年，认知障碍患者脑龄差距显著增大。
- 通过梯度归因揭示脑室和白质是老化关键区域，适合神经退行研究。
目的：开发一种对人口统计与技术差异鲁棒、可解释的脑龄预测模型。方法：提出基于Transformer的架构，利用大规模数据自监督预训练；处理来自三个解剖视角的伪3D T1加权MRI，并融合脑体积信息。通过引入茎干结构，将传统Transformer的二次复杂度降至线性，实现高维MRI数据的可扩展性。模型在北美ADNI2&3（N=1348）和OASIS3（N=716）数据集（年龄范围：42-95岁）上训练，按8:1:1划分训练/验证/测试集。随后在澳大利亚AIBL数据集（N=768，年龄范围：60-92岁）上验证。结果：在ADNI2&3和OASIS3测试集上达到3.65年平均绝对误差（MAE），在AIBL上保持3.54年高泛化性能。各认知组脑龄差距（BAG）显著增加：对照组（CN）均值0.15年（95%置信区间：[-0.22, 0.51]），轻度认知障碍（MCI）为2.55年（[2.40, 2.70]），阿尔茨海默病（AD）为6.12年（[5.82, 6.43]）。脑龄差距与认知评分呈显著负相关，MoCA相关系数-0.185（p<0.001），MMSE为-0.231（p<0.001）。梯度特征归因显示脑室和白质结构是老化影响的关键区域。结论：该模型有效融合多视角与体积信息，实现领先水平的脑龄预测精度、更强泛化能力与可解释性，并与神经退行性疾病相关联。
原文摘要 · Abstract (English)
Purpose: To develop an age prediction model which is interpretable and robust to demographic and technological variances in brain MRI scans. Materials and Methods: We propose a transformer-based architecture that leverages self-supervised pre-training on large-scale datasets. Our model processes pseudo-3D T1-weighted MRI scans from three anatomical views and incorporates brain volumetric information. By introducing a stem architecture, we reduce the conventional quadratic complexity of transformer models to linear complexity, enabling scalability for high-dimensional MRI data. We trained our model on ADNI2 $\&$ 3 (N=1348) and OASIS3 (N=716) datasets (age range: 42 - 95) from the North America, with an 8:1:1 split for train, validation and test. Then, we validated it on the AIBL dataset (N=768, age range: 60 - 92) from Australia. Results: We achieved an MAE of 3.65 years on ADNI2 $\&$ 3 and OASIS3 test set and a high generalizability of MAE of 3.54 years on AIBL. There was a notable increase in brain age gap (BAG) across cognitive groups, with mean of 0.15 years (95% CI: [-0.22, 0.51]) in CN, 2.55 years ([2.40, 2.70]) in MCI, 6.12 years ([5.82, 6.43]) in AD. Additionally, significant negative correlation between BAG and cognitive scores was observed, with correlation coefficient of -0.185 (p < 0.001) for MoCA and -0.231 (p < 0.001) for MMSE. Gradient-based feature attribution highlighted ventricles and white matter structures as key regions influenced by brain aging. Conclusion: Our model effectively fused information from different views and volumetric information to achieve state-of-the-art brain age prediction accuracy, improved generalizability and interpretability with association to neurodegenerative disorders.
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