融合结构与功能核磁数据，用多任务对抗自编码器更准预测脑龄。
Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging
- 分离共享与模态特异性特征，提升多模态数据融合效果。
- 在OpenBHB数据集上实现2.77年平均绝对误差，优于传统方法。
- 支持性别差异分析，适合脑老化研究与数字健康应用。
尽管深度学习在基于结构MRI预测脑龄方面取得进展，但功能MRI因结构复杂且功能连接测量噪声大，整合难度高。为此，我们提出多任务对抗变分自编码器（M-AVAE），通过将潜在变量分解为通用与特定代码，分离共享与模态特异性特征，结合性别分类作为附加任务，捕捉性别相关的衰老模式。在包含多个站点的OpenBHB大规模脑部MRI数据集上评估，该模型实现2.77年的平均绝对误差，优于传统方法。此成果使M-AVAE成为元宇宙医疗中脑龄估计的强大工具。
原文摘要 · Abstract (English)
Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements. To address this, we present the Multitask Adversarial Variational Autoencoder, a custom deep learning framework designed to improve brain age predictions through multimodal MRI data integration. This model separates latent variables into generic and unique codes, isolating shared and modality-specific features. By integrating multitask learning with sex classification as an additional task, the model captures sex-specific aging patterns. Evaluated on the OpenBHB dataset, a large multisite brain MRI collection, the model achieves a mean absolute error of 2.77 years, outperforming traditional methods. This success positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation.
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