Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging
This work addresses the problem of noisy functional MRI data integration for brain age estimation, which is incremental as it builds on existing deep learning approaches with a novel multimodal framework.
The paper tackled the challenge of estimating biological brain age from multimodal neuroimaging data, particularly integrating functional MRI with structural MRI, and achieved a mean absolute error of 2.77 years on the OpenBHB dataset, outperforming traditional methods.
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.