Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging

TL;DR

This paper introduces a novel deep learning framework that combines multimodal MRI data and multitask learning to improve brain age estimation, effectively capturing shared and modality-specific features for more accurate predictions.

Contribution

The proposed Multitask Adversarial Variational Autoencoder effectively integrates structural and functional MRI data with sex classification, enhancing brain age prediction accuracy.

Findings

Achieved a mean absolute error of 2.77 years on the OpenBHB dataset.

Outperformed traditional brain age estimation methods.

Effectively captured sex-specific aging patterns.

Abstract

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.