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

arXiv: https://arxiv.org/abs/2411.10100 · HTML: https://mlsanity.com/paper/2411.10100/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Relief-Based Feature Selection: Introduction and Review](https://mlsanity.com/paper/1711.08421/) (2017)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [Learn to Combine Modalities in Multimodal Deep Learning](https://mlsanity.com/paper/1805.11730/) (2018)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection](https://mlsanity.com/paper/1801.08322/) (2018)
- [DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI](https://mlsanity.com/paper/2307.09547/) (2023)
- [Deep Learning for Brain Age Estimation: A Systematic Review](https://mlsanity.com/paper/2212.03868/) (2022)
- [Deep Relation Learning for Regression and Its Application to Brain Age Estimation](https://mlsanity.com/paper/2204.06598/) (2022)
- [Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning](https://mlsanity.com/paper/1912.13335/) (2019)
- [Using Deep Autoencoders for Facial Expression Recognition](https://mlsanity.com/paper/1801.08329/) (2018)
- [Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans](https://mlsanity.com/paper/2210.03739/) (2022)
- [MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan](https://mlsanity.com/paper/2304.01576/) (2023)

## Abstract (arXiv)

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.