Variational AutoEncoder For Regression: Application to Brain Aging Analysis.

Area of Science:

• Neuroimaging
• Machine Learning
• Probabilistic Modeling

Background:

• Unsupervised variational autoencoders (VAEs) are established in neuroimage analysis.
• Supervised applications of VAEs remain underexplored.
• Disentangled representation learning is a recent advancement.

Purpose of the Study:

• To propose a unified probabilistic model for latent space learning and supervised regression in neuroimaging.
• To integrate VAEs with supervised learning for enhanced neuroimage analysis.
• To enable interpretable analysis of brain development patterns.

Main Methods:

• Developed a novel generative process modeling conditional distribution of latent representations.
• Employed variational inference for joint regularization of VAE and neural network regressor.
• Applied the model to predict age from structural Magnetic Resonance (MR) images of 245 subjects.

Main Results:

• Achieved higher accuracy in age prediction compared to state-of-the-art methods using both ROI and 3D volume data.
• Demonstrated superior performance over simple feed-forward neural networks.
• Enabled intuitive interpretation of structural brain developmental patterns through disentangled latent representations.

Conclusions:

• The proposed unified probabilistic model effectively bridges unsupervised and supervised learning in neuroimaging.
• The model offers enhanced accuracy and interpretability for neuroimage analysis tasks like age prediction.
• Disentangled latent representations provide valuable insights into human brain development.