Neural Autoregressive Modeling of Brain Aging

TL;DR

NeuroAR is a novel autoregressive transformer model that synthesizes realistic brain aging trajectories from MRI scans, outperforming existing generative models in image fidelity and age consistency validation.

Contribution

The paper introduces NeuroAR, a new autoregressive transformer-based model for brain aging synthesis that effectively captures subject-specific aging patterns.

Findings

NeuroAR outperforms state-of-the-art models like LDM and GANs in image fidelity.

The model produces realistic aging trajectories validated by an age predictor.

NeuroAR demonstrates superior modeling of subject-specific brain aging.

Abstract

Brain aging synthesis is a critical task with broad applications in clinical and computational neuroscience. The ability to predict the future structural evolution of a subject's brain from an earlier MRI scan provides valuable insights into aging trajectories. Yet, the high-dimensionality of data, subtle changes of structure across ages, and subject-specific patterns constitute challenges in the synthesis of the aging brain. To overcome these challenges, we propose NeuroAR, a novel brain aging simulation model based on generative autoregressive transformers. NeuroAR synthesizes the aging brain by autoregressively estimating the discrete token maps of a future scan from a convenient space of concatenated token embeddings of a previous and future scan. To guide the generation, it concatenates into each scale the subject's previous scan, and uses its acquisition age and the target age at each block via cross-attention. We evaluate our approach on both the elderly population and adolescent subjects, demonstrating superior performance over state-of-the-art generative models, including latent diffusion models (LDM) and generative adversarial networks, in terms of image fidelity. Furthermore, we employ a pre-trained age predictor to further validate the consistency and realism of the synthesized images with respect to expected aging patterns. NeuroAR significantly outperforms key models, including LDM, demonstrating its ability to model subject-specific brain aging trajectories with high fidelity.