Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?

TL;DR

This paper introduces TABLeT, a novel method that uses a pre-trained 2D autoencoder to tokenize fMRI volumes, enabling efficient long-range spatiotemporal modeling with Transformers and demonstrating superior performance on large-scale brain imaging datasets.

Contribution

The paper presents a new approach that compresses 3D fMRI data into tokens using a 2D autoencoder, allowing scalable long-range modeling with Transformers and improved efficiency.

Findings

Outperforms existing models on UKB, HCP, and ADHD-200 datasets.

Achieves better computational and memory efficiency than voxel-based methods.

Self-supervised masked token pre-training enhances downstream task performance.

Abstract

Modeling long-range spatiotemporal dynamics in functional Magnetic Resonance Imaging (fMRI) remains a key challenge due to the high dimensionality of the four-dimensional signals. Prior voxel-based models, although demonstrating excellent performance and interpretation capabilities, are constrained by prohibitive memory demands and thus can only capture limited temporal windows. To address this, we propose TABLeT (Two-dimensionally Autoencoded Brain Latent Transformer), a novel approach that tokenizes fMRI volumes using a pre-trained 2D natural image autoencoder. Each 3D fMRI volume is compressed into a compact set of continuous tokens, enabling long-sequence modeling with a simple Transformer encoder with limited VRAM. Across large-scale benchmarks including the UK-Biobank (UKB), Human Connectome Project (HCP), and ADHD-200 datasets, TABLeT outperforms existing models in multiple tasks, while demonstrating substantial gains in computational and memory efficiency over the state-of-the-art voxel-based method given the same input. Furthermore, we develop a self-supervised masked token modeling approach to pre-train TABLeT, which improves the model's performance for various downstream tasks. Our findings suggest a promising approach for scalable and interpretable spatiotemporal modeling of brain activity. Our code is available at https://github.com/beotborry/TABLeT.