Embedding Human Brain Function via Transformer

BOLD fMRI has been an established tool for studying the human brain's functional organization. Considering the high dimensionality of fMRI data, various computational techniques have been developed to perform the dimension reduction such as independent component analysis (ICA) or sparse dictionary learning (SDL). These methods decompose the fMRI as compact functional brain networks, and then build the correspondence of those brain networks across individuals by viewing the brain networks as one-hot vectors and performing their matching. However, these one-hot vectors do not encode the regularity and variability of different brains, and thus cannot effectively represent the functional brain activities in different brains and at different time points. To bridge the gaps, in this paper, we propose a novel unsupervised embedding framework based on Transformer to encode the brain function in a compact, stereotyped and comparable latent space where the brain activities are represented as dense embedding vectors. The framework is evaluated on the publicly available Human Connectome Project (HCP) task based fMRI dataset. The experiment on brain state prediction downstream task indicates the effectiveness and generalizability of the learned embeddings. We also explore the interpretability of the embedding vectors and achieve promising result. In general, our approach provides novel insights on representing regularity and variability of human brain function in a general, comparable, and stereotyped latent space.