Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks

TL;DR

This paper introduces a novel deep learning model called Spatial-Temporal Convolutional Attention (STCA) that dynamically maps functional brain networks from fMRI data, emphasizing spatial and temporal features for improved brain activity analysis.

Contribution

The study presents a new STCA model that leverages spatial-wise attention and sliding windows to discover dynamic FBNs, advancing beyond traditional correlation-based methods.

Findings

STCA outperforms classical methods in spatial similarity with templates.

The model effectively captures the dynamics of brain functional networks.

Results demonstrate improved understanding of human brain activity.

Abstract

Using functional magnetic resonance imaging (fMRI) and deep learning to explore functional brain networks (FBNs) has attracted many researchers. However, most of these studies are still based on the temporal correlation between the sources and voxel signals, and lack of researches on the dynamics of brain function. Due to the widespread local correlations in the volumes, FBNs can be generated directly in the spatial domain in a self-supervised manner by using spatial-wise attention (SA), and the resulting FBNs has a higher spatial similarity with templates compared to the classical method. Therefore, we proposed a novel Spatial-Temporal Convolutional Attention (STCA) model to discover the dynamic FBNs by using the sliding windows. To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset. The results indicate that STCA can be used to discover FBNs in a dynamic way which provide a novel approach to better understand human brain.