Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis

TL;DR

This paper introduces DART, a novel transformer-based model that combines static and dynamic brain networks with attention mechanisms to improve functional brain network analysis and interpretability.

Contribution

It proposes a new dynamic brain network analysis method integrating static and dynamic data using attention, enhancing performance and explainability over traditional static approaches.

Findings

DART outperforms traditional static methods in brain network analysis.

The model provides insights into brain circuit contributions to predictions.

Attention mechanisms improve model interpretability.

Abstract

Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural Networks has fostered a substantial interest in predicting clinical outcomes and categorizing individuals based on brain networks. However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function. Although recent studies have attempted to harness dynamic brain networks, their high dimensionality and complexity present substantial challenges. This paper proposes a novel methodology, Dynamic bRAin Transformer (DART), which combines static and dynamic brain networks for more effective and nuanced brain function analysis. Our model uses the static brain network as a baseline, integrating dynamic brain networks to enhance performance against traditional methods. We innovatively employ attention mechanisms, enhancing model explainability and exploiting the dynamic brain network's temporal variations. The proposed approach offers a robust solution to the low signal-to-noise ratio of blood-oxygen-level-dependent signals, a recurring issue in direct DNN modeling. It also provides valuable insights into which brain circuits or dynamic networks contribute more to final predictions. As such, DRAT shows a promising direction in neuroimaging studies, contributing to the comprehensive understanding of brain organization and the role of neural circuits.