Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

TL;DR

This paper introduces TBDS, a novel framework using task-aware DAG-based brain connectivity graphs generated by GNNs for improved fMRI analysis, providing both enhanced prediction accuracy and interpretability.

Contribution

It proposes a task-aware brain network generator with DAG learning and contrastive regularization, tailored for GNN-based fMRI analysis, which improves prediction and interpretability.

Findings

TBDS outperforms baseline models on ABCD and PNC datasets.

Generated brain networks highlight task-related brain regions.

The approach offers interpretable insights into brain connectivity.

Abstract

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopted for fMRI analysis with superior performance. Unfortunately, traditional functional brain networks are mainly constructed based on similarities among region of interests (ROI), which are noisy and agnostic to the downstream prediction tasks and can lead to inferior results for GNN-based models. To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis. The key component of TBDS is the brain network generator which adopts a DAG learning approach to transform the raw time-series into task-aware brain connectivities. Besides, we design an additional contrastive regularization to inject task-specific knowledge during the brain network generation process. Comprehensive experiments on two fMRI datasets, namely Adolescent Brain Cognitive Development (ABCD) and Philadelphia Neuroimaging Cohort (PNC) datasets demonstrate the efficacy of TBDS. In addition, the generated brain networks also highlight the prediction-related brain regions and thus provide unique interpretations of the prediction results. Our implementation will be published to https://github.com/yueyu1030/TBDS upon acceptance.