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Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks by Yu, Yue; Kan, Xuan; Cui, Hejie; Xu, Ran; Zheng, Yujia; Song, Xiangchen; Zhu, Yanqiao; Zhang, Kun; Nabi, Razieh; Guo, Ying; Zhang, Chao; Yang, Carl is a scholarly article available to read on EtoBox.

What is Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks about?

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 ef

Author

Yu, Yue; Kan, Xuan; Cui, Hejie; Xu, Ran; Zheng, Yujia; Song, Xiangchen; Zhu, Yanqiao; Zhang, Kun; Nabi, Razieh; Guo, Ying; Zhang, Chao; Yang, Carl

Published

2022

Language

EN