Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis

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 construc...

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Vydané v:Proceedings (International Symposium on Biomedical Imaging) Ročník 2023; s. 1 - 5
Hlavní autori: Yu, Yue, Kan, Xuan, Cui, Hejie, Xu, Ran, Zheng, Yujia, Song, Xiangchen, Zhu, Yanqiao, Zhang, Kun, Nabi, Razieh, Guo, Ying, Zhang, Chao, Yang, Carl
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.04.2023
Predmet:
Brain modeling
Brain Network
Direct Acyclic Graph Generation
fMRI Analysis
Functional magnetic resonance imaging
Generators
Graph Neural Network
Graph neural networks
Graphics processing units
Noise measurement
Transforms
fMRI Analysis
Brain Network
Direct Acyclic Graph Generation
Graph Neural Network
ISSN:1945-7928, 1945-8452
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Shrnutí: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 (ROIs), which are noisy and can lead to inferior results for GNN models. To better adapt GNNs for fMRI analysis, we propose DABNet, a Deep DAG learning framework based on Brain Networks for fMRI analysis. DABNet adopts a brain network generator module, which harnesses the DAG learning approach to transform the raw time-series into effective brain connectivities. Experiments on two fMRI datasets demonstrate the efficacy of DABNet. The generated brain networks also highlight the prediction-related brain regions and thus provide interpretations for predictions.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI53787.2023.10230429