Addictive brain-network identification by spatial attention recurrent network with feature selection

• Published in: Brain informatics Vol. 10; no. 1; p. 2
• Main Authors: Gong, Changwei, Chen, Xinyi, Mughal, Bushra, Wang, Shuqiang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 10.01.2023; Springer Nature B.V; SpringerOpen
• Subjects: Abnormalities; Addictions; Artificial Intelligence; Biomarkers; Brain; Brain network; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Feature extraction; Feature selection; Graph neural networks; Health Informatics; Magnetic resonance imaging; Neural imaging computing; Neurosciences; Brain network; Graph neural networks; Neural imaging computing
• ISSN: 2198-4018; 2198-4026
• DOI: 10.1186/s40708-022-00182-4

Addiction in the brain is associated with adaptive changes that reshape addiction-related brain regions and lead to functional abnormalities that cause a range of behavioral changes, and functional magnetic resonance imaging (fMRI) studies can reveal complex dynamic patterns of brain functional change. However, it is still a challenge to identify functional brain networks and discover region-level biomarkers between nicotine addiction (NA) and healthy control (HC) groups. To tackle it, we transform the fMRI of the rat brain into a network with biological attributes and propose a novel feature-selected framework to extract and select the features of addictive brain regions and identify these graph-level networks. In this framework, spatial attention recurrent network (SARN) is designed to capture the features with spatial and time-sequential information. And the Bayesian feature selection(BFS) strategy is adopted to optimize the model and improve classification tasks by restricting features. Our experiments on the addiction brain imaging dataset obtain superior identification performance and interpretable biomarkers associated with addiction-relevant brain regions.