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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.<\/jats:p>","DOI":"10.1186\/s40708-022-00182-4","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T10:05:14Z","timestamp":1673345114000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Addictive brain-network identification by spatial attention recurrent network with feature selection"],"prefix":"10.1186","volume":"10","author":[{"given":"Changwei","family":"Gong","sequence":"first","affiliation":[]},{"given":"Xinyi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Bushra","family":"Mughal","sequence":"additional","affiliation":[]},{"given":"Shuqiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"issue":"3","key":"182_CR1","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E Bullmore","year":"2009","unstructured":"Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. 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