1. BS-GAENets: Brain-Spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis
- Author
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Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
- Subjects
Multi-modal MRI ,Graph deep representation learning ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,[INFO]Computer Science [cs] ,[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] ,Spatial-cerebral features ,Regression ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
International audience; The obsession with how the brain and behavior are related is a challenge for cognitive neuroscience research, for which functional magnetic resonance imaging (fMRI) has significantly improved our understanding of brain functions and dysfunctions. In this paper, we propose a novel multi-modal spatial cerebral graph based on an attention mechanism called MSCGATE that combines both fMRI modalities: task-, and rest-fMRI based on spatial and cerebral features to preserve the rich complex structure between brain voxels. Moreover, it attempts to project the structural-functional brain connections into a new multi-modal latent representation space, which will subsequently be inputted to our trace regression predictive model to output each subject’s behavioral score. Experiments on the InterTVA dataset reveal that our proposed approach outperforms other graph representation learning-based models, in terms of effectiveness and performance.
- Published
- 2023
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