While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.

Filip, A., Azevedo, T., Passamonti, L., Toschi, N., Lio, P. (2020). A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.1071-1074). IEEE.

A novel Graph Attention Network Architecture for modeling multimodal brain connectivity

Toschi, N;
2020-01-01

Abstract

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.
Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC
Montreal (Canada)
42
Rilevanza internazionale
2020
Settore FIS/07 - FISICA APPLICATA (A BENI CULTURALI, AMBIENTALI, BIOLOGIA E MEDICINA)
English
Intervento a convegno
Filip, A., Azevedo, T., Passamonti, L., Toschi, N., Lio, P. (2020). A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.1071-1074). IEEE.
Filip, A; Azevedo, T; Passamonti, L; Toschi, N; Lio, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/278388
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