Name
Title | ||
|---|---|---|
A Novel Graph Attention Network Architecture For Modeling Multimodal Brain Connectivity |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176613 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandru-Catalin Filip | 1 | 0 | 0.34 |
| Tiago Azevedo | 2 | 0 | 0.68 |
| Luca Passamonti | 3 | 43 | 11.28 |
| nicola toschi | 4 | 36 | 15.57 |
| Pietro Liò | 5 | 550 | 99.98 |