Learning Similarity-Preserving Representations of Brain Structure-Function Coupling

Advances in graph signal processing for network neuroscience offer a unique pathway to integrate brain structure and function, with the goal of revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning frame...

Full description

Bibliographic Details

Published in:	2022 30th European Signal Processing Conference (EUSIPCO) pp. 922 - 926
Main Authors:	Li, Yang, Mateos, Gonzalo
Format:	Conference Proceeding
Language:	English
Published:	EUSIPCO 29.08.2022
Subjects:	Brain connectomics Convolution Couplings graph convolutional network graph representation learning Network analyzers Pipelines Representation learning Siamese network Training Training data
ISSN:	2076-1465
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!