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...

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Vydané v:	2022 30th European Signal Processing Conference (EUSIPCO) s. 922 - 926
Hlavní autori:	Li, Yang, Mateos, Gonzalo
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	EUSIPCO 29.08.2022
Predmet:	Brain connectomics Convolution Couplings graph convolutional network graph representation learning Network analyzers Pipelines Representation learning Siamese network Training Training data
ISSN:	2076-1465
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