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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Vydáno v:2022 30th European Signal Processing Conference (EUSIPCO) s. 922 - 926
Hlavní autoři: Li, Yang, Mateos, Gonzalo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: EUSIPCO 29.08.2022
Témata:
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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Shrnutí: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 framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system. Specifically, we propose a Siamese network architecture equipped with graph convolutional encoders to learn graph (i.e., subject)-level embeddings that preserve application-dependent similarity measures between brain networks. This way, we effectively increase the number of training samples and bring in the flexibility to incorporate additional prior information via the prescribed target graph-level distance. While information on the brain structure-function coupling is implicitly distilled via reconstruction of brain FC from SC, our model also manages to learn representations that preserve the similarity between input graphs. The superior discriminative power of the learnt representations is demonstrated in downstream tasks including subject classification and visualization. All in all, this work advocates the prospect of leveraging learnt graph-level, similarity-preserving embeddings for brain network analysis, by bringing to bear standard tools of metric data analysis.
ISSN:2076-1465
DOI:10.23919/EUSIPCO55093.2022.9909566