A Graph-Based Generative Adversarial Network Model for Inferring Task-State from Resting-State Functional Connectivity Networks

Resting-state functional connectivity networks (rs-FCNs) have been most frequently used for brain network analysis in neuroscience. However, a body of evidence indicates that task-state FCNs (ts-FCNs) are better associated with individual differences in behavior than rs-FCN. Until now there have bee...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 1 - 5
Hauptverfasser: Jin, Tao, Guan, Hongzheng, Xiao, Li, Qu, Gang, Wang, Yu-Ping
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 06.04.2025
Schlagworte:
Autoencoders
Convolutional neural networks
Correlation
functional connectivity
generative adversarial network
Generative adversarial networks
Generators
graph convolutional network
Measurement
Network analyzers
Neuroscience
Signal processing
Speech processing
ISSN:2379-190X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Resting-state functional connectivity networks (rs-FCNs) have been most frequently used for brain network analysis in neuroscience. However, a body of evidence indicates that task-state FCNs (ts-FCNs) are better associated with individual differences in behavior than rs-FCN. Until now there have been no studies of ascertaining to what extent rs-FCNs can account for ts-FCNs. In this paper, we propose a Multiple Graph Autoencoder based Generative Adversarial Network (MGAE-GAN) model to enable the inference of ts-FCNs from rs-FCNs. The generator of MGAE-GAN is built upon several graph autoencoders to learn and adaptively combine multiple implicit relationships between rs-FCNs and ts-FCNs. To ensure the authenticity of the predicted ts-FCNs, we design the discriminator of MGAE-GAN based on graph metric learning. Additionally, we incorporate a correlation loss and a subject-similarity-preserving loss to maintain overall correlation and between-subject similarities before and after the generator, respectively. Experimental results on the Human Connectome Project (HCP) S1200 demonstrate the effectiveness of our MGAE-GAN for predicting ts-FCNs from rs-FCNs.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889206