Graph autoencoder-based embedded learning in dynamic brain networks for autism spectrum disorder identification

Fuad Noman, Sin Yee Yap, Raphaël C.W. Phan, Hernando Ombao, Chee Ming Ting

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Recent applications of pattern recognition techniques to brain connectome-based classification focus on static functional connectivity (FC) neglecting the dynamics of FC over time, and use input connectivity matrices on a regular Euclidean grid. We exploit the graph convolutional networks (GCNs) to learn irregular structural patterns in brain FC networks and propose extensions to capture dynamic changes in network topology. We develop a dynamic graph autoencoder (DyGAE)-based framework to leverage the time-varying topological structures of dynamic brain networks for identification of autism spectrum disorder (ASD). The framework combines a GCN-based DyGAE to encode individual-level dynamic networks into time-varying low-dimensional network embeddings, and classifiers based on weighted fully-connected neural network (FCNN) and long short-term memory (LSTM) to facilitate dynamic graph classification via the learned spatial-temporal information. Evaluation on a large ABIDE resting-state functional magnetic resonance imaging (rs-fMRI) dataset shows that our method outperformed state-of-the-art methods in detecting altered FC in ASD. Dynamic FC analyses with DyGAE learned embeddings also reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing - Proceedings
EditorsGiuseppe Valenzise, Thomas Maugey
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2891-2895
Number of pages5
ISBN (Electronic)9781665496209
ISBN (Print)9781665496216
DOIs
Publication statusPublished - 2022
EventIEEE International Conference on Image Processing 2022 - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022
Conference number: 29th
https://ieeexplore.ieee.org/xpl/conhome/9897158/proceeding (Proceedings)

Publication series

NameProceedings - International Conference on Image Processing, ICIP
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing 2022
Abbreviated titleICIP 2022
Country/TerritoryFrance
CityBordeaux
Period16/10/2219/10/22
Internet address

Keywords

  • Brain connectivity networks
  • graph autoencoder
  • graph convolutional network
  • resting-state fMRI

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