A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

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Abstract

Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging the non-Euclidean topology and dynamic aspects of brain connectivity across time. In this paper, a deep spatiotemporal variational Bayes (DSVB) framework is proposed to learn time-varying topological structures in dynamic FC networks for identifying autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture rich spatiotemporal patterns across dynamic FC networks. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework substantially outperforms state-of-the-art methods in identifying patients with ASD. Dynamic FC analyses with DSVB-learned embeddings reveal apparent group differences between ASD and healthy controls in brain network connectivity patterns and switching dynamics of brain states.

Original languageEnglish
Title of host publicationProceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
EditorsKate Larson
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages5353-5361
Number of pages9
ISBN (Electronic)9781956792041
DOIs
Publication statusPublished - 2024
EventInternational Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, South
Duration: 3 Aug 20249 Aug 2024
Conference number: 33rd
https://www.ijcai.org/Proceedings/2024/ (Proceedings)
https://ijcai24.org/ (Website)

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence, IJCAI 2024
Abbreviated titleIJCAI 2024
Country/TerritoryKorea, South
CityJeju
Period3/08/249/08/24
Internet address

Keywords

  • Machine Learning
  • Learning graphical models
  • Probabilistic machine learning
  • Adversarial machine learning
  • Multidisciplinary Topics and Applications
  • Health and medicine

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