Learning-Disability Recognition by Using Sparse Spatio-Temporal Graph Neural Networks

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

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Abstract

This paper proposes a representation learning model for identifying learning disability in resting-state fMRI, with the potential to enhance our understanding of the human brain. The traditional GNN model is used to learn graph representation from fMRI but neglects to consider side information and sparsity. In this paper, we introduce Sparse Spatio-Temporal Graph Neural Networks (SSTGNN) for brain image representation. Specifically, SSTGNN consists of four parts: The ROI Feature Encoder aims to learn temporal ROI features from fMRI, then generate sparse spatial-temporal graphs based on the encoded features, employ GNN for brain image classification, and introduce side information regularization to narrow the gap between the generated graph and prior information. Our model is trained to minimize the cross-entropy (CE) loss. We conducted experiments on the publicly available Autism Brain Imaging Data Exchange dataset. The results demonstrate that the proposed SSTGNN benefits from the introduced side information regularization and sparsity, leading to improved performance in brain classification. This study not only presents an effective fMRI classification model but also has the potential to deepen our understanding of brain intelligence and assist patients with learning disabilities.

Original languageEnglish
Title of host publicationProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
EditorsXingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3521-3528
Number of pages8
ISBN (Electronic)9798350337488
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey
Duration: 5 Dec 20238 Dec 2023

Publication series

NameProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/TerritoryTurkey
CityIstanbul
Period5/12/238/12/23

Keywords

  • Brain-network classification
  • Learning disability
  • Resting-state fMRIs
  • Spatio-temporal GNN

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