Inter-regional High-Level Relation Learning from Functional Connectivity via Self-supervision

Wonsik Jung, Da Woon Heo, Eunjin Jeon, Jaein Lee, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In recent studies, we have witnessed the applicability of deep learning methods on resting-state functional magnetic resonance image (rs-fMRI) analysis and its use for brain disease diagnosis, e.g., autism spectrum disorder (ASD). However, it still remains challenging to learn discriminative representations from raw BOLD signals or functional connectivity (FC) with a limited number of samples. In this paper, we propose a simple but efficient representation learning method for FC in a self-supervised learning manner. Specifically, we devise a proxy task of estimating the randomly masked seed-based functional networks from the remaining ones in FC, to discover the complex high-level relations among brain regions, which are not directly observable from an input FC. Thanks to the random masking strategy in our proxy task, it also has the effect of augmenting training samples, thus allowing for robust training. With the pretrained feature representation network in a self-supervised manner, we then construct a decision network for the downstream task of ASD diagnosis. In order to validate the effectiveness of our proposed method, we used the ABIDE dataset that collected subjects from multiple sites and our proposed method showed superiority to the comparative methods in various metrics.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030871956
Publication statusPublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 2021 Sept 272021 Oct 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Autism spectrum disorder
  • Deep learning
  • Multi-site fMRI
  • Representation learning
  • Resting-state functional magnetic resonance imaging
  • Self-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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