Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders

Xin Ma, Guorong Wu, Seong Jae Hwang, Won Hwa Kim

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

1 Citation (Scopus)

Abstract

Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant efforts made for graph neural network (GNN) techniques, their focus on graph nodes makes the state-of-the-art GNN methods not suitable for classifying brain connectivity as graphs where the objective is to characterize disease-relevant network dysfunction patterns on graph links. To address this issue, we propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks with high discrimination power across diagnostic categories. The core of MENET is a novel graph edge-wise transform that we propose, which allows us to capture multi-resolution “connectomic” features. Using a rich set of the connectomic features, we devise a graph learning framework to jointly select discriminative edges and assign diagnostic labels for graphs. Experiments on two real datasets show that MENET accurately predicts diagnostic labels and identify brain connectivities highly associated with neurological disorders such as Alzheimer’s Disease and Attention-Deficit/Hyperactivity Disorder.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
EditorsAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages253-266
Number of pages14
ISBN (Print)9783030781903
DOIs
Publication statusPublished - 2021
Event27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online
Duration: 2021 Jun 282021 Jun 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12729 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Information Processing in Medical Imaging, IPMI 2021
CityVirtual, Online
Period21/6/2821/6/30

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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