Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences

Xiaodan Xing, Lili Jin, Qinfeng Li, Lei Chen, Zhong Xue, Ziwen Peng, Feng Shi, Dinggang Shen

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

Abstract

Graph convolutional network (GCN) has shown its potential on modeling functional MRI connectivity and recognizing neurological disease tasks. However, conventional GCN layers generally inherit the original graph topology, without the modeling of hierarchical graph representation. Besides, although the interpretability of GCN has been widely investigated, such studies only identify several independently affected brain regions instead of forming them as neurological circuits, which are more desirable for disease mechanism investigation. In this paper, we propose a hierarchical dynamic GCN (HD-GCN), which combines the information from both low-order graph composed of brain regions and high-order graph composed of brain region clusters. The algorithm learns a consistent dynamic graph pooling, which helps improve the classification accuracy by hierarchical graph representation learning and could identify the affected neurological circuits. We employed two datasets to evaluate the generalizability of the proposed method: ADNI dataset containing 177 AD patients and 115 controls, and Obsessive-Compulsive Disorder (OCD) dataset including 67 patients and 61 controls. The classification accuracy reaches$$89.4\%$$ on ADNI dataset and$$89.1\%$$ on OCD dataset. The affected brain circuits were also identified, which are consistent with previous psychological studies.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsCarole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot
PublisherSpringer Science and Business Media Deutschland GmbH
Pages121-130
Number of pages10
ISBN (Print)9783030603649
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 82020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/820/10/8

Keywords

  • Circuit detection
  • Functional connectivity
  • Graph convolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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