Adaptive Thresholding of Functional Connectivity Networks for fMRI-Based Brain Disease Analysis

Zhengdong Wang, Biao Jie, Weixin Bian, Daoqiang Zhang, Dinggang Shen, Mingxia Liu

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

Abstract

Functional connectivity (FC) networks based on functional magnetic resonance imaging (fMRI) data have been widely applied to automated identification of brain diseases, such as attention deficit hyperactivity disorder (ADHD) and Alzheimer’s disease (AD). To generate compact representations of FC networks for disease analysis, various thresholding strategies have been developed for analyzing brain FC networks. However, existing studies typically employ predefined values or percentages of connections to threshold the whole FC networks, thus ignoring the diversity of temporal correlations (particularly strong correlations) among different brain regions. In addition, in practice, it is usually very challenging to decide the optimal threshold or connection percentage in FC network analysis. To address these problems, in this paper, we propose a weight distribution based thresholding (WDT) method for FC network analysis with resting-state function MRI data. Specifically, for FC between a pair of brain regions, we calculate its optimal threshold value by using the weight (i.e., temporal correlation) distributions of the FC across two subject groups (i.e., patient and normal groups). The proposed WDT method can adaptively yields FC-specific thresholds, thus preserving the diversity information of FCs among different brain regions. Experiment results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate the effectiveness of our proposed WDT method.

Original languageEnglish
Title of host publicationGraph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsDaoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
PublisherSpringer
Pages18-26
Number of pages9
ISBN (Print)9783030358167
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 172019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1719/10/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, Z., Jie, B., Bian, W., Zhang, D., Shen, D., & Liu, M. (2019). Adaptive Thresholding of Functional Connectivity Networks for fMRI-Based Brain Disease Analysis. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 18-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_3