Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification

Zhengdong Wang, Biao Jie, Mi Wang, Chunxiang Feng, Wen Zhou, Dinggang Shen, Mingxia Liu

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

Abstract

Function connectivity networks (FCNs) based on resting-state functional magnetic resonance imaging (rs-fMRI) have been used for analysis of brain diseases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (e.g., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection methods (e.g., t-test) to improve the performance of learning model, thus ignoring important structural information of FCNs. To address this problem, we propose a graph-kernel-based structured feature selection (gk-MTSFS) method for brain disease classification using rs-fMRI data. Different with existing method that focus on vector-based feature selection, our proposed gk-MTSFS method adopts the graph kernel (i.e., kernel constructed on graphs) to preserve the structural information of FCNs, and uses the multi-task learning to explore the complementary information of multi-level thresholded FCNs (i.e., thresholded FCNs with different thresholds). Specifically, in the proposed gk-MTSFS model, we first develop a novel graph-kernel based Laplacian regularizer to preserve the structural information of FCNs. Then, we employ an 2,1-norm based group sparsity regularizer to joint select a small amount of discriminative features from multi-level FCNs for brain disease classification. Experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate the effectiveness of our proposed gk-MTSFS method in rs-fMRI-based brain disease diagnosis.

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
Pages27-35
Number of pages9
ISBN (Print)9783030358167
DOIs
Publication statusPublished - 2019 Jan 1
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

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, Z., Jie, B., Wang, M., Feng, C., Zhou, W., Shen, D., & Liu, M. (2019). Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification. 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. 27-35). (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_4