A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure

Shu Zhang, Yanqing Kang, Sigang Yu, Jinru Wu, Enze Shi, Ruoyang Wang, Zhibin He, Lei Du, Tuo Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Understanding the relationship between brain function and structure is vital important in the field of brain image analysis. It elucidates the working mechanism of the brain, which will contribute to better understand the brain and simulate the brain-like system. Extensive efforts have been made on this topic, but still far from the satisfactory. The major difficulties are at least two aspects. One is the huge individual difference among the subjects, which makes it hard to obtain stable results at groupwise level, e.g., noise signals can significantly affect the exploring process. The other one is the huge difference between functional and structural features of the brain, both in their pattern and size, which are very different. To alleviate the above problems, in this paper, we propose a two-stage multi-view low-rank sparse subspace clustering (Two-stage MLRSSC) method to jointly study the relationship between brain function and structure and identify the common regions of brain function and structure. The major innovation of proposed Two-stage MLRSSC is that comparable features of brain function and structure can be effectively extracted from low-rank sparse representation, and results are further improved the stability by two-stage strategy. Finally, groupwise-based stable functional and structural common regions are identified for better understanding the relationship. Experimental results shed new ways to explore the brain function and structure, new insights are observed and discussed.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui
PublisherSpringer Science and Business Media Deutschland GmbH
Pages191-200
Number of pages10
ISBN (Print)9783031210136
DOIs
StatePublished - 2022
Event13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sep 202218 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13583 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/2218/09/22

Keywords

  • Brain function
  • Brain structure
  • Low-Rank Sparse Subspace Clustering
  • Multi-View

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