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A Multilayer Sparse Representation of Dynamic Brain Functional Network Based on Hypergraph Theory for ADHD Classification

  • Yuduo Zhang
  • Zhichao LianEmail author
  • Chanying Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)

Abstract

Nowadays, studies on the brain show that the resting brain is still dynamic, and the dynamics of brain functional connectivity remains to be proven, which is very important for the research and diagnosis of mental disorders. In this paper, we apply the Bayesian Connection Change Point Model (BCCPM) to perform dynamic testing on the brain. A sparse model is used to construct a hypergraph to represent the brain function connectivity network, and then the dictionary obtained by sparse learning is used to further extract the features of brain function network. The experimental results on ADHD data show that the accuracy of the proposed method has been improved. Meanwhile, we find that there are obvious differences in the sparse features values of the brain functional networks between patients and normal controls. In addition, the comparison between the proposed method with/without the BCCPM demonstrated the importance of dynamic detection further.

Keywords

ADHD BCCPM Hyper network Sparse representation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNanjingChina

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