Improving sparsity and modularity of high-order functional connectivity networks for MCI and ASD identification

Yueying Zhou, Limei Shenghua Zhang Teng, Lishan Qiao, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.

Original languageEnglish
Article number959
JournalFrontiers in Neuroscience
Volume12
DOIs
Publication statusPublished - 2018

Keywords

  • Autism spectrum disorder
  • Dynamic network
  • Functional connectivity network
  • High-order correlation
  • Mild cognitive impairment
  • Modularity

ASJC Scopus subject areas

  • Neuroscience(all)

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