Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

Qingsong Xie, Xiangfei Zhang, Islem Rekik, Xiaobo Chen, Ning Mao, Dinggang Shen (Lead / Corresponding author), Feng Zhao (Lead / Corresponding author)

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Abstract

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

Original languageEnglish
Article numbere11692
Number of pages25
JournalPeerJ
Volume7
DOIs
Publication statusPublished - 6 Jul 2021

Keywords

  • Autism spectrum disorder
  • Central moment feature
  • Cross validation
  • Dynamic functional connectivity network
  • Feature extraction
  • Feature selection
  • Functional connectivity
  • Functional magnetic resonance imaging
  • High functional connectivity network
  • Low functional connectivity network

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