Weighted graph regularized sparse brain network construction for MCI identification

Renping Yu, Lishan Qiao, Mingming Chen, Seong Whan Lee, Xuan Fei, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.

Original languageEnglish
Pages (from-to)220-231
Number of pages12
JournalPattern Recognition
Publication statusPublished - 2019 Jun


  • Brain functional network
  • Graph Laplacian regularization
  • Mild cognitive impairment (MCI)
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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