Estimating functional brain networks by incorporating a modularity prior

Lishan Qiao, Han Zhang, Minjeong Kim, Shenghua Teng, Limei Zhang, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)


Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct “ideal” brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis.

Original languageEnglish
Pages (from-to)399-407
Number of pages9
Publication statusPublished - 2016 Nov 1


  • Brain network
  • Classification
  • Functional magnetic resonance imaging (fMRI)
  • Low-rank representation
  • Mild cognitive impairment (MCI)
  • Modularity
  • Partial correlation
  • Pearson's correlation
  • Sparse representation

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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