Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, Yixuan Yuan, Peili Lv, Tuo Zhang, Lei Guo, Dinggang Shen, Tianming Liu

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

Original languageEnglish
Article number6512602
Pages (from-to)1576-1586
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - 2013


  • Diffusion tensor imaging (DTI)
  • functional magnetic resonance imaging (fMRI)
  • multi-view clustering
  • multimodal brain connectome

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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