Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment

Xi Jiang, Dajiang Zhu, Kaiming Li, Tuo Zhang, Lihong Wang, Dinggang Shen, Lei Guo, Tianming Liu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.

Original languageEnglish
Pages (from-to)542-557
Number of pages16
JournalBrain Imaging and Behavior
Issue number4
Publication statusPublished - 2014 Nov 23

Bibliographical note

Funding Information:
Acknowledgments T Liu was supported by NIH Career Award (NIH EB-006878), NSF CAREER Award (IIS-1149260), NIH R01 DA-033393, NIH R01 AG-042599, and NSF BME-1302089. L Guo was supported by the NWPU Foundation for Fundamental Research. K Li and T Zhang were supported by the China Government Scholarship. L Wang was supported by the Paul B. Beeson Career Developmental Awards (K23-AG028982) and a National Alliance for Research in Schizophrenia and Depression Young Investigator Award. The authors would like to thank the anonymous reviewers for their constructive comments.

Publisher Copyright:
© 2013, Springer Science+Business Media New York.


  • DTI
  • Functional connectivity
  • Mild cognitive impairment
  • Predictive models of networks
  • Resting state fMRI
  • Resting state networks

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Radiology Nuclear Medicine and imaging
  • Behavioral Neuroscience


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