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

Publication ,  Journal Article
Jiang, X; Zhu, D; Li, K; Zhang, T; Wang, L; Shen, D; Guo, L; Liu, T
Published in: Brain Imaging Behav
December 2014

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.

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Published In

Brain Imaging Behav

DOI

EISSN

1931-7565

Publication Date

December 2014

Volume

8

Issue

4

Start / End Page

542 / 557

Location

United States

Related Subject Headings

  • Rest
  • Neural Pathways
  • Models, Neurological
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Female
  • Experimental Psychology
  • Diffusion Tensor Imaging
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Jiang, X., Zhu, D., Li, K., Zhang, T., Wang, L., Shen, D., … Liu, T. (2014). Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment. Brain Imaging Behav, 8(4), 542–557. https://doi.org/10.1007/s11682-013-9280-x
Jiang, Xi, Dajiang Zhu, Kaiming Li, Tuo Zhang, Lihong Wang, Dinggang Shen, Lei Guo, and Tianming Liu. “Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment.Brain Imaging Behav 8, no. 4 (December 2014): 542–57. https://doi.org/10.1007/s11682-013-9280-x.
Jiang X, Zhu D, Li K, Zhang T, Wang L, Shen D, et al. Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment. Brain Imaging Behav. 2014 Dec;8(4):542–57.
Jiang, Xi, et al. “Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment.Brain Imaging Behav, vol. 8, no. 4, Dec. 2014, pp. 542–57. Pubmed, doi:10.1007/s11682-013-9280-x.
Jiang X, Zhu D, Li K, Zhang T, Wang L, Shen D, Guo L, Liu T. Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment. Brain Imaging Behav. 2014 Dec;8(4):542–557.
Journal cover image

Published In

Brain Imaging Behav

DOI

EISSN

1931-7565

Publication Date

December 2014

Volume

8

Issue

4

Start / End Page

542 / 557

Location

United States

Related Subject Headings

  • Rest
  • Neural Pathways
  • Models, Neurological
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Female
  • Experimental Psychology
  • Diffusion Tensor Imaging