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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Jiang, X; Zhu, D; Li, K; Zhang, T; Wang, L; Shen, D; Guo, L; Liu, T

Published Date

  • December 2014

Published In

Volume / Issue

  • 8 / 4

Start / End Page

  • 542 - 557

PubMed ID

  • 24293138

Pubmed Central ID

  • 24293138

Electronic International Standard Serial Number (EISSN)

  • 1931-7565

International Standard Serial Number (ISSN)

  • 1931-7557

Digital Object Identifier (DOI)

  • 10.1007/s11682-013-9280-x

Language

  • eng