Predictive models of resting state networks for assessment of altered functional connectivity in MCI.

X. Jiang, Dajiang Zhu, Kaiming Li, Tuo Zhang, Dinggang Shen, Lei Guo, Tianming Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages674-681
Number of pages8
Volume16
EditionPt 2
Publication statusPublished - 2013 Jan 1
Externally publishedYes

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Cognitive Dysfunction
Brain
Cluster Analysis
Magnetic Resonance Imaging
Control Groups

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Jiang, X., Zhu, D., Li, K., Zhang, T., Shen, D., Guo, L., & Liu, T. (2013). Predictive models of resting state networks for assessment of altered functional connectivity in MCI. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 16, pp. 674-681)

Predictive models of resting state networks for assessment of altered functional connectivity in MCI. / Jiang, X.; Zhu, Dajiang; Li, Kaiming; Zhang, Tuo; Shen, Dinggang; Guo, Lei; Liu, Tianming.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. p. 674-681.

Research output: Chapter in Book/Report/Conference proceedingChapter

Jiang, X, Zhu, D, Li, K, Zhang, T, Shen, D, Guo, L & Liu, T 2013, Predictive models of resting state networks for assessment of altered functional connectivity in MCI. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 16, pp. 674-681.
Jiang X, Zhu D, Li K, Zhang T, Shen D, Guo L et al. Predictive models of resting state networks for assessment of altered functional connectivity in MCI. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 16. 2013. p. 674-681
Jiang, X. ; Zhu, Dajiang ; Li, Kaiming ; Zhang, Tuo ; Shen, Dinggang ; Guo, Lei ; Liu, Tianming. / Predictive models of resting state networks for assessment of altered functional connectivity in MCI. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. pp. 674-681
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