Abstract
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 language | English |
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Pages (from-to) | 542-557 |
Number of pages | 16 |
Journal | Brain Imaging and Behavior |
Volume | 8 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2014 Nov 23 |
Bibliographical note
Publisher Copyright:© 2013, Springer Science+Business Media New York.
Keywords
- DTI
- Functional connectivity
- Mild cognitive impairment
- Predictive models of networks
- Resting state fMRI
- Resting state networks
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Neurology
- Cognitive Neuroscience
- Clinical Neurology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health
- Behavioral Neuroscience