Connectome-scale assessments of structural and functional connectivity in MCI

Journal Article

Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. Although neuroimaging techniques hold promise, compared to commonly used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In this article, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as "connectome signatures." Our results indicate that these "connectome signatures" have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of "connectome signatures" are mainly derived from the interactions among different functional networks, for example, cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional "connectome signatures" as neuroimaging biomarkers of MCI. © 2013 Wiley Periodicals, Inc.

Full Text

Duke Authors

Cited Authors

  • Zhu, D; Li, K; Terry, DP; Puente, AN; Wang, L; Shen, D; Miller, LS; Liu, T

Published Date

  • 2014

Published In

Volume / Issue

  • 35 / 7

Start / End Page

  • 2911 - 2923

International Standard Serial Number (ISSN)

  • 1065-9471

Digital Object Identifier (DOI)

  • 10.1002/hbm.22373