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Connectome-scale assessments of structural and functional connectivity in MCI.

Publication ,  Journal Article
Zhu, D; Li, K; Terry, DP; Puente, AN; Wang, L; Shen, D; Miller, LS; Liu, T
Published in: Hum Brain Mapp
July 2014

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.

Duke Scholars

Published In

Hum Brain Mapp

DOI

EISSN

1097-0193

Publication Date

July 2014

Volume

35

Issue

7

Start / End Page

2911 / 2923

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Oxygen
  • Neural Pathways
  • Middle Aged
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Female
  • Experimental Psychology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhu, D., Li, K., Terry, D. P., Puente, A. N., Wang, L., Shen, D., … Liu, T. (2014). Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp, 35(7), 2911–2923. https://doi.org/10.1002/hbm.22373
Zhu, Dajiang, Kaiming Li, Douglas P. Terry, A Nicholas Puente, Lihong Wang, Dinggang Shen, L Stephen Miller, and Tianming Liu. “Connectome-scale assessments of structural and functional connectivity in MCI.Hum Brain Mapp 35, no. 7 (July 2014): 2911–23. https://doi.org/10.1002/hbm.22373.
Zhu D, Li K, Terry DP, Puente AN, Wang L, Shen D, et al. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp. 2014 Jul;35(7):2911–23.
Zhu, Dajiang, et al. “Connectome-scale assessments of structural and functional connectivity in MCI.Hum Brain Mapp, vol. 35, no. 7, July 2014, pp. 2911–23. Pubmed, doi:10.1002/hbm.22373.
Zhu D, Li K, Terry DP, Puente AN, Wang L, Shen D, Miller LS, Liu T. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp. 2014 Jul;35(7):2911–2923.
Journal cover image

Published In

Hum Brain Mapp

DOI

EISSN

1097-0193

Publication Date

July 2014

Volume

35

Issue

7

Start / End Page

2911 / 2923

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Oxygen
  • Neural Pathways
  • Middle Aged
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Female
  • Experimental Psychology