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Accurate Identification of MCI Patients via Enriched White-Matter Connectivity Network

  • Chong-Yaw Wee
  • Pew-Thian Yap
  • Jeffery N. Brownyke
  • Guy G. Potter
  • David C. Steffens
  • Kathleen Welsh-Bohmer
  • Lihong Wang
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6357)

Abstract

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer’s disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques have made understanding neurological disorders at a whole brain connectivity level possible. Accordingly, we propose a network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber penetration count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ 1, λ 2, λ 3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), the average statistics of each ROI in relation to the remaining ROIs are extracted as features for classification. These features are then sieved to select the most discriminant subset of features for building an MCI classifier via support vector machines (SVMs). Cross-validation results indicate better diagnostic power of the proposed enriched WM connection description than simple description with any single physiological parameter.

Keywords

Support Vector Machine Mild Cognitive Impairment Fractional Anisotropy Connectivity Network Biophysical Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chong-Yaw Wee
    • 1
  • Pew-Thian Yap
    • 1
  • Jeffery N. Brownyke
    • 3
  • Guy G. Potter
    • 2
  • David C. Steffens
    • 2
  • Kathleen Welsh-Bohmer
    • 3
  • Lihong Wang
    • 4
  • Dinggang Shen
    • 1
  1. 1.Department of RadiologyUniversity of North Carolina at Chapel HillU.S.A.
  2. 2.Department of Psychiatry and Behavioral Sciences 
  3. 3.Joseph and Kathleen Bryan Alzheimer’s Disease Research Center and 
  4. 4.Brain Imaging and Analysis CenterDuke University Medical CenterU.S.A.

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