Discriminative Analysis of Early Alzheimer’s Disease Based on Two Intrinsically Anti-correlated Networks with Resting-State fMRI

  • Kun Wang
  • Tianzi Jiang
  • Meng Liang
  • Liang Wang
  • Lixia Tian
  • Xinqing Zhang
  • Kuncheng Li
  • Zhening Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


In this work, we proposed a discriminative model of Alzheimer’s disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.


Blood Oxygen Level Dependent Discriminative Model Discriminative Approach Fisher Linear Discriminative Analysis Elderly Healthy Control 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kun Wang
    • 1
  • Tianzi Jiang
    • 1
  • Meng Liang
    • 1
  • Liang Wang
    • 2
  • Lixia Tian
    • 1
  • Xinqing Zhang
    • 2
  • Kuncheng Li
    • 2
  • Zhening Liu
    • 3
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Radiology, NeurologyXuanwu Hospital of Capital University of Medical ScienceBeijingChina
  3. 3.Institute of Mental Health, Second Xiangya HospitalCentral South UniversityChangshaChina

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