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Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment

  • Lichi Zhang
  • Han Zhang
  • Xiaobo Chen
  • Qian Wang
  • Pew-Thian Yap
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. First, we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. Second, it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lichi Zhang
    • 1
  • Han Zhang
    • 1
  • Xiaobo Chen
    • 1
  • Qian Wang
    • 2
  • Pew-Thian Yap
    • 1
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Med-X Research Institute, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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