Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis

  • Xi Yang
  • Yan Jin
  • Xiaobo Chen
  • Han Zhang
  • Gang Li
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xi Yang
    • 1
  • Yan Jin
    • 1
  • Xiaobo Chen
    • 1
  • Han Zhang
    • 1
  • Gang Li
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA

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