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Machine Learning Techniques for AD/MCI Diagnosis and Prognosis

  • Dinggang Shen
  • Chong-Yaw Wee
  • Daoqiang Zhang
  • Luping Zhou
  • Pew-Thian Yap
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)

Abstract

In the past two decades, machine learning techniques have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some of the latest developments in the application of machine learning techniques to AD and MCI diagnosis and prognosis. We will divide our discussion into two parts: single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the various modalities, such as structural T1-weighted imaging, diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.

Keywords

Alzheimer’s disease Mild cognitive impairment Machine learning Diagnosis Prognosis Connectivity networks Multimodality 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Dinggang Shen
    • 1
  • Chong-Yaw Wee
    • 1
  • Daoqiang Zhang
    • 1
    • 2
  • Luping Zhou
    • 3
  • Pew-Thian Yap
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging Center (BRIC)University of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.University of WollongongWollongongAustralia

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