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Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

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    Title
    • Boosting Brain Connectome Classification Accuracy in Alzheimer's Disease Using Higher-Order Singular Value Decomposition
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    Date Created
    2015-07-24
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.3389/fnins.2015.00257
    • Identifier Type
      International standard serial number
      Identifier Value
      1662-4548
    • Identifier Type
      International standard serial number
      Identifier Value
      1662-453X
    Note
    • View the article as published at http://journal.frontiersin.org/article/10.3389/fnins.2015.00257/full

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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Zhan, L., Liu, Y., Wang, Y., Zhou, J., Jahanshad, N., Ye, J., & Thompson, P. M. (2015). Boosting brain connectome classification accuracy in Alzheimers disease using higher-order singular value decomposition. Frontiers in Neuroscience, 9. doi:10.3389/fnins.2015.00257

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