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.
Details
- Boosting Brain Connectome Classification Accuracy in Alzheimer's Disease Using Higher-Order Singular Value Decomposition
- Zhan, Liang (Author)
- Liu, Yashu (Author)
- Wang, Yalin (Author)
- Zhou, Jiayu (Author)
- Jahanshad, Neda (Author)
- Ye, Jieping (Author)
- Thompson, Paul M. (Author)
- Alzheimer's Disease Neuroimaging Initiative (Project) (Contributor)
- Digital object identifier: 10.3389/fnins.2015.00257
- Identifier TypeInternational standard serial numberIdentifier Value1662-4548
- Identifier TypeInternational standard serial numberIdentifier Value1662-453X
- View the article as published at http://journal.frontiersin.org/article/10.3389/fnins.2015.00257/full
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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