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Independent component analysis-based classification of Alzheimer's disease MRI data

Wenlu Yang Ronald LM Lui Jia-Hong Gao Tony F Chan Shing-Tung Yau Reisa A Sperling Xudong Huang

Computational Geometry mathscidoc:1912.43530

Journal of Alzheimer's disease, 24, (4), 775-783, 2011.1
There is an unmet medical need to identify neuroimaging biomarkers that allow us to accurately diagnose and monitor Alzheimer's disease (AD) at its very early stages and to assess the response to AD-modifying therapies. To a certain extent, volumetric and functional magnetic resonance imaging (fMRI) studies can detect changes in structure, cerebral blood flow, and blood oxygenation that distinguish AD and mild cognitive impairment (MCI) subjects from healthy control (HC) subjects. However, it has been challenging to use fully automated MRI analytic methods to identify potential AD neuroimaging biomarkers. We have thus proposed a method based on independent component analysis (ICA) for studying potential AD-related MR image features that can be coupled with the use of support vector machine (SVM) for classifying scans into categories of AD, MCI, and HC subjects. The MRI data were selected from
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@inproceedings{wenlu2011independent,
  title={Independent component analysis-based classification of Alzheimer's disease MRI data},
  author={Wenlu Yang, Ronald LM Lui, Jia-Hong Gao, Tony F Chan, Shing-Tung Yau, Reisa A Sperling, and Xudong Huang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224203837568613094},
  booktitle={Journal of Alzheimer's disease},
  volume={24},
  number={4},
  pages={775-783},
  year={2011},
}
Wenlu Yang, Ronald LM Lui, Jia-Hong Gao, Tony F Chan, Shing-Tung Yau, Reisa A Sperling, and Xudong Huang. Independent component analysis-based classification of Alzheimer's disease MRI data. 2011. Vol. 24. In Journal of Alzheimer's disease. pp.775-783. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224203837568613094.
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