Independent Component Analysis-Based Classification of Alzheimer's MRI Data

Wenlu Yang Shanghai Maritime University Ronald Lok Ming Lui Harvard University Jia-Hong Gao The University of Chicago Tony F. Chan The Hong Kong University of Science and Technology Shing-Tung Yau Harvard University Reisa A. Sperling Harvard University Xudong Huang Harvard University

Computational Geometry mathscidoc:1609.09032

Journal of Alzheimer's Disease, 24, (4), 775–783, 2011
There is an unmet medical need to identify neuroimaging biomarkers that is able to accurately diagnose and monitor Alzheimer's disease (AD) at very early stages and 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 are able to distinguish AD and mild cognitive impairment (MCI) subjects from normal controls. 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, coupled with the use of support vector machine (SVM) for classifying scans into categories of AD, MCI, and normal control (NC) subjects. The MRI data were selected from Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) databases. The experimental results showed that our ICA-based method can differentiate AD and MCI subjects from normal controls, although further methodological improvement in the analytic method and inclusion of additional variables may be required for optimal classification.
Alzheimer's disease; mild cognitive impairment; magnetic resonance imaging; independent component analysis; support vector machine; neuroimaging biomarker
[ Download ] [ 2016-09-06 11:44:15 uploaded by lmlui ] [ 750 downloads ] [ 0 comments ] [ Cited by 15 ]
@inproceedings{wenlu2011independent,
  title={Independent Component Analysis-Based Classification of Alzheimer's MRI Data},
  author={Wenlu Yang, Ronald Lok Ming 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/20160906114415636495644},
  booktitle={Journal of Alzheimer's Disease},
  volume={24},
  number={4},
  pages={775–783},
  year={2011},
}
Wenlu Yang, Ronald Lok Ming Lui, Jia-Hong Gao, Tony F. Chan, Shing-Tung Yau, Reisa A. Sperling, and Xudong Huang. Independent Component Analysis-Based Classification of Alzheimer's MRI Data. 2011. Vol. 24. In Journal of Alzheimer's Disease. pp.775–783. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160906114415636495644.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved