Title |
Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition
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Published in |
Frontiers in Neuroscience, July 2015
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DOI | 10.3389/fnins.2015.00257 |
Pubmed ID | |
Authors |
Liang Zhan, Yashu Liu, Yalin Wang, Jiayu Zhou, Neda Jahanshad, Jieping Ye, Paul M. Thompson, Alzheimer's Disease Neuroimaging Initiative |
Abstract |
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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Geographical breakdown
Country | Count | As % |
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United States | 2 | 50% |
Switzerland | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 2% |
Unknown | 42 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 8 | 19% |
Researcher | 7 | 16% |
Student > Bachelor | 4 | 9% |
Student > Master | 4 | 9% |
Professor | 3 | 7% |
Other | 6 | 14% |
Unknown | 11 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 7 | 16% |
Computer Science | 6 | 14% |
Medicine and Dentistry | 3 | 7% |
Neuroscience | 3 | 7% |
Agricultural and Biological Sciences | 2 | 5% |
Other | 6 | 14% |
Unknown | 16 | 37% |