Journal
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2018.00031
Keywords
minimum spanning tree; gSpan; graph kernel principal component analysis; mild cognitive impairment; Alzheimer's disease; classification
Funding
- National Natural Science Foundation of China [61472270, 61402318, 61672374]
- Natural Science Foundation of Shanxi Province [201601D021073]
- Fundamental Research Project of Shanxi Province [2015D21106]
- Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [2016139]
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Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently Researchers have constructed threshold brain function networks and extracted various features for the classification of brain diseases However, in the construction of the brain function network, the selection of threshold is very important, and the unreasonable setting will seriously affect the final classification results To address this issue, in this paper, we propose a minimum spanning tree (MST) classification framework to identify Alzheimer's disease (AD), MCI, and normal controls (NCs) The proposed method mainly uses the MST method, graph-based Substructure Pattern mining (gSpan), and graph kernel Principal Component Analysis (graph kernel PCA) Specifically, MST is used to construct the brain functional connectivity network, gSpan, to extract features, and subnetwork selection and graph kernel PCA, to select features Finally, the support vector machine is used to perform classification We evaluate our method on MST brain functional networks of 21 AD, 25 MCI, and 22 NC subjects The experimental results show that our proposed method achieves classification accuracy of 98.3, 91.3, and 77.3%, for MCI vs NC, AD vs NC, and AD vs MCI, respectively The results show our proposed method can achieve significantly improved classification performance compared to other state-of-the-art methods.
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