Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network

• Published in: Frontiers in computational neuroscience Vol. 12; p. 31
• Main Authors: Cui, Xiaohong, Xiang, Jie, Guo, Hao, Yin, Guimei, Zhang, Huijun, Lan, Fangpeng, Chen, Junjie
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 09.05.2018; Frontiers Media S.A
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Classification; Cognitive ability; graph kernel principal component analysis; gSpan; Medical imaging; mild cognitive impairment; minimum spanning tree; Neural networks; Neurodegenerative diseases; Neuroscience; NMR; Nuclear magnetic resonance; Principal components analysis; China; mild cognitive impairment; gSpan; graph kernel principal component analysis; classification; Alzheimer's disease; minimum spanning tree
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2018.00031

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.