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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 4820935, 14 pages
Research Article

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

1College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
2National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
3Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China

Correspondence should be addressed to Hao Guo; moc.anis@oug_uyief

Received 16 June 2017; Revised 10 October 2017; Accepted 9 November 2017; Published 14 December 2017

Academic Editor: Marko Gosak

Copyright © 2017 Hao Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.