Journals & Publications

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

 Authors: Guo, H; Qin, MN; Chen, JJ; Xu, Y; Xiang, J

 Author Full Names: Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

 Source: COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 10.1155/2017/4820935 2017

 Language: English

 Abstract: 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 withmajor depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

 ISSN: 1748-670X

 eISSN: 1748-6718

 Article Number: 4820935

 IDS Number: FR1KQ Unique

ID: WOS:000418826100001