FMRI classification method with multiple feature fusion based on minimum spanning tree analysis. (30th July 2018)

• Record Type: Journal Article
• Authors: Guo, Hao; Yan, Pengpeng; Cheng, Chen; Li, Yao; Chen, Junjie; Xu, Yong; Xiang, Jie
• Is Part Of: Psychiatry research. Volume 277(2018)
• Page Start: 14
• Page End: 27
• Publication Date: 2018-07-30
• Subjects: Functional brain network -- Minimum spanning tree -- Classifier -- Depression -- Multiple feature fusion; Psychiatry -- Periodicals; Brain -- Imaging -- Periodicals; Psychiatry -- Periodicals; Diagnostic Imaging -- Periodicals; Psychiatrie -- Périodiques; Cerveau -- Imagerie pour le diagnostic -- Périodiques; 616.890754
• DOI: 10.1016/j.pscychresns.2018.05.001
• Languages: English
• ISSNs: 0925-4927

Abstract

Highlights: The MSTs of MDD patients were more like random networks than NC. Compared with NCs, the MSTs of MDD patients exhibited significant differences in certain regions concentrated in the LCSPT circuit. The results of prosed method exhibited significantly better performance than the methods using a single feature type. The proposed method performed better than the conventional methods of constructing the functional network by partial correlations or Pearson correlations. Abstract: Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.