Computer Science ›› 2016, Vol. 43 ›› Issue (7): 265-267.doi: 10.11896/j.issn.1002-137X.2016.07.048

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Classification of Multi-scale Functional Brain Network in Depression

CHENG Chen, GUO Hao and CHEN Jun-jie   

  • Online:2018-12-01 Published:2018-12-01

Abstract: As a complex network analysis method,brain network has been widely accepted in the field of neuroimaging.According to the research,the scale of nodes in the brain has a major impact on the network topological properties.This paper used the resting state functional imaging data to construct brain networks for patients and normal controls respectively under five different node scales and compared variances of the network topological properties,and then selected four different algorithms to do the classification.The results show that the node scale can not only affect the topological properties,but also has a direct effect on the construction of classification model.Support vector machine (RBF kernel function) model shows the best classification results when the node scale is 250,the average accuracy is 83.18%.The research results have an important application value in the clinical diagnosis of depression,and provide a significant reference basis on the network nodes’ selection based on machine learning of brain network.

Key words: Brain network,Topological properties,Node scale,Machine learning,Depression

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