复杂网络主成分分析的分类方法在音乐家白质可塑性研究中的应用

doi:10.3969/j.issn.0258-8021.2015.02.008

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Abstract The human brain is a complex network with multiple scales of time and space which includes large amount of connection information. Principal component analysis (PCA) can extract important features from vast quantities of information; therefore, it was used to explore important information from complex network in this study. As is widely known, musicians represent an ideal model to investigate experiencedriven plasticity changes in the human brain. It is a far more significant research that explores plasticity changes in brain networks of musicians. In this study, white matter brain networks of 16 musicians and 16 nonmusicians were firstly constructed by fiber tracking based on diffusionweighted imaging (DWI); secondly, PCA process was used to extract the feature networks of two the groups, support vector machine (SVM) classification method was then applied to each component, the component with best classification performance was obtained; finally, the first 1% connections with highest contribution to the component were considered to be the main connections which may represent the changes in the musicians’ white matter anatomical networks compared to nonmusicians. This method provides a new approach which utilizes the PCA based classification for complex network comparison issues. And, comparison analysis of the white matter anatomical brain network between musicians and nonmusicians indicated that musicians showed enhanced information transfer efficient between motor, auditory, emotional, and memory related brain regions. These findings may extend the network level understanding of white matter plasticity in musicians who have had longterm musical training.

Key words： principal component analysis (PCA) support vector machine (SVM) diffusionweighted imaging (DWI) brain network

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	Li Jianfu Luo Cheng^* Dong Li Yao Dezhong^#

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Li Jianfu Luo Cheng^* Dong Li Yao Dezhong^#. Classification of Principal Component Analysis on Complex Network and Application for White Matter Plasticity of Musicians[J]. journal1, 2015, 34(2): 184-189.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2015.02.008 OR http://cjbme.csbme.org/EN/Y2015/V34/I2/184

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