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Classification of Principal Component Analysis on Complex Network and Application for White Matter Plasticity of Musicians |
Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China |
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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 experiencedriven 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 nonmusicians were firstly constructed by fiber tracking based on diffusionweighted 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 nonmusicians. 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 nonmusicians 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 longterm musical training.
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