title: Probabilistic diffusion tractography reveals improvement of structural network in musicians.
abstract: OBJECTIVE
  Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain.
  
  
  METHODS
  Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory.
  
  
  RESULTS
  Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level.
  
  
  CONCLUSIONS
  We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Jianfu
authors.initials: J
authors.lastname: Li
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Luo
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Yueheng
authors.initials: Y
authors.lastname: Peng
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Qiankun
authors.initials: Q
authors.lastname: Xie
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Jinnan
authors.initials: J
authors.lastname: Gong
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Li
authors.initials: L
authors.lastname: Dong
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Yongxiu
authors.initials: Y
authors.lastname: Lai
authors.affiliation: Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Li
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D009146:Music; D009415:Nerve Net; D011336:Probability; D066127:White Matter / Q000033:anatomy & histology; D055815:Young Adult