Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study

• Published in: PloS one 2019-02, Vol.14 (2), p.e0210015-e0210015
• Main Authors: Pi, Yan-Ling, Wu, Xu-Heng, Wang, Feng-Juan, Liu, Ke, Wu, Yin, Zhu, Hua, Zhang, Jian
• Format: Article
• Language: English
• Subjects: Adult; Analysis; Athletes; Attention; Biology and Life Sciences; Brain; Brain - diagnostic imaging; Brain - physiology; Brain research; Computer and Information Sciences; Correlation analysis; Diffusion; Diffusion Tensor Imaging; Education; Environmental changes; Exercise; Experts; Family medical history; Female; Graph theory; Health sciences; Humans; Imaging; Imaging systems; Laboratories; Learning; Learning - physiology; Levels; Male; Mathematical analysis; Medical imaging; Medicine and Health Sciences; Memory; Motor ability; Motor skill; Motor skill learning; Motor Skills - physiology; Neural Pathways - diagnostic imaging; Neural Pathways - physiology; Neuroimaging; Neuronal Plasticity; Neurosciences; Parameters; Plastic properties; Plasticity; Professional basketball; Regional analysis; Research and Analysis Methods; Semantics; Sensorimotor system; Skills; Social Sciences; Sports injuries; Substantia alba; Tensors; Training; Transmission efficiency; Visual perception
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0210015

Motor skills and the acquisition of brain plasticity are important topics in current research. The development of non-invasive white matter imaging technology, such as diffusion-tensor imaging and the introduction of graph theory make it possible to study the effects of learning skills on the connection patterns of brain networks. However, few studies have characterized the brain network topological features of motor skill learning, especially open skill. Given the need to interact with environmental changes in real time, we hypothesized that the brain network of high-level open-skilled athletes had higher transmission efficiency and stronger interaction in attention, visual and sensorimotor networks. We selected 21 high-level basketball players and 25 ordinary individuals as control subjects, collected their DTI data, built a network of brain structures, and used graph theory to analyze and compare the network properties of the two groups at global and regional levels. In addition, we conducted a correlation analysis on the training years of high-level athletes and brain network nodal parameters on the regional level to assess the relationship between brain network topological characteristics and skills learning. We found that on the global-level, the brain network of high-level basketball players had a shorter path length, small-worldness, and higher global efficiency. On the regional level, the brain nodes of the high-level athletes had nodal parameters that were significantly higher than those of control groups, and were mainly distributed in the visual network, the default mode network, and the attention network. The changes in brain node parameters were significantly related to the number of training years.