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.

Funding: This work was supported by grants from the 973 project 2011CB707803, the National Nature Science Foundation of China (grant number: 81271547, 81201159, and 91232725) and the Chinese Fundamental Research Funding for Central Universities (grant number: ZYGX 2011J097, ZYGX2012J110). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

As is widely known, musicians represent an ideal model to investigate experience-driven plasticity changes in the human brain related to their long-term complex musical training and performances [1], [2]. In the past ten years, many researchers have focused on the neuroplasticity of musicians' brains using diverse technologies and methods, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), diffusion-weighted imaging (DWI), voxel-based morphometry (VBM), and surface-based morphometry (SBM).

In fMRI studies, musicians have exhibited different activation patterns during specific tasks. For example, musicians displayed increased activation of the premotor, primary, and supplementary motor cortices during motor or auditory tasks [3], [4], [5]; enhanced activities in the left middle and superior temporal gyri, the left inferior frontal gyrus, and the right ventromedial prefrontal cortex in response to pattern deviation [6]; greater activation in the bilateral visual cortex during memory retrieval [7]; and unique activities in the auditory association cortex during conceptual processing of visually presented musical instruments [8]. Based on EEG data, researchers showed that musicians exhibit enhanced event-related brain potentials in response to sound omissions and auditory-evoked potentials during specific auditory tasks by analyzing the mismatch negativity (MMN) component [9] and the P2 and N1 components separately [10], [11]. MEG studies have shown increased somatosensory representation of the fingers of the left hand in string players compared to control group [12], as well as an increased auditory cortical representation in musicians versus non-musicians [13], [14]; additionally, the locations of the equivalent current dipoles (ECDs) for the noise burst were significantly posterior to the ECDs for the tones in the two hemispheres of the musicians, but were not in the non-musicians [15]. These event-related functional imaging results suggested that musicians had better performances in specific sensory, motor, and auditory tasks. Musical training may have triggered intrinsic plasticity in the corresponding cortices. Other fMRI studies have also observed reduced activation of primary motor cortical areas in musicians, they deduced that possibly due to fewer activated neurons showing increased efficiency [16], [17]. Moreover, in our previous resting-state fMRI study, enhanced integration of the motor and perceptual systems was observed in musicians [18]. In summary, these findings demonstrated that intensive musical experience could induce changes in functional plasticity in the human brain and may increase the efficiency of integration and the processing of motor, auditory, and visual information.

VBM, SBM and DWI-based approaches have been used to study anatomical plasticity. VBM studies have resulted in some evidence of structural differences in the gray matter of musicians and non-musicians. Researchers found that these differences were distributed in several regions in the brain, including the anterior corpus callosum [19], the planum temporale [20], [21], the primary hand motor area, the cerebellum [22], [23], [24], Broca's area [25], the right auditory cortex [26], and Heschl's gyrus [27]. Recently, using SBM, Li et al. [28] found that musicians showed greater local variability in the middle section (i.e., somatotopic hand area) of the right central sulcus (CS) and the lower section of the left CS compared to the controls. In conclusion, studies focused on gray matter features have shown that intensive musical experience may result in the increased volume or local variability of the surface of gray matter regions related to musical training.

Taken together, these functional and structural studies found that musicians had different neuronal activation patterns and/or anatomical features in some sensory-, motor-, auditory-, and visual-related brain regions in comparison with non-musicians. These findings may reflect the stronger functional demands of these regions during musical training and performance and lend support to the hypothesis that training induces plasticity in these regions.

In contrast to the gray matter, the few studies have investigated the architectural changes in the white matter (WM) in response to musical training. The regions with group differences include the corpus callosum (body, genu, and splenium), the posterior limb of the internal capsule, and the right superior longitudinal fasciculus, which showed altered diffusion parameters, such as fractional anisotropy (FA) and diffusivity, in the WM of musicians versus non-musicians [29], [30]. Imfeld and colleagues observed that the FA values of musicians were bilaterally lower than those of non-musicians in the corticospinal tract, and diffusivity was negatively correlated with the onset of musical training in childhood [31]. Recently, using diffusion tensor tractography, an increased volume and number of streamlines of WM were observed in the right cerebellum in musicians [32]. These findings showed that musical training induced plasticity in the WM fibers that convey sensory and motor information.

The human brain is a complex network using multiple scales of time and space [33]. Numerous studies have explored the functional and structural networks in the human brain using complex network approaches and, in recent years, graph theory. Within graph analysis, the brain is modeled as a graph comprising N nodes connected by M edges. Based on the graph, through a rich set of mathematical tools and theoretical concepts, we can understand brain network more integrally. DWI tractography is a useful method that can be used to map human WM networks [34], [35], [36], [37], [38]. Gong and colleagues [39] showed that human WM structural networks showed prominent “small-world” attributes with embedded pivotal regions and exponentially truncated power-law topological distribution in long-range WM tracts. The small world topology could reveal the efficiency of a brain and play a central role in cortical information processing [40]. Many researches about neurological and psychiatric disorders showed that the small world efficiency of brain are changed in those patients with different kinds of diseases [41], [42]. Although many studies have demonstrated that long-term musical training can induce functional and structural changes in the brain, the training-induced influences on the WM network are still poorly understood. As far as we know, there is no study reporting alterations in the topological organization of the WM networks in musicians. Musical training could cause changes in WM plasticity, which are likely to be reflected in changes in the microstructure; therefore, changes in plasticity may also lead to changes in the topological organization of the WM networks. To investigate if and how the topological properties change, probabilistic diffusion tractography and graph theoretical approaches were used to analyze both musicians and controls.