Effects of music composition on structural and functional connectivity in the orbitofrontal cortex

Abstract

AbstractAims Composer is a classic case for investigating the plasticity effect of musical composition on the brain. As an essential brain region associated with prediction and decision, the orbitofrontal cortex exhibits the significant difference in many studies between composers and controls. Meanwhile, these studies also show that musical composition induces changes in cognitive networks, such as auditory and attentional functions. However, these structural and functional connectivity changes are often measured independently, making it crucial to exam the link between these two changes in order to further understand the neuroplasticity of musical composition.Methods In this work, we recruited 18 composers and 20 controls under resting-state functional Magnetic Resonance Imaging (fMRI) scanning. First, based on the Tract-Based Spatial Statistics method, we found the differences in white matter skeleton between composers and the controls. Subsequently, we compared the differences in structural connectivity by probabilistic tracing from the orbitofrontal cortex. Finally, we examined the functional difference between groups.Results We found that composers had higher anisotropy scores and mean diffusion rates in white matter regions such as the corpus callosum, anterior radiating corona, anterior and posterior branches of the internal capsule than the control group. Functional connectivity also provided evidence for the more robust relationship between the orbitofrontal cortex and other regions, such as attentional networks.Conclusion This experiment suggests that the structural and functional connectivity between the orbitofrontal cortex and other higher cognitive areas of composers is stronger than nonmusicians, which improves the understanding of the effect of composition training on structural and functional neuroplasticity.

Keywords

• Composer
• diffusion tensor imaging
• functional connectivity
• the based spatial statistics method