Analysis Of Anatomical Mri And Applications In Musicians’ Brain

Magnetic resonance imaging(MRI), especially the structural MRI(s MRI) and diffusion-weighted imaging(DWI), has the characteristics of noninvasive, high-definition images and high contrast. Therefore, MRI is well-suited to anatomical imaging and research of brain tissue. It is also a great imaging tool to explore the relationships between brain structure and development, disease or plasticity. It can be used to analyze the architecture of gray matter and white matter based on s MRI data, especially the concentration, cortical thickness, cortical surface area, and cortical folding complexity of gray matter. We can obtain the attributes of white matter and the streamlines of fiber bundles and build the fiber-connection networks for complex network analysis. Exploration of neural plasticity, particularly the plasticity changes induced by long-term regular study and practice, is feasible and significant to understand human brain. Musicians experienced a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Consequently, brains of musicians are the ideal model for investigating neural adaptations. Musicians and age-, gender- and education-matched non-musicians were recruited as subjects in this thesis. Brain MR structural images of the two groups were analyzed by using several approaches to explore the brain structural plasticity changes induced by long-term musical training and playing. We obtained some meaningful results and evidences through these studies. The main works and findings in this thesis were introduced as following:1. The s MRI images of two groups were analyzed using voxel-based morphometry(VBM) method in this study. Firstly, affine transformation with 12 parameters, for each subject, was applied to register between s MRI image and T1 template in standard space. Secondly, images from last step were segmented to gray matter, white matter, and cerebrospinal fluid. Finally, gray matter images of two groups were used to make voxel-by-voxel statistical and contrast analysis in the standard space. Through these steps, voxel clusters with significantly difference between two groups were obtained. In this study, we found that musicians showed significantly enhanced gray matter concentration in several regions including bilateral cerebellum, middle temporal gyrus and left superior occipital gyrus. These results showed that long-term musical experiences of musicians affected primarily in motor- and auditory-related brain regions.2. In second study, the above s MRI data were analyzed by surface-based morphometric(SBM) method. Firstly, each s MRI image was registered to Talairach standard space. And then gray value correction and non-brain tissue elimination were performed to each image. Secondly, white matter region was marked and from this region interface between gray matter and white matter was found out which was then extended outward to obtain interface between gray matter and CSF. These two interfaces were both represented as vertices. Thirdly, cortical thickness, surface area, curvature, gaussian curvature, folding index, and intrinsic curvature index of each vertex were calculated. And then mean values of these indices of every brain regions, which were divided based on Destrieux Atlas, were obtained. Finally, statistical analysis for each region was performed index-by-index to obtain the indices of brain regions which showed significant differences between two groups. The results showed that long-term musical training may significantly enhanced the cortical surface area, thickness, and/or folding complexity in brain regions related to functions including vision, somatic movement, emotion, motor control, auditory sense, and somatosensory.3. Based on brain DWI and s MRI images, the white matter fiber anatomical networks of musicians and controls were constructed and analyzed. Firstly, each s MRI image were registered separately to DWI image and T1 template in standard space. The normalized s MRI image was then segmented to three tissues and the gray matter was divided into 90 regions based on AAL atlas, which was subsequently obtained in b0 space derived from the inverse transformation between s MRI image and DWI image. Secondly, probabilistic fiber tracking from each region in b0 space to the whole brain were performed and the connectivity strength of each pair of regions, even the whole brain network, were obtained. Thirdly, global and local topological attributes of network of each subject were calculated by graph theory. Finally, statistical analysis for each region at each attributes was made to find out the differences of white matter anatomical network between the two groups. This study revealed that musicians showed significant differences compared with the controls in white matter network attributes of motor system, visual system, supramarginal gyrus, basal ganglia regions, and orbital part of frontal lobe. Consequently, the plasticity changes of neural fibers of musicians were indicated mediately.4. Principal component analysis(PCA), combined with support vector machine(SVM), was applied to analyze the white matter anatomical networks of two subjects above in another perspective. Firstly, PCA was utilized at the integration of networks of all subjects by using singular value decomposition(SVD); Then, SVM classification with leave-one-out cross validation was applied to each right singular vector and the main component corresponding to the singular vector with best effect of classification was obtained. Finally, connections corresponding to the elements with highest contribution to this component were found out. Then the main connections were obtained which represent the changes in the musicians’ white matter anatomical networks compared to non-musicians. Results in this study indicated that musicians showed enhanced information transfer efficient in motor-, auditory-, emotional-, and memory-related brain regions.To sum up, by analyzing two brain MR structural images of musicians and non-musicians from several different aspects, we found that long-term musical training and playing can induce anatomical plasticity changes in musicians’ brains. Principally, these changes were mostly the improvement of gray matter and/or white matter anatomical attributes in brain regions of somatic movement, auditory, and motor control functions and the enhancement of information-transmission efficiencies between these regions. Furthermore, musicians also showed plasticity changes in several emotion-, vision-, and memory-related regions more or less. These findings extended our understanding of brain anatomical plasticity correlated with long-term and routine musical experiences, and made new evidences in multi perspectives.