CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor刘冰
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword精神分裂症 弥散张量磁共振成像 白质纤维 脑网络
Other AbstractSchizophrenia is a kind of persistent, chronic and severe psychiatric disorder. The incidence of schizophrenia in general population is about 1% and the clinical manifestations are complex and varied. Since the diffusion tensor imaging technique was applied in this area, lots of studies have started to use it to explore the deficits of white matter in schizophrenia, but the results were often different and hard to repeat. In this study, we used four site datasets with large samples to analyze the abnormalities of white matter and network in schizophrenia, with the methods of atlas based analysis, voxel based analysis, fiber tracking and so on, in order to obtain consistent and stable results.
Firstly, we used the method of atlas based analysis to analyze the deficits of 56 white matter fibers in the atlas in the part of the analyses of whiter matter deficits in schizophrenia and found that the mean diffusivity (MD) values of ten white matter fibers increased in schizophrenia, mainly in the corpus callosum, fornix, right superior longitudinal fasciculus and left superior corona radiata. The MD values of some white matter fibers were found to be associated with the age of onset, illness duration and PANSS negative symptom. Meanwhile, in order to verify whether the results are consistent under different methods, we added a complementary analysis with the method of voxel based analysis and the results accorded with the above. Then, we extracted some brain regions involved in schizophrenia as seed points in the result of meta analysis of schizophrenia and analyzed the abnormalities of white matter connections between these areas. Five connections were found to be abnormal with increased MD values in schizophrenia and were mainly distributed in corpus callosum, superior frontooccipital fasciculus, superior corona radiata and superior longitudinal fasciculus.
The method of graph theory was applied to study the brain network in schizophrenia and healthy subjects. Firstly, we analyzed the structural connectivity pattern of the default mode network (DMN) in a healthy population and found its association with memory and anxiety. Then, in the four site datasets with large sample size, the DMN structural network was constructed both in schizophrenia and health controls and the connection between left and right superior prefrontal cortices, the nodal degree of left and right superior prefrontal cortices and left temporoparietal cortex and the nodal efficiency of the left and right superior prefrontal cortices were significantly reduced in schizophrenia. Besides, the degree and global efficiency of the whole DMN were also decreased in schizophrenia.
Document Type学位论文
Recommended Citation
GB/T 7714
陶艳. 基于弥散磁共振的精神分裂症网络异常模式研究[D]. 北京. 中国科学院大学,2015.
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