|Place of Conferral||北京|
|Keyword||静息态功能网络 工作记忆 荟萃分析 多任务学习 特征选择|
Schizophrenia is a complex mental illness. The endeavor to exploring the pathogenesis of mental disorders not only contributes to the diagnosis and treatment of the disease itself, but also promotes the understanding of human brain, as the most complex and vital organ of human. Brain network analysis based on Functional magnetic resonance imaging (FMRI) has become an important method to explore the pathophysiology of this disease.
The existing studies support schizophrenia as a mental disorder of improper functional integration, manifested by abnomral structural or functional connections between brain network structures. However, due to the complexity of schizophrenia itself, the limitations of the existing observation technology and small the sample size, the results of previous studies give us limited and vague conclusions. A bottom up and point to surface strategy by forcusing on each dimension of the schizophrenia symptoms may be an effective path to explore such a disease. In addition, with the advance of the field, the researchers began to use machine learning technology and other advanced data analysis tools to to directly solve the problem of automatic diagnosis of mental illness. The aims of this dissertation are to investigate the imparied resting-state functional connections in schizophrenia, especially for the working memory core network, and try to use machine learning techniques to explore the ways of automatic diagnosis of schizophrenia. The main contents and contributions of this dissertation are as follows:
1. Meta-analytic activation modeling-based parcellation (MAMP). Choosing the appropriate brain structure as the node is important in brain network analysis.In general,we emphasize that each node is a functionally independent brain region. Therefore,the study of how to partition the human brain to such regions is one of the topics of brain network technology. In this work, we propose a new parcellation scheme that uses “modeled activation” pattern across the experiments related with the region of interest (ROI). By estimating the activation value of each voxel in each experiment as its activation mode and calculating the similarity of the two voxels, the clustered sub-regions are obtained. We compare the results with cytoarchitecture results and MACM results, and demonstrate the effectiveness and advantage of this method. In addition, using the experimental task information in the meta data, we can deduce the functional portraits of each subregion by comparing the distributions of the activation points corresponding to each sub-region and the whole region of interest.
2. The functional connectivity working memory “core network” in schizophrenia was investigated. Based on the existing meta-analysis de?ned working memory “core network” that mainly located in the frontal lobe, parietal lobe, and insular, we calculated functional connectivities between these nodes and compaired between the two groups of schizophrenia patients and normal. The study found that patients with schizophrenia showed reduced functional integration (decreased connectivity) in four connections. The impairment of these functional connections may imply that the patient’s ability to suppress irrelevant information in the execution of a working memory task and the allocation of attention are compromised. Oweing to the multicentre large sample data, we were able to use a multicenter meta-analysis stratergy in statistical analysis to improve the statistical power.
3. A multi-task learning method was applied in feature selection and classification on multicenter large sample schizophrenia dataset. It is a high-dimensional and small-sample classfication problem when we use functional connectivitiy features to classify the patients and the normal. Therefore, feature selection is a critical step. Due to the heterogeneity of schizophrenia patients and multicenter imaging, feature selection on each dataset separately result in inconsistency across data sites.
To address this issue, we adopt the multi-task learning method, which adopts L12 norm to constrain the feature selection of different datasets, so that the selected feature contributes to the classification on all data sets. We found that several functional connections have consistent contributions to classification tasks, which may reflect the core pathological features of the psychiatric class. Additionally, we found that, in order to ensure high classification accuracy, the classifier must incorporate most of the functional connection features. These features are widely distributed in the whole brain, so we speculate that schizophrenia is a diffuse neurophysiological psychiatric disorder.
|杨勇. 精神分裂症的脑网络表征[D]. 北京. 中国科学院研究生院,2017.|
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