advanced image analysis techniques to study large scale functional networks in the human brain
With the development of neuroimaging technology, multimodal image and image analysis techniques have gained increasing interests in the neuroscience research, especially the advanced image analysis techniques (such as graph theory), which through different modeling method to extract and analyze the feature information of human brain images. Most studies have shown that many neurological and psychiatric disorders (such as schizophrenia, depression, etc.) are not individual brain structure or function abnormalities, but a brain network abnormality or circuit loop abnormality disease. Therefore, from brain structural characteristics and the interaction between these regions (for example, through the brain network construct) to analyze the neural mechanism of mental and neurological diseases disease, it can provide a more comprehensive understanding. For the research of the network, especially the complex and large-scale network, the most basic theory is graph theory. This is the most important part of my PhD program. Graph theoretical measures can characterize one or several aspects of local and global network properties. Such as degree of the node, local clustering coefficient, characteristic path length, small world topology, network modularity structure and so on. For the brain structure image segmentation, registration and other techniques are utilized to calculate the cortical thickness, area, volume of subcutaneous nuclei, anaphora index. In short, the advanced imaging analysis techniques can be a promising tool to combine multi-modal image characteristic indicators to explore the neural mechanisms of disease.