The Research Of Cerebral Cortex Structural Network Algorithm And The Application In Schizophrenia

The human brain is known to be the most sophisticated and complex entity system in the world. From a functional perspective, the brain is responsible for the control of the individual’s early high-level behavior. In the structure, the brain has about 1011 neurons and 1015 synapses connected these neurons.The cerebral cortex networks were found having few dense local clustering(hubs) nodes and a lot of average low number of connections nodes, so that fast communication could have minimal energy cost. The interconnection hubs, which were thought to be the energy-efficient regions(densely connected nodes), have been found that the abnormalities in their configuration always link to neuropsychiatric diseases abnormalities in their configuration. Although there have been a number of studies investigating finding functional connectivity hubs, little is known about the network hubs of structural connections in the human cerebral cortex.Schizophrenia is a common mental illness, which etiology is unclear. The current approach of diagnosis is based on its clinical symptoms, because there is not objective biological markers. At present, the study of magnetic resonance imaging in schizophrenia is focused on functional magnetic resonance imaging and structural magnetic resonance imaging based on the brain region. Our study propose a method of data driven research based on the voxel level of the brain structure network. In this paper, the main contributions are as follows:1.The interregional statistical associations in cortical thickness was demonstrated to provide important connectivity information in the human brain in previous studies.And considering the cortical thickness reflects the size, density and arrangement of cells,we proposed an voxel-wise data-driven structural connectivity analysis method on morphometric feature, termed “structural connectivity density mapping”. We first generate a model of the GM-WM and GM-CSF surfaces with vertices per hemisphere to determine the consistent structural region to identify the cortical thickness. The structural connection between two cortical voxels is identified if they had significant covariation of cortical thickness across a population. Then the SCDM was assessed in global, short and long scale across normal subjects in several research centers. Finally,the stability analysis was done by computing the correlation of short SCD of random subsample with all healthy subjects.2. By using the brain structural connectivity density algorithm, we compared the local and long SCDM of the schizophrenia patients and healthy subjects, and the difference of the contrast was projected to the brain areas corresponding to the automatic anatomical labeling template. These schizophrenia patients have higher local structural connectivity density in the bilateral medial superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus than healthy subjects. But in the precentral gyrus,precuneus, posterior central gyrus, superior temporal gyrus and rostral gyrus the schizophrenia patients are significantly lower than that of healthy subjects. And schizophrenia patients have higher long structural connectivity density in the medial frontal gyrus, inferior frontal gyrus and precuneus cover brain areas than normal subjects, and in the precentral gyrus, posterior central gyrus, insula, and pillow to brain areas but was significantly lower than that of healthy subjects of long-range structure connection density. The abnormality of these brain areas is very important to the pathology of schizophrenia, that means that the brain structural connectivity density algorithm has very important significance for the identification and diagnosis and treatment of schizophrenia patients.The contents of this study include proposing and validating the algorithm, and in the spirit of the split data of patients in the clinical data validation studies indicate that brain structure connection density algorithm, reliable, stable and practical, for the study of the brain structure connected network bring a new way.