| The brain is the most complex and mysterious organ in the human body.The complexity of the structure and function of the brain has always attracted researchers to explore the mysteries of the brain.The rapid development of neuroimaging technology provides a good foundation for brain network research.More and more evidence shows that the network is the physiological basis for information acquisition and processing and cognitive ability expression in the brain,brain network research can provides the basis for the disease diagnosis.Diffusion tensor imaging(DTI)technology can noninvasively measure the distribution of white matter in the brain in vivo,and can quantitatively detect the diffusion characteristics of nerve fiber bundles,and can delineate the white matter in the brain,which can reflect the structure of the white matter fiber in the brain to a certain extent.Brain network is a hot topic in neuroscience research.Brain structural network and brain functional network are key technologies in brain network research.Brain structural network based on DTI is widely used in Alzheimer’s disease,brain aging,Parkinson’s disease,autism,bipolar disorder,schizophrenia and other neuropsychiatric diseases.The fiber tracking of traditional DTI-based brain structural network research is based on individual sample space.Because the MRI data acquisition time is longer,the original data may contain many motion artifacts;magnetic field inhomogeneity,eddy currents,etc.also affect the quality of the imaging data,which will This will reduce the signal-to-noise ratio of the diffusion tensor image;The data resolution of the magnetic resonance scanners can be relatively lower compared with that of the real brain nerve fibers,Moreover,due to the complicated structure of the brain fibers,the variety of fiber connection patterns,and the fiber tracking is susceptible to noise,and so on,which shows the limitations of accurately brain fiber tracking based on the sample individual space and that may affect the reliability of DTI-based brain structural network analysis results.Therefore,there is some room for improvement in the traditional DTI-based brain structural network analysis research.In order to research and analyze this topic,this paper first introduces the traditional DTI-based brain structural network construction process,and applies it to patients with neuromyelitis optica spectrum disorder(NMOSD).Diffusion tensor imaging was performed on 41 NMOSD patients(patient group)and 40 age-and sex-matched healthy volunteers(control group)who were admitted to the Department of Neurology of the Third Affiliated Hospital of Sun Yat-sen University from September 2014 to October 2017,using deterministic fiber tracking technology to construct the white matter structural weighted network,and then calculate the brain structural network properties based on complex graph theory analysis,using statistical methods to compare the global and local parameters of the two groups of brain structural network.The results showed that the two groups of brain structural networks both exhibited small world properties.Compared with the control group,the global efficiency of the brain structural network in the patient group was decreased significantly,and the shortest path length increased significantly,The difference was statistically significant(P=0.0017,P=0.0022,FDR correction).There was no statistically significant between the clustering coefficient,the average shortest path length,the small world property value,the average clustering coefficient and the local efficiency of the 2 groups of brain structural networks(P=0.7801,P=0.4959,P=0.2790,P=0.2688,P=0.0502,FDR correction).Compared with the control group,the nodal efficiency of the brain structural network of the patient group in the frontal lobe(bilateral precentral gyrus,middle frontal gyrus of right orbital part,Inferior frontal gyrus of right opercular part,right rolandic operculum,bilateral median cingulate and paracingulate gyri),parietal lobe(right posterior cingulate gyrus,right superior parietal gyrus,left inferior parietal of angular gyri,right angle gyrus,right precuneus),temporal lobe(bilateral hippocampus,right parahippocampal gyrus),occipital lobe(left cuneus,left superior occipital gyrus,bilateral middle occipital gyrus,left inferior occipital gyrus)and subcortical region(right caudate nucleus,right thalamus)were significantly decreased,with statistically significant differences(P<0.05,FDR correction).There is abnormal connection in brain structural network based on DTI of NMOSD patients.Then,considering the limitations of the traditional DTI-based brain structural network analysis based on individual space,relied on the original analysis framework,this paper proposes a concept of constructing DTI brain structural network based on standard space to optimize traditions and introduce the process and method in detail.The basic idea is to standardize the individual tensor image space to the standard space,extract the FA image in the standard space,and perform fiber tracking in the standard tensor map space.In the standard space,the brain structural network is constructed according to the brain regions and the fiber connections between the brain nodes.We choose the DTI-ToolKit(DTI-TK)as our registration tool which based on tensor registration algorithm with higher registration accuracy and the reference image is high quality human brain map IIT v4.1.The improved brain structural network analysis process is used in the research and analysis of the brain structural network of NMOSD patients.The preliminary results show that the results of the improved DTI-based brain structural network analysis process are in good agreement with the original method in the overall properties and the nodal efficiency of the brain structural network,but the statistical results are more significant.The result shows that the improved method is more reliable and shows the superiority of the new method.The improved brain structural network analysis method detected a significant decrease in the efficiency of more brain network nodes,and the rationality of the results should be further analyzed and studied in combination with the clinical manifestations of the disease.In addition,this new method can also be further analyzed and tested with the help of simulation data and other disease data. |
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