Brain Anatomical Connection Analysis Based On Diffusion Tensor Imaging

The human brain is known as "one of the most complex systems in the universe," consisting of more than 100 billion neurons and 100 trillion nerve connections,which formed an extremely giant complex network.In order to explore the working mechanisms of the brain and the neuropathology of brain diseases,cognitive neuroscience research should be carried out at the level of brain connectivity and networks.As a only noninvasive imaging technique in vivo,diffusion tensor imaging(DTI)provids a powerful tool in the investigation of large-scale human brain structure networks with high spatial and temporal resolution.In this paper,we analyze the characteristics of brain structure network based on DTI imaging.We mainly study the whole brain structure network in the epileptic neuropathological model.We propose a multi-scale modular analysis method and pattern classification method for complex network based on structure network.Besides,we also investigate the damage mechanism of hyperglycemia for the human brain structure network.The study of white matter integrity provides new insights into the neuropathological mechanisms of benign adult familial myoclonic epilepsy.Tract-based spatial statistics(TBSS)is fully automated,does not require the designation of the fiber bundle but covers all fiber bundles,and solves the problem of alignment and smoothing in conventional methods.Therefore,we used TBSS to investigate whole brain white matter changes in the benign adult familial myoclonic epilepsy(BAFME)patients.BAFME is a monogenic disease epilepsy syndrome,which provides a new perspective and opportunity to investigate the epilepsy and design for the medication.The results showed that the FA values in the cerebellum,capsid nucleus,right anterior cingulate lobe and central anterior gyrus were significantly decreased in BAFME patients,which provided new insights into the neuropathological mechanisms of cognitive and motor impairment in patients with BAFME.Cerebellar structural connectivity abnormalities provide a new perspective to the pathogenesis of diabetiesTo further investigate the pathogenesis of diabetes,diffusion tensor imaging deterministic tractography and statistical analysis were employed to investigate abnormal cerebellar anatomical connections in diabetes patients.It was found that there were decreased anatomical connections in the cerebellar and cerebro-cerebellum circuit of T2 DM patients.Duration of illness was significantly correlated with the connections from the superior frontal gyrus to the right cerebellum crus,from the right precuneus to the right cerebellum crus,and from the left cerebellum to the vermis.At the same time,the cerebellum and precuneus may be played a very important role in the pathology of diabetes.The results provided valuable new insight into the underlying neurological pathophysiology of diabetes-related motor and cognitive deficits.Supervised Pattern analysis of the whole brain structure connection revealed the difference and the common network between the left and right temporal lobe epilepsyWe proposed a multidimensional pattern recognition framework based on whole brain structural connections to identify brain connectivity networks of left and right temporal lobe epilepsy(TLE)patients and controls,so as to extract the different and common anatomical network of left and right TLE.The classification results revealed left and right TLE can be identified from each other.In addition,compared with normal controls,left and right TLE showed decreased connections.Compared with the right mTLE,the left mTLE exhibited different connectivity pattern in the cortical-limbic network and cerebellum.At the same time,we found that the left and right TLE showed a similar pattern of abnormal connections,which were mainly located in the limbic-frontal and temporal-occipital subsystems.Our results suggest that structural network differences may serve as a biological label for potential left and right TLE and may explain the neuropathological mechanisms underlying the high incidence of depression in TLE.Multi-scale modularity analysis of complex networkWe proposed the multi-scale modular analysis to study brain structure network in BAFME patients.In this study,we found that there was a significant difference in the network distribution of BAFME patients and normal controls: the brain network of normal group could be divided into 8 modules,the cerebellums belonged to one module;the brain network of the patients group could be divided into 9 Module,the cerebellum is divided into two modules.This study provides new insight into the neuropathological network mechanisms of cognitive and motor impairment in BAFME patients.