Primary Study On White Matter Network In Patients With First-episode Untreated Depression Based On Diffusion Tensor Imaging

Objective : Based on diffusion tensor imaging and analysis method of graph theory to explore the possible changes of global and local topological properties of white matter network in patients with first-episode untreated depression,and to find the imaging signs of white matter network in patients with first-episode untreated depression were found from the perspective of white matter network,and then the neuropathological mechanism of depression was further discussed.Methods:This study included 30 outpatients diagnosed with depression in the Department of Mental Health and Neurology of the Bethune First Hospital of Jilin University from January 2020 to April 2021.All patients were first diagnosed,without any treatment,and the course of disease lasted more 2 weeks.Twenty-one normal subjects with the matching age,gender and other data were selected at the same time.All patients were diagnosed with DSM-V depression according to the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders,and the Hamilton Depression Scale（HAMD）-17 score was 14 or greater.The same PHILIPS 3.0T whole body MRI machine was used to collect T1 structural images and diffusion tensor imaging（DTI）images of all subjects in both groups.Firstly,the DTI data of all subjects were preprocessed,including head movement eddy current correction,skull stripping and spatial registration.Then,the whole brain was divided into 90 brain regions with priori anatomical automatic calibration template（AAL）.Based on PANDA software,the deterministic fiber tracking algorithm was used to track the whole brain white matter fiber,determine the existence of fibers between brain regions,and establish the brain white matter network.Finally,GRETNA software based on MATLAB 2013 b was used to calculate the topological attribute indexes of brain white matter network.Shapiro-Wilk test was applied to verify the normality of general clinical data and topological attribute indexes in the two groups.SPSS 25.0 was used to compare the general clinical data between groups.On the basis of controlling covariables such age and gender,the differences of network indicators between groups（two independent sample T test and non-parametric test）are compared for global and local node attribute indexes,when P<0.05,the differences were considered to be statistically significant.For the node attribute indexes,FDR multiple comparison correction was performed to control the false positive rate.Results:（1）There was no statistically significant difference in age,gender and years of education between the two groups（P > 0.05）.（2）The white matter networks in both groups had small-world properties.Compared with the healthy control group,the clustering coefficient（P = 0.007）and local network efficiency（P =0.008）in the depressive group were decreased.（3）The nodal clustering coefficient of the right inferior frontal gyrus of orbit（P= 0.011）and the left middle occipital gyrus（P = 0.002）were decreased in in patients with first-episode untreated depression;the nodal local efficiency of the right inferior frontal gyrus（P = 0.012）were decreased.Conclusions:（1）The depression group and the healthy control group had small-world attribute;The topological properties of the nodes of right orbital inferior frontal gyrus and left middle occipital gyrus were changed.（2）The global and local topological attributes of the brain network of depressive patients were partially changed,suggesting that the local brain network of the enrolled patients was abnormal,and the global network was reserved.（3）Right orbital inferior frontal gyrus and left middle occipital gyrus may be related to the occurrence of depression.