Clinical Characteristics And Multimodality MRI Study On Posterior Horn Of Lateral Ventricle White Matter Lesions

Background: White matter lesions(WMLs) are frequently seen on brain MRI of elderly subjects. In previous studies, age and hypertension were reported to be the most common risk factors for WMLs, other risk factors, such as diabetes mellitus, hyperlipidemia, smoking, high body mass index, decreased vitamin B12, and alcohol have yielded inconsistent associations with WMH. It is suggested that periventricular white matter lesions(PVWMLs) is related to the risk of brain atrophy, whereas deep white matter lesions(DWMLs) is related to cerebrovascular events. The nature and severity of cognitive impairment associated with WMH will also depend on the volume and location of lesions, and probably on other factors such as brain reserve. The concept of a spatially varying vulnerability to WMLs is important because it might reflect differences in pathophysiological mechanisms, and as such be of potential use to the design of clinical intervention. Furthermore it may help inform the interpretation of clinical MRI scans showing white matter hyperintensity, and to establish a firmer basis for defining patterns related to normal versus pathological ageing. Much has been learned about WMLs progression and its clinical meaning. However, whether reduction of progression is a valid therapeutic target and, if so, what kind of intervention to use and when to start, is still open to question, and should be the subject of future studies. Few studies however, have investigated these relationships between risk factors and WMLs at different locations. Posterior horn of lateral Ventricle WMLs(PHLVWMLs) were the most common WMLs in In clinical work, but it clinical characteristics and correlation with deep white matter and cerebral cortex remain unclear. In this article we investigated clinical characteristics of PHLVWMLs and examined it effect on the gray matter and white matter using multimodality MRI included diffusion tensor imaging(DTI), voxel-based morphometry method of magnetic resonance imaging(VBM-MRI), Blood oxygenation level dependent functional magnetic resonance imaging(BOLD-f MRI).Methods: 479 patients, divided into two groups according to with or no deep white matter lesions disease. The scores of PVWMLs and risk factors were analyzed statistically for the two groups. analysis of DTI image of 13 cases with and 12 cases with no posterior horn of lateral ventricle white matter lesions(PHLVWMLs) to confirme the early stage DWMLs combined with them.In case-control study of 97 patients with PHLVWMLs and 73 controls,univariate analysis screened the related factors. Subsequently the factors with statistical significance in univariate analysis as the final candidate variables were conducted to binomial logistic regression analysis to identify clinical factors with an independent association with the presence of PHLVWMLs.Voxel-based morphometry(VBM) analysis of 97 cases PHLVWMLs and 73 controls was performed using VBM8 toolbox and SPM8 in Matlab environment.The Mini Mental State Examination(MMSE) was performed for 15 PHLVWMLs and 12 controls(CN) to distinguishe the cognitive impairment feature. analysis by SPM8 to screen the related brain regions. BOLD-MRI were acquired on a 3T scanner in a delayed digital match memory task and analysis by SPM8 to screen the related brain regions.Results: Anterior horn, posterior horn, side periventricular WMLs, gender, anterior horn, posterior horn, periventricular white matter lesions, history of smoking, hypertension, diabetes mellitus, in age, blood uric acid were significantly different between DWMLs group and control group(p <0.05); Gender, anterior horn, posterior horn, side ventricle wall narrator matter lesions, history of smoking, hypertension, history of diabetes, age, blood uric acid were significantly associated with deep white matter lesions(p<0.05). Binomial logistic regression analysis showed the angle, the anterior horn, the lateral ventricles of the white matter, hypertension in the regression equation has statistical significance(p<0.05), PHLVWMLs has the highest OR value. DTI analysis showed that there was a significant difference between the PHLVWMLs and the CN group(p<0.05, no correction).Univariate analysis showed that TC, TG, LDL-C and Apo-B were statistically different between PHLVWMLs and CN groups(p<0.05) by Student’s t-test, gender and age were positively, while LDL-C and TG was negatively related to PHLVWMLs by Pearson correlation coefficient test(p<0.05). Multivariate analysis indicated aging is the risk factor[OR value 95% CI:1.09(1.04-1.14);p=0.00],and LDL-C is the protective factor[OR value 95% CI: 0.37(0.16-0.89);p=0.03]for PHLVWMLs independently.VBM analysis revealed significant gray matter volume loss at SupraMarginal_R、Angular_R 、 Insula_L 、 Temporal_Mid_R and Cingulum_Ant_R of PHLVWMLs group compared with CN group(P<0.001, without revision). Multivariate analysis showed that OPVH significantly independently positive correlated with Precentral_L and Insula_L by multiple regression analysis of VBM(p(FWE-corr)<0.05).PHLVWMLs group had significantly lower scores of Total, IM, DM compared with CN group(p<0.05). Analysis of BOLD-MRI demonstrated that left Insula, Precentral Gyrus and Heschl of OPVHs group have decreaed activated brain region compared with CN group(p(unc)<0.005). Multivariate analysis showed that PHLVWMLs significantly negatively correlated with the reduced brain areas independently(p(unc)<0.005).Conclusion: 1. PHLVWMLs may correlate with DWMLs, PHLVWMLs combined with deep white matter abnormalities; 2. Age is the risk factor, while LDL-C may be the protective factor for PHLVWMLs independently; 3. Regional gray matter atrophy coexisted with PHLVWMLs mainly located in Precentral_L and Insula_L. 4. PHLVWMLs maybe mainly damage immediate memory by affecting Insula and Heschl.