Objective To investigate the pathological mechanism of epileptic comorbid sleep disorder by analyzing the changes of cerebral white matter diffusion tensor in patients with sleep disorder with negative magnetic resonance imaging (MRI) epilepsy based on the method of tract-based spatial statistics (TBSS). Methods MRI negative epilepsy patients comorbid sleep disorder who were epileptic patients treated l in China-Japan Union Hospital of Jilin University from January 2020 to December 2022 completed the Epworth sleepiness scale (ESS) and Pittsburgh sleep quality index (PSQI) tests, and those who complained of sleep disorder and PSQI index ≥11 were monitored by nighttime polysomnography (PSG) and those with objective sleep disorder confirmed by PSG were included in the epilepsy comorbid sleep disorder group. Healthy volunteers with matching gender, age, education were included in the health control group. Diffusion tensor image ( DTI) was collected for all subjects by using a 3.0T magnetic resonance scanner. Diffusion parameters were compared between the two groups using TBSS. Results This study included 36 epilepsy patients comorbid sleep disorder and 35 healthy volunteers. epilepsy patients comorbid sleep disorder showed significantly lower fraction anisotropy (FA) (P<0.05) and significantly higher mean diffusivity (MD) (P<0.05) than the health control group . Brain regions with statistical differences in FA reduction included middle peduncle of cerebellum, genu of corpus callosum, body of corpus callosum, splenium of corpus callosum, anterior corona radiata, external capsule and right posterior thalamic radiation.Brain regions with statistical differences in MD degradation included genu of corpus callosum, body of corpus callosum, anterior limb of internal capsule, anterior corona radiata, superior corona radiata, external capsule and right posterior limb of internal capsul. Conclusion Patients with epilepsy comorbidities with sleep disorders have widespread and symmetric white matter damage.The white matter damage is concentrated in the front of the brain.
In transcranial magnetic stimulation (TMS), the conductivity of brain tissue is obtained by using diffusion tensor imaging (DTI) data processing. However, the specific impact of different processing methods on the induced electric field in the tissue has not been thoroughly studied. In this paper, we first used magnetic resonance image (MRI) data to create a three-dimensional head model, and then estimated the conductivity of gray matter (GM) and white matter (WM) using four conductivity models, namely scalar (SC), direct mapping (DM), volume normalization (VN) and average conductivity (MC), respectively. Isotropic empirical conductivity values were used for the conductivity of other tissues such as the scalp, skull, and cerebrospinal fluid (CSF), and then the TMS simulations were performed when the coil was parallel and perpendicular to the gyrus of the target. When the coil was perpendicular to the gyrus where the target was located, it was easy to get the maximum electric field in the head model. The maximum electric field in the DM model was 45.66% higher than that in the SC model. The results showed that the conductivity component along the electric field direction of which conductivity model was smaller in TMS, the induced electric field in the corresponding domain corresponding to the conductivity model was larger. This study has guiding significance for TMS precise stimulation.
This study aims to detect early changes of kidney in patients with primary hypertension by 3.0 T functional magnetic resonance imaging (fMRI). 26 patients with primary hypertension (hypertension group) and 33 healthy volunteers (control group) underwent conventional and functional magnetic resonance scans, which included blood oxygen level-dependent (BOLD) MRI, diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). We measured renal cortical thickness (CT), parenchymal thickness (PT), and functional values of renal cortex and medulla including R2* value, apparent diffusion coefficient (ADC) value and fractional anisotropy (FA) value in each group, and then calculated the cortical/parenchymal thickness ratio (CPR). Compared with those in the control group, CT and CPR in hypertension group were larger (P<0.01), cortical and medullar R2* values increased (P<0.01) whereas medullar FA values decreased (P<0.05). It could be well concluded that noninvasive 3.0 T functional MRI would have important clinical significance in identifying early abnormalities of kidney in hypertension patients.
White matter lesion (WML) of presumed vascular origin is one of the common imaging manifestations of cerebral small vessel diseases, which is the main reason of cognitive impairment and even vascular dementia in the elderly. However, there is a lack of early and effective diagnostic methods currently. In recent years, studies of diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown that cognitive impairment in patients with WMLs is associated with disrupted white matter microstructural and brain network connectivity. Therefore, it’s speculated that DTI and rs-fMRI can be effective in early imaging diagnosis of WMLs-related cognitive impairment. This article reviews the role and significance of DTI and rs-fMRI in WMLs-related cognitive impairment.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.
Objective To investigate the feasibility of magnetic resonance diffusion tensor imaging (MRDTI) technique in displaying myocardial fiber architecture. Methods In five ex vivo swine heart, diffusion tensor imaging (DTI) was acquired in 25 directions within 2 hours after excision. The myocardial fiber was reconstructed by using brain white matter tractography algorithm to display its course, distribution and arrangement. Results In the swine heart 1 hour after excision, MRDTI revealed that the arrangement of the myocardial fiber had certain continuity. It spiraled and twisted to form the left and right ventricle. The divection of general myocardial fiber in the left ventricle was vertical below endocardium, horizontal below epicardium and oblique in stratum medium, which is consistent with the theory of ventricular myocardial band. Conclusion MRDTI can reveal the myocardial fiber architecture, showing its integrity and arrangement, and at some level confirming the theory of ventricular myocardial band.
This study aims to determine the salient brain regions with abnormal changes in white matter structures from diffusion tensor imaging (DTI) images of the patients with temporal lobe epilepsy (TLE), and to discriminate the patients with TLE from normal controls (NCs). Firstly, the DTI images from 50 subjects (28 NCs and 22 TLE) were acquired. Secondly, the four measures including the fractional anisotropy (FA), the mean diffusivity (MD), the axial diffusivity (AD) and the radial diffusivity (RD) were calculated. Thirdly, the tract-based spatial statistics (TBSS) was adopted to extract the measures in brain regions with significant differences between the two compared groups. Fourthly, the obtained measures were used as input features of the support vector machine (SVM) for classification, and the support vector machine-recursive feature elimination (SVM-RFE) was compared with the support vector machine-tract-based spatial statistics (SVM-TBSS) method. Finally, the essential brain regions and their spatial distribution were analyzed and discussed. The experimental results showed that the FA measures of the TLE group decreased significantly in the corpus callosum, superior longitudinal fasciculus, corona radiata, external capsule, internal capsule, inferior fronto-occipital fasciculus, fasciculus uncinatus and sagittal stratum, which were nearly bilaterally distributed, while the MD and RD increased significantly in most of these brain regions of the TLE group. Although the AD also increased, the differences were not statistically significant. The SVM-TBSS classifier obtained accuracies of 82%, 76% and 76% using the FA, MD and RD for classification, respectively, and 80% using combined measures. The SVM-RFE classifier obtained accuracies of 90%, 90% and 92% using the FA, MD and RD respectively, while the highest accuracy was 100% using combined measures. These results demonstrated that the SVM-RFE outperformed the SVM-TBSS, and the dominant characteristic influencing classification in brain regions were in associative and commissural fibers. These results illustrated that the measures of DTI images could reveal the abnormal changes in white matter structure of patients with TLE, providing effective information to clarify its pathological mechanism, localize the focus and diagnose automatically.
Objective To explore the difference of white matter changes between bipolar affective disorder and schizophrenia using diffusion tensor imaging (DTI). Methods Patients with bipolar affective disorder and schizophrenia were selected from the Mental Health Center of West China Hospital of Sichuan University between October 2014 and January 2017. Volunteers were recruited from October 2014 to January 2017. The included patients were divided into bipolar affective disorder group and schizophrenia group according to their diagnosis. Volunteers were divided into normal control group. The bipolar affective disorder group was divided into two subgroups: manic episode and depressive episode. DTI was performed on the included patients and volunteers. Tract based spatial statistics (TBSS) was used to study the differences in fractional anisotropy (FA) of white matter between patients and normal controls, and FA values of two subgroups of bipolar affective disorder and schizophrenia were compared. Results A total of 99 patients and 40 normal controls were included in this study. Among them, there were 40 cases in schizophrenia group and 59 cases in bipolar affective disorder group (31 cases of manic episode and 28 cases of depressive episode). Compared with the normal control group, FA values decreased in corpus callosum, fornix, occipital forceps and left inferior longitudinal fasciculus with bipolar affective disorder group and schizophrenia group (P<0.05). There was no significant difference in FA values between bipolar affective disorder group and schizophrenia group (P>0.05), but the FA value in left posterior thalamic radiation decreased in depressive episode of bipolar affective disorder group compared with schizophrenia group (P=0.001). Conclusions There are similarities between white matter changes in bipolar affective disorder and schizophrenia. However, the white matter change in posterior thalamic radiation may be the characteristic change in depressive episode of bipolar affective disorder.