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.
Diffusion tensor imaging (DTI) is a rapid development technology in recent years of magnetic resonance imaging. The diffusion tensor interpolation is a very important procedure in DTI image processing. The traditional spectral quaternion interpolation method revises the direction of the interpolation tensor and can preserve tensors anisotropy, but the method does not revise the size of tensors. The present study puts forward an improved spectral quaternion interpolation method on the basis of traditional spectral quaternion interpolation. Firstly, we decomposed diffusion tensors with the direction of tensors being represented by quaternion. Then we revised the size and direction of the tensor respectively according to different situations. Finally, we acquired the tensor of interpolation point by calculating the weighted average. We compared the improved method with the spectral quaternion method and the Log-Euclidean method by the simulation data and the real data. The results showed that the improved method could not only keep the monotonicity of the fractional anisotropy (FA) and the determinant of tensors, but also preserve the tensor anisotropy at the same time. In conclusion, the improved method provides a kind of important interpolation method for diffusion tensor image processing.
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.
Present study used diffusion tensor image and tractography to construct brain white matter networks of 15 cerebral palsy infants and 30 healthy infants that matched for age and gender. After white matter network analysis, we found that both cerebral palsy and healthy infants had a small-world topology in white matter network, but cerebral palsy infants exhibited abnormal topological organization: increased shortest path length but decreased normalize clustering coefficient, global efficiency and local efficiency. Furthermore, we also found that white matter network hub regions were located in the left cuneus, precuneus, and left posterior cingulate gyrus. However, some abnormal nodes existed in the frontal, temporal, occipital and parietal lobes of cerebral palsy infants. These results indicated that the white matter networks for cerebral palsy infants were disrupted, which was consistent with previous studies about the abnormal brain white matter areas. This work could help us further study the pathogenesis of cerebral palsy infants.
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 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.