基于弥散张量成像的脑白质微结构分析方法在视路损伤中的应用进展

视觉通路包括视神经、视交叉、视束、视放射及视皮质。常规磁共振检查技术难以发现视路损伤后白质纤维微结构改变,眼科学检查也存在一定的局限性及主观性,且不能探测后视路的变化。弥散张量成像(diffusion tensor imaging,DTI)作为一种新兴的磁共振成像技术,通过各种后处理分析方法结合不同的参数进行分析,可提供组织的微结构信息,并能够直观显示活体白质纤维束,在无创地探索疾病的神经病理机制、评估预后方面起着重要的作用。近年来随着DTI后处理方法的不断创新,其在视路损伤中的研究越来越多。本文在介绍DTI的主要参数及常见脑白质微结构分析方法的同时,阐述了其在视路损伤研究中的应用,并进一步对各种分析方法的优缺点进行总结。     

基于DTI评价急性缺血性脑卒中运动功能缺损程度与肾虚髓亏证的相关性研究＊

目的 运用核磁共振弥散张量成像技术（DTI），从皮质脊髓束损伤程度评价的角度，探讨肾虚髓亏证与急性缺血性脑卒中运动功能缺损程度相关性，丰富中风病病机及证候诊断，拓宽缺血性脑卒中的中医药防治思路，为急性缺血性脑卒中肾虚髓亏证患者运动功能损伤程度提供临床依据，强调肾虚髓亏证在急性缺血性脑卒中运动功能损伤中的重要意义。方法 纳入符合诊断标准的90例病例，根据证候分别归入肾虚髓亏组和非肾虚髓亏组，每组各45例。每组患者均给予常规西药治疗。对两组患者入院后行弥散张量成像检测，同时分别于治疗前及治疗后14天，记录两组患者NIHSS评分、改良Barthel指数量表及简化Fugl-Meyer运动功能评分量表评分，比较两组病例发病时的轻重程度及治疗前后两组病例组间的恢复差异。结果 研究显示，治疗前肾虚髓亏组在NIHSS评分方面高于非肾虚髓亏组（P<0.05）；治疗前肾虚髓亏组在改良Barthel指数量表及简化Fugl-Meyer运动功能评分方面低于非肾虚髓亏组（P<0.05）。治疗前后NIHSS评分、改良Barthel指数评分及简化Fugl-Meyer运动功能评分改善情况，非肾虚髓亏组要优于肾虚髓亏组（P<0.05）。治疗前两组患者在健侧内囊后肢及大脑脚外侧处FA值及ADC值无明显差异；肾虚髓亏组在患侧内囊后肢及大脑脚外侧处FA值及ADC值均低于非肾虚髓亏组（P<0.05）。相关性分析得出，两组患者患侧内囊后肢FA值与患者治疗前NIHSS评分呈负相关；两组患者患侧内囊后肢FA值与患者改良Barthel指数评分及简化Fugl-Meyer运动功能评分呈正相关；肾虚髓亏组患者患侧内囊后肢FA值与肾虚髓亏证中医证候评分呈负相关。结论 肾虚髓亏是急性缺血性脑卒中运动功能障碍的重要病机。研究结果显示，两组皮质脊髓束损伤程度与神经功能及运动功能损伤存在相关性，且肾虚髓亏组在皮质脊髓束的损伤程度方面与其中医证候评分呈负相关。     

White matter alterations in Parkinson's disease with levodopa-induced dyskinesia

IntroductionLevodopa-induced dyskinesia is a complication of levodopa therapy and negatively impacts the quality of life of patients. We aimed to elucidate white matter alterations in Parkinson's disease with levodopa-induced dyskinesia using advanced diffusion magnetic resonance imaging techniques.MethodsThe enrolled subjects included 26 clinically confirmed Parkinson's disease patients without levodopa-induced dyskinesia, 25 Parkinson's disease patients with levodopa-induced dyskinesia, and 23 healthy controls. Subjects were imaged using a 3-T magnetic resonance scanner. Diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging findings were compared between groups with a group-wise whole brain approach and a region-of-interest analysis for each white matter tract. Additionally, logistic regression analysis was used to calculate odds ratios for levodopa-induced dyskinesia.ResultsGroup-wise tract-based spatial statistical analysis revealed significant white matter differences in isotropic diffusion, complexity, or heterogeneity, and neurite density between healthy controls and Parkinson's disease patients without levodopa-induced dyskinesia and between patients with and without levodopa-induced dyskinesia. Region-of-interest analysis revealed similar alterations using a group-wise whole-brain approach in the external capsule, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. These tracts had an odds ratio of approximately 2.3 for the presence of levodopa-induced dyskinesia.ConclusionsOur findings suggest that Parkinson's disease with levodopa-induced dyskinesia produces less white matter microstructural disruption, especially in temporal lobe fibers, than Parkinson's disease without levodopa-induced dyskinesia. These fibers has a more than 2-fold odds ratio for the presence of levodopa-induced dyskinesia and might be associated with the pathogenesis of the sequela.     

磁共振弥散张量成像在脑小血管病中的应用进展

随着人口老龄化进程的加速,脑小血管病(CSVD)的发病率也在不断上升,其导致的多种症状严重影响了患者的生活质量。MRI弥散张量成像(DTI)作为新近发展的影像技术,有无创显示白质纤维超微结构等优点,已被广泛用于疾病早期诊断、病情预测、治疗评估等多方面的研究。本文即就DTI技术在CSVD中的研究现状和未来发展前景进行综述。     

Identifying juvenile myoclonic epilepsy via diffusion tensor imaging using machine learning analysis

The aim of this study was to evaluate the feasibility of using a machine learning approach based on diffusion tensor imaging (DTI) to identify patients with juvenile myoclonic epilepsy. We analyzed the usefulness of combining conventional DTI measures and structural connectomic profiles. This retrospective study was conducted at a tertiary hospital. We enrolled 55 patients with juvenile myoclonic epilepsy. All of the subjects underwent DTI from January 2017 to March 2020. We also enrolled 58 healthy subjects as a normal control group. We extracted conventional DTI measures and structural connectomic DTI profiles. We employed the support vector machines (SVM) algorithm to classify patients with juvenile myoclonic epilepsy and healthy subjects based on the conventional DTI measures and structural connectomic profiles. The SVM classifier based on conventional DTI measures had an accuracy of 68.1% and an area under the curve (AUC) of 0.682. Another SVM classifier based on the structural connectomic profiles demonstrated an accuracy of 72.7% and an AUC of 0.727. The SVM classifier based on combining the conventional DTI measures and structural connectomic profiles had an accuracy of 81.8% and an AUC of 0.818. DTI using machine learning is useful for classifying patients with juvenile myoclonic epilepsy and healthy subjects. Combining both the conventional DTI measures and structural connectomic profiles results in a better classification performance than using conventional DTI measures or the structural connectomic profiles alone to identify juvenile myoclonic epilepsy.     

磁共振弥散张量成像技术在神经外科疾病的应用进展

磁共振弥散张量成像(MR-DTI)可以无创地测量体内水的平移运动,提供关于其在不同组织中的各向异性的信息;在组织内水分子进行自由热运动时的各向异性特点进行成像,可以观察到神经纤维束损伤后的定性及定量的变化,对神经纤维束损伤后肢体功能恢复做出比较客观的预后评估。近些年来DTI已广泛应用于神经外科疾病,对疾病的诊断与鉴别诊断、肿瘤定性与分级、降低手术风险、评估术中切除和预后等方面具有重要意义。本文就DTI在神经外科疾病的临床应用做一综述。     

In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester

The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion‐tracked slice‐to‐volume registration (MT‐SVR) method, we aimed to quantify tract‐specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2–8) diffusion scans of the fetal head (1–2 b = 0 s/mm² images and 12 diffusion‐sensitized images at b = 500 s/mm²). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT‐SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto‐occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract‐specific, GA‐related microstructural changes of the developing white matter in human fetal brain.     

Diffusion imaging in dementia with Lewy bodies: Associations with amyloid burden,atrophy, vascular factors and clinical features

IntroductionWhite matter disruption in dementia has been linked to a variety of factors including vascular disease and cortical pathology. We aimed to examine the relationship between white matter changes on diffusion tensor imaging (DTI) in DLB and factors including vascular disease, structural atrophy and amyloid burden.MethodsParticipants with DLB (n = 29), Alzheimer's disease (AD, n = 17) and healthy controls (n = 20) had clinical and neuropsychological assessments followed by structural and diffusion tensor 3T MRI and ¹⁸F-Florbetapir PET-CT imaging. Voxelwise statistical analysis of white matter fractional anisotropy (FA) and mean diffusivity (MD) was carried out using Tract-Based Spatial Statistics with family-wise error correction (p < 0.05).ResultsDLB and AD groups demonstrated widespread increased MD and decreased FA when compared with controls. There were no differences between the DLB and AD groups. In DLB, increased MD and decreased FA correlated with decreased grey matter and hippocampal volumes as well as vascular disease. There was no correlation with cortical florbetapir SUVR. The relationship between DTI changes and grey matter/hippocampal volumes remained after including Cumulative Illness Rating Scale-Geriatric vascular score as a covariate.ConclusionsWidespread disruption of white matter tracts is present in DLB and is associated with vascular disease, reduced hippocampal volume and reduced grey matter volume, but not with cortical amyloid deposition. The mechanism behind the correlation observed between hippocampal volume and white matter tract disruption should be investigated in future cohorts using tau imaging, as hippocampal atrophy has been shown to correlate with tau deposition in DLB.