Evaluation of voxel-based group-level analysis of diffusion tensor images using simulated brain lesions

We simulated brain lesions in mean diffusivity (MD) and fractional anisotropy (FA) images of healthy subjects to evaluate the performance of voxel-based analysis (VBA) with SPM2. We increased MD and decreased FA, simulating the most typical abnormalities in brain pathologies, in the superior longitudinal fasciculus (SLF), corticospinal tract (CST), and corpus callosum (CC). Lesion sizes varied from 10 to 400 voxels (10.5 mm³ each) and intensity changes from 10 to 100%. The VBA contained eddy current correction, spatial normalization, smoothing, and statistical analysis.The preprocessing steps changed the intensities of MD and FA lesions from the original values, and many lesions remained undetected. The detection thresholds varied between the three brain areas, and between MD and FA images. Although spatial smoothing often improved the sensitivity, it also markedly enlarged the estimated lesion sizes.Since conventional VBA preprocessing significantly affected the outcome and sensitivity of the method itself, the impact of analysis steps should be verified and considered before interpreting the findings. Our results provide insight into the sizes and intensity changes of lesions that can be detected with VBA applied to diffusion tensor imaging (DTI) data.     

Amyotrophic lateral sclerosis: diffusion tensor tractography and voxel-based analysis

The purpose of this study is to investigate whether the diffusional anisotropy of water molecules is disrupted in the pyramidal and extra-pyramidal regions in patients with amyotrophic lateral sclerosis (ALS). We studied seven patients with probable ALS (four women, mean age +/- SD, 57.3 +/- 6.2 years old) according to the criteria of the World Federation of Neurology. A control group consisted of 11 age- and sex-matched volunteers (six women, 57.1 +/- 4.5) without disorders affecting the central nervous system. Voxel-based diffusion tensor analysis was made with statistical parametric mapping (SPM99). We also created the normalized corticospinal tractography from the diffusion tensor data. The significant fractional anisotropy (FA) decrease in the ALS group was found in the right frontal subgyral white matter and left frontal precentral white matter. These clusters with significant FA decrease corresponded well to the average group map of the corticospinal tract in a standard reference frame. These results suggested that the combination of voxel-based diffusion tensor analysis and diffusion tensor tractography might help determine the location of the affected neuronal tissues among ALS patients in a non-invasive manner.     

基于体素的健康中国成人概率性弥散张量成像脑图谱的建立

目的 使用计算机医学图像处理技术在标准人脑空间建立基于体素的健康中国成人概率性弥散张量成像（DTI）脑图谱,以期为脑白质的功能研究和相关疾病的诊治提供基础信息。 方法 选择50例健康成年志愿者进行常规MRI扫描,确认未发现异常后,再行DTI扫描,获取相应图像数据。首先使用MRIcron软件包中的相关程序,将原始的DICOM数据格式转换为图像处理所需要的格式,然后运用DtiStudio和DiffeoMap软件,结合自动图像配准算法对DTI数据进行数据预处理、张量计算和图像归一化,最后在MATLAB中对图像进行平均计算,构建图谱。结果 成功构建出基于50例健康中国成人的概率性DTI脑图谱,图谱清晰。各向异性（FA）图和彩色编码图上均可见脑白质结构和纤维束走向,在彩色编码图上以特定颜色代表了不同纤维束的走行方向。结论 运用图像处理技术可以构建国人概率性DTI脑图谱。该图谱以通用数据格式保存,可被大部分医学图像处理软件及分析软件识别读取,能为研究脑白质正常结构功能及相关疾病服务。     

Absolute eigenvalue diffusion tensor analysis for human brain maturation

The absolute eigenvalues of the diffusion tensor of white matter in sixteen normal subjects in two groups representing the early developmental stage (ages 1-10 years, n=8) and young adult stage (ages 18-34 years, n=8) were assessed using a high-field (3.0 T) magnetic resonance (MR) system. All three eigenvalues, including the largest eigenvalue, decreased significantly with brain maturation. The rate of the decline in the two small eigenvalues was, however, much higher than that of the largest eigenvalue, resulting in an actual increase in fractional anisotropy, a commonly measured relative index. The data demonstrate that an increase in anisotropy associated with brain maturation represents a significant decline in the small eigenvalue components, rather than an increase in the largest eigenvalue. The observed pattern of eigenvalue changes is best explained by the simultaneous occurrence of two of several independent phenomena within the axonal microenvironment during the myelination process, namely, (1) decline in unrestricted water content in extra-axonal space, and (2) increase in apparent diffusivity within the axon.     

Systematic review and voxel-based meta-analysis of diffusion tensor imaging studies in bipolar disorder

Background

Diffusion tensor imaging (DTI) studies have shown changes in the microstructure of white matter in bipolar disorder. Studies suggest both localised, predominantly fronto-limbic, as well as more widespread changes in white matter, but with some apparent inconsistency. A meta-analysis of white matter alterations in adults with bipolar disorder was undertaken.

Method

Whole-brain DTI studies comparing adults with bipolar disorder to healthy controls on fractional anisotropy (FA) were retrieved using searches of MEDLINE and EMBASE from between 2003 and December 2012. White-matter tract involvement was collated and quantified. Clusters of significantly altered FA were meta-analysed using effect-size signed differential mapping (ES-SDM).

Results

Ten VBA studies (252 patients and 256 controls) and five TBSS studies (138 patients and 98 controls) met inclusion criteria. Sixty-one clusters of significantly different FA between bipolar disorder and healthy controls were identified. Analysis of white-matter tracts indicated that all major classes of tracts are implicated. ES-SDM meta-analysis of VBA studies revealed three significant clusters of decreased FA in bipolar disorder (a right posterior temporoparietal cluster and two left cingulate clusters). Findings limited to the Bipolar Type I papers were more robust.

Limitations

Voxel-based studies do not accurately identify tracts, and our ES-SDM analysis used only published peak voxels rather than raw DTI data.

Conclusions

There is consistent data indicating widespread white matter involvement with decreased white matter FA demonstrated in three disparate areas in bipolar disorder. White matter alterations are not limited to anterior fronto-limbic pathways in bipolar disorder.     

Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging

Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m²; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m²; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC and accuracy over SVM, and either model can be used as a faster OSA screening tool for subjects having brain DTI data.     

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.     

联合应用VBM方法和DTI技术对精神分裂症小脑结构的研究

目的分别应用磁共振扩散张量成像技术(DTI)和基于体素的形态分析法(VBM)对精神分裂症患者小脑中脚和上脚白质纤维情况与小脑的灰白质密度进行初步探究。方法应用GESigna3.0TMRI扫描仪,分别对按照精神分裂症诊断标准确诊的14例患者及14名健康对照者行DTI和三维快速扰相梯度翻转恢复(3D-SPGR)序列扫描,检测双侧小脑中脚和上脚感兴趣区的各向异性分数值(FA)及小脑灰白质密度,应用成组t检验对病例组和正常对照组的以上数据进行比较。结果精神分裂症病例组左侧小脑上脚的FA值较正常对照组显著降低(<0.05),小脑局部脑区的灰质密度与正常组相比发生了显著改变,差异区域主要位于小脑前叶(<0.05),小脑的白质密度与正常组相比未发现显著改变脑区。结论 DTI技术和VBM方法的联合应用对精神分裂症患者的研究有着重要的应用价值。     

Structural brain alterations in bipolar disorder II: A combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) study

Background

Brain structural changes have been described in bipolar disorder (BP), but usually studies focused on both I and II subtypes indiscriminately and investigated changes in either brain volume or white matter (WM) integrity. We used combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) analysis to track changes in the grey matter (GM) and WM in the brains of patients affected by BPII, as compared to healthy controls.

Methods

Using VBM and DTI, we scanned 20 DSM-IV-TR BPII patients in their euthymic phase and 21 healthy, age- and gender-matched volunteers with no psychiatric history.

Results

VBM showed decreases in GM of BPII patients, compared to controls, which were diffuse in nature and most prominent in the right middle frontal gyrus and in the right superior temporal gurus. DTI showed significant and widespread FA reduction in BPII patients in all major WM tracts, including cortico-cortical association tracts.

Limitations

The small sample size limits the generalisability of our findings.

Conclusions

Reduced GM volumes and WM integrity changes in BPII patients are not prominent like those previously reported in bipolar disorder type-I and involve cortical structures and their related association tracts.     

应用DTI技术对人脑白质纤维束的初步研究

目的建立扩散张量纤维束成像对人脑白质纤维的显示方法,并应用中国数字化可视人体数据进行对照观察,验证扩散张量成像(DTI)方法的可靠性。方法选择5名健康志愿者进行DTI成像,采用DtiStudio软件进行分析处理,重建出部分各向异性(FA)图、容积比(VR)图、相对各向异性(RA)图、表面扩散系数(ADC)图以及二维彩色张量图。应用中国数字化可视人体数据集断面图像、FA图及彩色FA图进行对照观察,利用fibertracking纤维跟踪软件及3DMRI软件进行三维重建显示脑内主要白质纤维束,辨认脑内白质纤维束的位置、形态。结果应用DTI纤维束成像可以清晰准确地描绘脑白质内主要神经纤维束的解剖图谱,包括联络纤维如弓形纤维、钩束、扣带束、上纵束和下纵束,连合纤维如胼胝体、前连合和穹隆,投射纤维如锥体束、视放射、内侧丘系等。DTI纤维束成像结果与已知解剖知识、中国可视化人体断面图像具有很好的一致性。结论应用DTI纤维束成像可以清晰准确地描绘脑白质内主要神经纤维束的解剖图谱,其结果与中国可视化人体断面图像、已知解剖知识是一致的,应用DTI纤维束成像研究脑内纤维连通性是可靠的。     

Classification of diffusion tensor images for the early detection of Alzheimer's disease

Early detection of Alzheimer's disease (AD) is important since treatments are more efficacious when used at the beginning of the disease. Despite significant advances in diagnostic methods for AD, there is no single diagnostic method for AD with high accuracy. We developed a support vector machine (SVM) model that classifies mild cognitive impairment (MCI) and normal control subjects using probabilistic tractography and tract-based spatial statistics of diffusion tensor imaging (DTI) data. MCI is an intermediate state between normal aging and AD, so finding MCI is important for an early diagnosis of AD. The key features of DTI data we identified through extensive analysis include the fractional anisotropy (FA) values of selected voxels, their average FA value, and the volume of fiber pathways from a pre-defined seed region. In particular, the volume of the fiber pathways to thalamus is the most powerful single feature in classifying MCI and normal subjects regardless of the age of the subjects. The best performance achieved by the SVM model in a 10-fold cross validation and in independent testing was sensitivity of 100%, specificity of 100% and accuracy of 100%.     

磁共振扩散张量成像评价小儿脑病的初步应用

目的应用扩散张量成像（DTI）来进一步评价小儿脑疾病，探讨DTI的诊断价值。方法对15例患各种脑病的小儿进行MRI检查，其中男性10例，女性5例，年龄为生后3天至11岁（平均年龄4．8岁）。采用Philips Intera Achieva 3．0 Tesla的超导MRI仪，用回波平面成像（EPI）的DTI技术，b为800s／mm^2，15个方向。观察彩色分数各向异性（FA）图和三维彩色编码图。结果15例患儿中，发育畸形2例，分别是胼胝体发育不良、巨脑回；脑室旁白质软化（PVL）7例；缺氧缺血性脑病3例；脑软化2例；脑积水1例。在巨脑回病例，常规MRI见右侧脑回发育不良，呈巨脑回畸形，右侧脑室扩大，在DTI上见右侧病变区白质束明显较对侧少。胼胝体发育不良病例在DTI张量图见胼胝体菲薄。在PVL和缺氧缺血性脑病病例均可见白质纤维束在放射冠颜色混杂，方向性混乱。脑软化病例可见白质纤维束部分中断。在脑积水病例可见白质束受压推移。结论DTI能够显示白质束的走向、绕行、交叉及推挤、中断等异常，可能对今后评估小儿脑病的预后转归有帮助。     

Tracing superior longitudinal fasciculus connectivity in the human brain using high resolution diffusion tensor tractography

The major language pathways such as superior longitudinal fasciculus (SLF) pathways have been outlined by experimental and diffusion tensor imaging (DTI) studies. The SLF I and some of the superior parietal lobule connections of the SLF pathways have not been depicted by prior DTI studies due to the lack of imaging sensitivity and adequate spatial resolution. In the current study, the trajectory of the SLF fibers has been delineated on five healthy human subjects using diffusion tensor tractography on a 3.0-T scanner at high spatial resolution. We also demonstrate for the first time the trajectory and connectivity of the SLF fibers in relation to other language pathways as well as the superior parietal lobule connections of the language circuit using high spatial resolution DTI in the healthy adult human brain.     

Fractal characterization of brain lesions in CT images

Fractal Dimension (FD) is a parameter used widely for classification, analysis, and pattern recognition of images. In this work we explore the quantification of CT (computed tomography) lesions of the brain by using fractal theory. Five brain lesions, which are portions of CT images of diseased brains, are used for the study. These lesions exhibit self-similarity over a chosen range of scales, and are broadly characterized by their fractal dimensions.     

Longitudinal diffusion tensor imaging in a rat brain glioma model

In order to investigate the properties of water motion within and around brain tumors as a function of tumor growth, longitudinal diffusion tensor imaging (DTI) was carried out in a rat brain glioma (C6) model. As tumors grew in size, significant anisotropy of water diffusion was seen both within and around the tumor. The tissue water surrounding the tumor exhibited high planar anisotropy, as opposed to the linear anisotropy normally seen in white matter, indicating that cells were experiencing stress in a direction normal to the tumor border. When tumors were sufficiently large, significant anisotropy was also seen within the tumor because of longer-range organization of cancer cells within the tumor borders. These findings have important implications for diffusion-weighted MRI experiments examining tumor growth and response to therapy. Copyright (c) 2008 John Wiley & Sons, Ltd.     

基于弥散张量成像的脑结构网络参数研究

目的 利用复杂网络参数评估大脑特征,是探明大脑工作机制的新思路。方法 本研究基于弥散张量纤维束追踪和小世界网络理论构建脑结构网络。首先利用自动解剖标定（automated anatomical labeling,AAL）模板对大脑分区,并计算脑区间的纤维连接情况,对正常人的大脑结构进行网络建模。然后分析脑结构网络中节点度、簇系数和节点介数等网络参数,并通过引入小世界网络的介数和损伤性定义人脑结构网络的核心节点位置和特征。结果 脑结构网络具有小世界属性,且网络中存在少量的核心节点,具有较高的节点度和簇系数值。结论 利用弥散张量成像能够客观构建大脑结构网络,而核心节点的存在为揭示脑疾病的病理生理机制提供新的思路。     

Effect of accurately calculated b matrix on the evaluation of the diffusion tensor using echoplanar diffusion tensor imaging

Diffusion-weighted magnetic resonance imaging is a recognised tool in early detection of infarction of the human brain. If diffusion weighted-images with different values of b matrix are acquired during one individual investigation, it is possible to calculate diffusion coefficient maps that are elements of the diffusion tensor: they represent the apparent diffusion coefficient of protons of water molecules in each pixel in the corresponding sample. The relationship between signal intensity in the diffusion-weighted images, diffusion tensor and b matrix is derived from the Bloch equations. Our goal is to establish the error made in the calculation of the elements of the diffusion tensor when imaging gradients are ignored in the calculation of the b matrix elements. The numerical calculation of the b matrices show that a systematic error of up to 7% is introduced in the derived diffusion tensor elements when neglecting imaging gradients.     

Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson's correlation

The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging (DTI) data, Fractional Anisotropy (FA) and Mean Diffusivity (MD), and to train and test classifiers able to discriminate Alzheimer's Disease (AD) patients from controls on the basis of features extracted from the FA or MD volumes. In this study, support vector machine (SVM) classifier was trained and tested on FA and MD data. Feature selection is done computing the Pearson's correlation between FA or MD values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted MRI volumes and DTI data from healthy control subjects and AD patients. FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of DTI-derived features as an image-marker for AD and to the feasibility of building Computer Aided Diagnosis systems for AD based on them.     

Corticoreticular pathway in the human brain: diffusion tensor tractography study

LINGO-1 (leucine rich repeat and Ig domain containing Nogo receptor interacting protein-1) is a central nervous system transmembrane protein which simultaneously interacts with the Nogo-66 receptor and p75^NTR or TROY on neurons to form a receptor complex responsible for myelin-mediated neurite outgrowth inhibition. On oligodendroglial cells, LINGO-1 interacts with p75^NTR to constitutively inhibit multiple aspects of oligodendrocyte differentiation. Recently, LINGO-1 was identified as an in vivo interacting partner of the amyloid precursor protein (APP) and, correspondingly, cellular LINGO-1 expression was found to augment the release of the Abeta peptide, the potential causative agent of Alzheimer's disease. In addition, the recombinant LINGO-1 ectodomain has been shown to self-interact in solution and after crystallisation. Here, we have used deletional mutagenesis to identify the regions on LINGO-1 that are involved in homo- and heterotypic interactions. We have found that the N-terminal region containing the leucine-rich repeats along with the transmembrane and cytoplasmic domains of LINGO-1 are not required for self-interaction or interaction with APP.     

闭锁综合征患者运动诱发电位和皮质脊髓束弥散张量成像变化

目的：应用弥散张量成像（DTI）技术和运动诱发电位（MEP）评估闭锁综合征患者皮质脊髓束（CST）运动功能,观察DTI与MEP变化与运动功能的关系.方法：对2例脑干卒中患者分别于入院时、入院后2周进行DTI及MEP动态观察,测定患侧CST的部分各向异性值（FAV）,观察内囊纤维成像和MEP变化及其与运动功能的关系.结果：入院时DTI显示CST的FAV降低,通过脑干的下行白质纤维明显减少,入院2周后FAV和白质纤维均增加;入院时MEP的皮质电位消失,但是2周后随着FAV增加,皮质MEP重新出现,患者的运动功能也开始好转.结论：DTI的变化与MEP关系密切,它们与运动功能呈正相关.