A practical approach to in vivo high-resolution diffusion tensor imaging of rhesus monkeys on a 3-T human scanner

The nonhuman primate brain study provides important supplemental means for human brain exploration since the two species share close anatomical and functional similarities. MR diffusion tensor imaging (DTI) in human brain has revealed exquisite details of brain structures especially in the brain white matter. However, most previous monkey brain DTI results lack the spatial resolution in comparison to the conventional tracing and postmortem imaging methods, especially when it is acquired in commonly available human MRI scanners of field strength of 3 T or lower. To meet the increasing demands for nonhuman primate DTI studies, we proposed an in vivo high-resolution monkey DTI acquisition protocol that is practically feasible and combined it with an improved postprocessing procedure for a 3-T human scanner. The acquisition protocol, susceptibility distortion correction method with phase reversal acquisition, and postprocessing steps were proved to be effective in our study of rhesus monkeys. Results from diffusion tensor estimations and fiber tractography at 1 x 1 x 1 mm(3) resolution were found to be comparable to previous ex vivo DTI studies with much longer acquisition times. Effects of image resolution were evaluated and it was confirmed that the partial volume effect due to the larger voxel size in low-resolution data biased the diffusion tensor estimation and produced erroneous fiber tractography. Our results suggest that in vivo high-resolution monkey brain DTI can be achieved within practical time, which allows accurate diffusion tensor estimation and fiber tractography in monkey brains, so that the complex anatomical structures within many small but important anatomic structures can be delineated.     

基于高夹角分辨率扩散磁振造影神经径路追踪的人脑语言机率路径图谱

大脑皮质内部的联系神经束,对于大脑皮质之间的信息传递担任非常重要的角色.传统的语言模型理论提出人类的2个主要语言中枢分别位于大脑皮质的左侧额下回的布罗卡区域(Broca’s area,BA44andBA45)以及颞上回处的维尼基区域(Wernicke’s area,BA22),而联系这2个区域的纤维束,也就是弓状束(arcuate fasciculus).另外,近期研究也发现下顶叶(inferior parietal cortex,BA39and BA40)在语音处理历程具重要性.扩散磁振造影(Diffu-sionMRI)可以提供大脑白质精细的组织结构,配合神经径路追踪(tractography)便能撷取出复杂的神经纤维连结路径.该研究利用扩散磁振影像中的高夹角分辨率扩散磁振造影(high angular resolution diffusion imaging)与神经径路追踪技术,呈现与语言相关的大脑机率神经连结路径(probabilistic language pathway).     

应用限制球形卷积解析弓状束的结构特性与语言理解表现的相关性

通过限制球形卷积(constrained spherical deconvolution,CSD)和神经纤维追踪技术(tractography),以了解大脑左右半球的弓状束(arcuate fasciculus,AF)神经结构完整性与语言理解能力的相关性.该文使用磁共振成像(MRI)仪对14例大脑左半球罹患肿瘤的右利手患者进行了术前术后扫描(每次扫描均伴随一次波士顿失语症测试),并用分析软件DSI Studio计算术前术后左右半球弓状束的四项扩散指标,并进行了比较.结果发现左脑弓状束有两项指标在术前术后有非常显著的差异(p0.01),而右脑四项指标均无显著改变(p0.05).另外,该文还将扩散指标与患者相对应的语言测试所反映的语言理解能力进行了相关性分析,发现无论术前术后,左半球弓状束与语言理解能力相关系数r介于0.6~0.8之间,而右半球则仅介于0.3~0.4之间.上述结果表明,语言理解能力与左侧弓状束密切相关(p0.01),而与右侧弓状束相关性不大,此结果与以往研究者对大脑侧化的认知相符.     

A Monte Carlo simulation of image misalignment effects in diffusion tensor imaging

Diffusion tensor imaging (DTI) and tractography are noninvasive MRI methods, providing an insight on microscopic structural information of anisotropic tissues in vivo. The success of this technique stems on a watchful choice of imaging parameters and post-acquisition reconstruction. In the present work, we have focused on the problem of residual linear image misalignment in the DTI data and its effects on the parameters of the diffusion tensor and fiber tracking in human brain. We demonstrate substantial sensitivity of the reconstructed diffusion tensor and fiber tractography on increasing amplitude of artificially induced random image misalignment in the DTI. We show that already a submillimeter image misalignment in the DTI is an important source of error, which may potentially mask pathological presentations of the diseases and may partially explain variations in the results obtained from the DTI. Finally, we evaluated four implementations of image registrations and demonstrate their variable performance. This further supports the fact that a robust image registration must be performed to ensure reliable and reproducible diffusion tensor mapping and reconstruction of white matter (WM) fibers.     

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.     

Distinguishing and quantification of the human visual pathways using high-spatial-resolution diffusion tensor tractography

Quantification of the living human visual system using MRI methods has been challenging, but several applications demand a reliable and time-efficient data acquisition protocol. In this study, we demonstrate the utility of high-spatial-resolution diffusion tensor fiber tractography (DTT) in reconstructing and quantifying the human visual pathways. Five healthy males, age range 24–37 years, were studied after approval of the institutional review board (IRB) at The University of Texas Health Science Center at Houston. We acquired diffusion tensor imaging (DTI) data with 1-mm slice thickness on a 3.0-Tesla clinical MRI scanner and analyzed the data using DTT with the fiber assignment by continuous tractography (FACT) algorithm. By utilizing the high-spatial-resolution DTI protocol with FACT algorithm, we were able to reconstruct and quantify bilateral optic pathways including the optic chiasm, optic tract, optic radiations free of contamination from neighboring white matter tracts.     

Segmentation of the brain using direction-averaged signal of DWI images

Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution.     

Globally optimized fiber tracking and hierarchical clustering -- a unified framework

Structural connectivity between cortical regions of the human brain can be characterized noninvasively with diffusion tensor imaging (DTI)-based fiber tractography. In this paper, a novel fiber tractography technique, globally optimized fiber tracking and hierarchical fiber clustering, is presented. The proposed technique uses k-means clustering in conjunction with modified Hubert statistic to partition fiber pathways, which are evaluated with simultaneous consideration of consistency with underlying DTI data and smoothness of fiber courses in the sense of global optimality, into individual anatomically coherent fiber bundles. In each resulting bundle, fibers are sampled, perturbed and clustered iteratively to approach the optimal solution. The global optimality allows the proposed technique to resist local image artifacts and to possess inherent capabilities of handling complex fiber structures and tracking fibers between gray matter regions. The embedded hierarchical clustering allows multiple fiber bundles between a pair of seed regions to be naturally reconstructed and partitioned. The integration of globally optimized tracking and hierarchical clustering greatly benefits applications of DTI-based fiber tractography to clinical studies, particularly to studies of structure-function relations of the complex neural network of the human. Experiments with synthetic and in vivo human DTI data have demonstrated the effectiveness of the proposed technique in tracking complex fiber structures, thus proving its significant advantages over traditionally used streamline fiber tractography.     

A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue

The effective diffusion tensor of water, D, measured by diffusion tensor MRI (DT-MRI), is inherently a discrete, noisy, voxel-averaged sample of an underlying macroscopic effective diffusion tensor field, D(x). Within fibrous tissues this field is presumed to be continuous and smooth at a gross anatomical length scale. Here a new, general mathematical framework is proposed that uses measured DT-MRI data to produce a continuous approximation to D(x). One essential finding is that the continuous tensor field representation can be constructed by repeatedly performing one-dimensional B-spline transforms of the DT-MRI data. The fidelity and noise-immunity of this approximation are tested using a set of synthetically generated tensor fields to which background noise is added via Monte Carlo methods. Generally, these tensor field templates are reproduced faithfully except at boundaries where diffusion properties change discontinuously or where the tensor field is not microscopically homogeneous. Away from such regions, the tensor field approximation does not introduce bias in useful DT-MRI parameters, such as Trace(D(x)). It also facilitates the calculation of several new parameters, particularly differential quantities obtained from the tensor of spatial gradients of D(x). As an example, we show that they can identify tissue boundaries across which diffusion properties change rapidly using in vivo human brain data. One important application of this methodology is to improve the reliability and robustness of DT-MRI fiber tractography.     

A 16-channel loop array for in vivo macaque whole-brain imaging at 3 T

Non-human primates (NHPs) are vital models for neuroscience research. These animals have been widely used in behavioral, electrophysiological, molecular, and more recently, multimodal neuroimaging and neuro-engineering studies. Several RF coil arrays have been designed for functional, high-resolution brain magnetic resonance imaging (MRI), but few have been designed to accommodate multimodal devices. In the present study, a 16-channel array coil was constructed for brain imaging of macaques at 3 Tesla (3 T). To construct this coil, a close-fitting helmet-shaped form was designed to host 16 coil loops for whole-brain coverage. This assembly is mountable onto stereotaxic head frame bars, and the coil functions while the monkey is in the sphinx position with a clear line of vision of stimuli presented from outside of the MRI system. In addition, 4 openings were allocated in the coil housing, allowing multimodal devices to directly access visual cortical regions such as V1-V4 and MT. Coil performance was evaluated in an anesthetized macaque by quantifying and comparing signal-to-noise ratios (SNRs), noise correlations, and g-factor maps to a vendor-supplied human pediatric coil frequently used for NHP MRI. The result from in vivo experiments showed that the NHP coil was well-decoupled, had higher SNRs in cortical regions, and improved data acquisition acceleration capability compared with a vendor-supplied human pediatric coil that has been frequently used in macaque MRI studies. Furthermore, whole-brain anatomic imaging, diffusion tensor imaging and functional brain imaging have also been conducted: the details of brain anatomical structure, such as cerebellum and brainstem, can be clearly visualized in T2-SPACE images; b0 SNR calculated from b0 maps was higher than the human pediatric coil in all regions of interest (ROIs); the time-course SNR (tSNR) map calculated for GRE-EPI images demonstrates that the presented coil can be used for high-resolution functional imaging at 3 T.     

q-Space high b value diffusion MRI of hemi-crush in rat spinal cord: evidence for spontaneous regeneration

The development of the damage following hemi-crush trauma in rat spinal cord was studied ex vivo using high b value (bmax = 1 x 10(7) s cm(-2)) q-space diffusion weighted MRI (DWI) at five days, ten days and six weeks post-trauma. Rat spinal cord trauma, produced by hemi-crush of 15s and 60s duration, was studied. The water signal decay in these diffusion experiments was found to be non mono-exponential and was analyzed using the q-space approach. The q-space MRI parameters were compared with T1 and T2 MR images, behavioral tests and histopathological osmium staining. A very good anatomical correlation was found between the q-space MRI parameters and the osmium staining. Interestingly, we found that in the 15s hemi-crush model significant recovery was observed in both the q-space MR images and the osmium staining six weeks post-trauma. However, in the 60s hemi-crush trauma model very little recovery was observed. These results paralleled those obtained from behavioral tests demonstrating that partial spontaneous recovery seems to occur in the 15s hemi-crush spinal cord model, which should be taken in consideration when using it to evaluate new therapies.     

Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model.     

Microtesla MRI of the human brain combined with MEG

One of the challenges in functional brain imaging is integration of complementary imaging modalities, such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG, which uses highly sensitive superconducting quantum interference devices (SQUIDs) to directly measure magnetic fields of neuronal currents, cannot be combined with conventional high-field MRI in a single instrument. Indirect matching of MEG and MRI data leads to significant co-registration errors. A recently proposed imaging method--SQUID-based microtesla MRI--can be naturally combined with MEG in the same system to directly provide structural maps for MEG-localized sources. It enables easy and accurate integration of MEG and MRI/fMRI, because microtesla MR images can be precisely matched to structural images provided by high-field MRI and other techniques. Here we report the first images of the human brain by microtesla MRI, together with auditory MEG (functional) data, recorded using the same seven-channel SQUID system during the same imaging session. The images were acquired at 46 microT measurement field with pre-polarization at 30 mT. We also estimated transverse relaxation times for different tissues at microtesla fields. Our results demonstrate feasibility and potential of human brain imaging by microtesla MRI. They also show that two new types of imaging equipment--low-cost systems for anatomical MRI of the human brain at microtesla fields, and more advanced instruments for combined functional (MEG) and structural (microtesla MRI) brain imaging--are practical.     

Diffusion-weighted imaging in epilepsy

Diffusion-weighted imaging (DWI) is a relatively new magnetic resonance imaging (MRI) technique that can be used to probe the microenvironment of water. Contrast in DWI depends on properties different from traditional T₁ and T₂ constrast, and is derived from the translational motion of water molecules. Since it is reasonable to think that a change in the microenvironment of water might be reflected in a change in water diffusion characteristics, the quantitative assessment of the (apparent) diffusion coefficient ADCw may represent a unique means of assessing tissue status. DWI has already shown great utility in the study of cerebral ischemia in animal models and has proved useful in the early identification of cerebral ischemia in patients. More recent reports have indicated a potential for DWI in studying epilepsy. Here, we briefly review some of what is known about the measurement of ADCw in ischemia and compare these results with what has recently been reported for epilepsy. In this manner we hope to better understand the underlying mechanisms behind changes in water diffusion associated with specific pathologies.     

Reduction of nonlinear impairments in fiber transmission system using fiber and/or transmitter diversity

A multi-fiber architecture is proposed to mitigate the nonlinear impairments in fiber-optic systems. The power launched to each fiber is reduced in multi-fiber architecture as compared to the case of single fiber leading to reduction in nonlinear impairments. The optical pulses propagating in the fibers undergo different amount of phase shifts and timing delays. Optical and electrical equalization techniques to compensate for these channel effects are discussed. Our numerical simulation results show that for unrepeatered systems, the performance (Q factor) is improved by 6.2 dB using 8-fiber configuration as compared to single-fiber system. In addition, for multi-span system, the transmission reach at a bit error rate (BER) of 2.1 × 10^? 3 is quadrupled in 8-fiber configuration.     

Ictal imaging using functional magnetic resonance

Magnetic resonance imaging (MRI) can now provide maps of human brain function with high spatial and temporal resolution. This noninvasive technique can also map the coritical activation that occurs during focal seizures, as demonstrated here by the results obtained using a conventional 1.5 T clinical MRI system for the investigation of a 4-year-old boy suffering from frequent partial motor seizures of his right side. FLASH images (TE = 60 ms) were acquired every 10 s over a period of 25 min, and activation images derived by subtracting baseline images from images obtained during clinical seizures. Functional MRI revealed sequential activation associated with specific gyri within the left hemisphere with each of five consecutive clinical seizures, and also during a period that was not associated with a detectable clinical seizure. The activated regions included gyri that were structurally abnormal. These results demonstrate (a) that functional MRI can potentially provide new insights into the dynamic events that occur in the epileptic brain and their relationship to brain structure; and (b) that there is the possibility of obtaining similar information in the absence of clinical seizures, suggesting the potential for studies in patients with interictal electrical disturbances.     

Pore diameter mapping using double pulsed-field gradient MRI and its validation using a novel glass capillary array phantom

Double pulsed-field gradient (d-PFG) MRI can provide quantitative maps of microstructural quantities and features within porous media and tissues. We propose and describe a novel MRI phantom, consisting of wafers of highly ordered glass capillary arrays (GCA), and its use to validate and calibrate a d-PFG MRI method to measure and map the local pore diameter. Specifically, we employ d-PFG Spin-Echo Filtered MRI in conjunction with a recently introduced theoretical framework, to estimate a mean pore diameter in each voxel within the imaging volume. This simulation scheme accounts for all diffusion and imaging gradients within the diffusion weighted MRI (DWI) sequence, and admits the violation of the short gradient pulse approximation. These diameter maps agree well with pore sizes measured using both optical microscopy and single PFG diffusion diffraction NMR spectroscopy using the same phantom. Pixel-by-pixel analysis shows that the local pore diameter can be mapped precisely and accurately within a specimen using d-PFG MRI.     

Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

Multiparametric MRI is a remarkable imaging method for the assessment of patho-physiological processes. In particular, brain tumor characterization has taken advantage of the development of advanced techniques such as Diffusion- (DWI) and Perfusion- (PWI) Weighted Imaging, but a thorough analysis of meningiomas is still lacking despite the variety of computational methods proposed.We compute perfusion and diffusion parametric maps relying on a well-defined methodological workflow, investigating possible correlations between pure and diffusion-based perfusion parameters in a cohort of 26 patients before proton therapy. A preliminary investigation of meningioma staging biomarkers based on IntraVoxel Incoherent Motion and Dynamic Susceptibility Contrast is also reported. We observed significant differences between the gross target volume and the normal appearing white matter for every investigated parameter, confirming the higher vascularization of the neoplastic tissue. DWI and PWI parameters appeared to be weakly correlated and we found that diffusion parameters – the perfusion fraction in particular – could be promising biomarkers for tumor staging.     

Simultaneous magnetic resonance imaging of diffusion anisotropy and diffusion gradient

The theory of diffusion gradient-weighted MRI (DGWI) is presented in this paper. The Bloch-Torrey equation was modified to include the effect of intravoxel spatial-location variation of water diffusion (diffusion gradient) on MRI signal, in addition to the effect of intravoxel spatial-direction variation of water diffusion (diffusion anisotropy). An analytical solution for a diffusion-encoding spin-echo pulse sequence was derived. Unlike water diffusion which attenuates the image signal intensity, this newly derived solution relates the spatial gradient of the water diffusion with the phase of the image signal. This novel MRI technique directly measures both the water diffusion and its spatial gradient, and thus offers a noninvasive imaging tool to simultaneously investigate the intravoxel inhomogeneity and anisotropy of tissue structures. In addition, as demonstrated with our preliminary data, this new method may be utilized to delineate the interfaces of tissues with different diffusion. This method is an extension of the successful diffusion tensor MRI (DTI), but requires no additional data acquisition. In addition to the measured diffusion tensor, this new method provides measurements of the spatial derivatives of the three principal diffusivities of the tensor, thereby providing additional information for improving white matter fiber tractography.     

Comparison of the small-world topology between anatomical and functional connectivity in the human brain

We applied graph analysis to both anatomical and functional connectivity in the human brain. Anatomical connectivity was acquired from diffusion tensor imaging data by probabilistic fiber tracking, and functional connectivity was extracted from resting-state functional magnetic resonance imaging data by calculating correlation maps of time series. For the same subject, anatomical networks seemed to be disassortative, while functional networks were significantly assortative. Anatomical networks showed higher efficiency and smaller diameters than functional networks. It can be proposed that anatomical connectivity, as a major constraint of functional connectivity, has a relatively stable and efficient structure to support functional connectivity that is more changeable and flexible.