Analysis of partial volume effects in diffusion-tensor MRI

Reducing slab boundary artifacts in three-dimensional multislab diffusion MRI using nonlinear inversion for slab profile encoding (NPEN)

PURPOSE

To propose a method to reduce the slab boundary artifacts in three-dimensional multislab diffusion MRI.

METHODS

Bloch simulation is used to investigate the effects of multiple factors on slab boundary artifacts, including characterization of residual errors on diffusion quantification. A nonlinear inversion method is proposed to simultaneously estimate the slab profile and the underlying (corrected) image.

RESULTS

Correction results of numerical phantom and in vivo data demonstrate that the method can effectively remove slab boundary artifacts for diffusion data. Notably, the nonlinear inversion is also successful at short TR, a regimen where previously proposed methods (slab profile encoding and weighted average) retain residual artifacts in both diffusion-weighted images and diffusion metrics (mean diffusion coefficient and fractional anisotropy).

CONCLUSION

The nonlinear inversion for removing slab boundary artifacts provides improvements over existing methods, particularly at the short TRs required to maximize SNR efficiency. Magn Reson Med 76:1183-1195, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder found to have widespread alterations in the function and synchrony of brain regions. These differences may underlie alterations in microstructural organization, such as in white matter pathways. To investigate the diffusion of major white matter tracts, the current study examined multiple indices of white matter diffusion in 42 children and adults with ASD and 44 typically developing (TD) age- and IQ-matched peers using diffusion tensor imaging. Diffusivity measures were compared between groups for the following tracts: bilateral cingulum bundle, corpus callosum, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. Results indicate a significant reduction in fractional anisotropy (FA) for the left superior longitudinal fasciculus (LSLF) in ASD children and adults compared with TD peers. A significant increase in radial diffusivity for ASD participants was also found in the same cluster along the LSLF. In addition, a significant positive correlation emerged for all subjects between FA for the LSLF and age, with FA increasing with age. These findings point to a significant alteration in long-distance white matter connectivity in children and adults with ASD, potentially underscoring the relationship between alterations in white matter diffusion and the ASD phenotype. These results also suggest that the white matter alterations in autism may be subtle and related to the developmental trajectory.

Diffusion-Weighted Images Superresolution Using High-Order SVD

The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.

Longitudinal in vivo MRI in a Huntington's disease mouse model: Global atrophy in the absence of white matter microstructural damage

Huntington’s disease (HD) is a genetically-determined neurodegenerative disease. Characterising neuropathology in mouse models of HD is commonly restricted to cross-sectional ex vivo analyses, beset by tissue fixation issues. In vivo longitudinal magnetic resonance imaging (MRI) allows for disease progression to be probed non-invasively. In the HdhQ150 mouse model of HD, in vivo MRI was employed at two time points, before and after the onset of motor signs, to assess brain macrostructure and white matter microstructure. Ex vivo MRI, immunohistochemistry, transmission electron microscopy and behavioural testing were also conducted. Global brain atrophy was found in HdhQ150 mice at both time points, with no neuropathological progression across time and a selective sparing of the cerebellum. In contrast, no white matter abnormalities were detected from the MRI images or electron microscopy images alike. The relationship between motor function and MR-based structural measurements was different for the HdhQ150 and wild-type mice, although there was no relationship between motor deficits and histopathology. Widespread neuropathology prior to symptom onset is consistent with patient studies, whereas the absence of white matter abnormalities conflicts with patient data. The myriad reasons for this inconsistency require further attention to improve the translatability from mouse models of disease.

Free water elimination improves test-retest reproducibility of diffusion tensor imaging indices in the brain: A longitudinal multisite study of healthy elderly subjects

Free water elimination (FWE) in brain diffusion MRI has been shown to improve tissue specificity in human white matter characterization both in health and in disease. Relative to the classical diffusion tensor imaging (DTI) model, FWE is also expected to increase sensitivity to microstructural changes in longitudinal studies. However, it is not clear if these two models differ in their test-retest reproducibility. This study compares a bi-tensor model for FWE with DTI by extending a previous longitudinal-reproducibility 3T multisite study (10 sites, 7 different scanner models) of 50 healthy elderly participants (55-80 years old) scanned in two sessions at least 1 week apart. We computed the reproducibility of commonly used DTI metrics (FA: fractional anisotropy, MD: mean diffusivity, RD: radial diffusivity, and AXD: axial diffusivity), derived either using a DTI model or a FWE model. The DTI metrics were evaluated over 48 white-matter regions of the JHU-ICBM-DTI-81 white-matter labels atlas, and reproducibility errors were assessed. We found that relative to the DTI model, FWE significantly reduced reproducibility errors in most areas tested. In particular, for the FA and MD metrics, there was an average reduction of approximately 1% in the reproducibility error. The reproducibility scores did not significantly differ across sites. This study shows that FWE improves sensitivity and is thus promising for clinical applications, with the potential to identify more subtle changes. The increased reproducibility allows for smaller sample size or shorter trials in studies evaluating biomarkers of disease progression or treatment effects. Hum Brain Mapp 38:12-26, 2017. © 2016 Wiley Periodicals, Inc.

Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging Based on a Riemannian Manifold Approach

In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.

Diffusion-weighted imaging in oral squamous cell carcinoma using 3 Tesla MRI: is there a chance for preoperative discrimination between benign and malignant lymph nodes in daily clinical routine?

BACKGROUND

Preoperative staging of cervical lymph nodes is important to determine the extent of neck dissection in patients with oral squamous cell carcinoma (OSCC).

PURPOSE

To evaluate whether a preoperative discrimination of benign and malignant cervical lymph nodes with diffusion-weighted imaging (DWI) (3T) is feasible for clinical application.

MATERIAL AND METHODS

Forty-five patients with histological proven OSCC underwent preoperative 3T-MRI. DWI (b = 0, 500, and 1000 s/mm(2)) was added to the standard magnetic resonance imaging (MRI) protocol. Mean apparent diffusion coefficients (ADCmean) were measured for lymph nodes with 3 mm or more in short axis by two independent readers. Finally, these results were matched with histology.

RESULTS

Mean ADC was significantly higher for malignant than for benign nodes (1.143 ± 0.188 * 10(-3) mm(2)/s vs. 0.987 ± 0.215 * 10(-3) mm(2)/s). Using an ADC value of 0.994 * 10(-3) mm(2)/s as threshold results in a sensitivity of 80%, specificity of 65%, positive predictive value of 31%, and negative predictive value of 93%.

CONCLUSION

Due to a limited sensitivity and specificity DWI alone is not suitable to reliably discriminate benign from malignant cervical lymph nodes in daily clinical routine. Hence, the preoperative determination of the extent of neck dissection on the basis of ADC measurements is not meaningful.

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

Subcortical White Matter Changes with Normal Aging Detected by Multi-Shot High Resolution Diffusion Tensor Imaging

Subcortical white matter builds neural connections between cortical and subcortical regions and constitutes the basis of neural networks. It plays a very important role in normal brain function. Various studies have shown that white matter deteriorates with aging. However, due to the limited spatial resolution provided by traditional diffusion imaging techniques, microstructural information from subcortical white matter with normal aging has not been comprehensively assessed. This study aims to investigate the deterioration effect with aging in the subcortical white matter and provide a baseline standard for pathological disorder diagnosis. We apply our newly developed multi-shot high resolution diffusion tensor imaging, using self-feeding multiplexed sensitivity-encoding, to measure subcortical white matter changes in regions of interest of healthy persons with a wide age range. Results show significant fractional anisotropy decline and radial diffusivity increasing with age, especially in the anterior part of the brain. We also find that subcortical white matter has more prominent changes than white matter close to the central brain. The observed changes in the subcortical white matter may be indicative of a mild demyelination and a loss of myelinated axons, which may contribute to normal age-related functional decline.

White matter structure in the right planum temporale region correlates with visual motion detection thresholds in deaf people

The right planum temporale region is typically involved in higher-order auditory processing. After deafness, this area reorganizes to become sensitive to visual motion. This plasticity is thought to support compensatory enhancements to visual ability. In earlier work we showed that enhanced visual motion detection abilities in early-deaf people correlate with cortical thickness in a subregion of the right planum temporale. In the current study, we build on this earlier result by examining the relationship between enhanced visual motion detection ability and white matter structure in this area in the same sample. We used diffusion-weighted magnetic resonance imaging and extracted the measures of white matter structure from a region-of-interest just below the grey matter surface where cortical thickness correlates with visual motion detection ability. We also tested control regions-of-interest in the auditory and visual cortices where we did not expect to find a relationship between visual motion detection ability and white matter. We found that in the right planum temporale subregion, and in no other tested regions, fractional anisotropy, radial diffusivity, and mean diffusivity correlated with visual motion detection thresholds. We interpret this change as further evidence of a structural correlate of cross-modal reorganization after deafness.

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease

BACKGROUND

Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort.

METHOD

15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses.

RESULTS

Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics.

CONCLUSION

Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics.

ZOOM or Non-ZOOM? Assessing Spinal Cord Diffusion Tensor Imaging Protocols for Multi-Centre Studies

The purpose of this study was to develop and evaluate two spinal cord (SC) diffusion tensor imaging (DTI) protocols, implemented at multiple sites (using scanners from two different manufacturers), one available on any clinical scanner, and one using more advanced options currently available in the research setting, and to use an automated processing method for unbiased quantification. DTI parameters are sensitive to changes in the diseased SC. However, imaging the cord can be technically challenging due to various factors including its small size, patient-related and physiological motion, and field inhomogeneities. Rapid acquisition sequences such as Echo Planar Imaging (EPI) are desirable but may suffer from image distortions. We present a multi-centre comparison of two acquisition protocols implemented on scanners from two different vendors (Siemens and Philips), one using a reduced field-of-view (rFOV) EPI sequence, and one only using options available on standard clinical scanners such as outer volume suppression (OVS). Automatic analysis was performed with the Spinal Cord Toolbox for unbiased and reproducible quantification of DTI metrics in the white matter. Images acquired using the rFOV sequence appear less distorted than those acquired using OVS alone. SC DTI parameter values obtained using both sequences at all sites were consistent with previous measurements made at 3T. For the same scanner manufacturer, DTI parameter inter-site SDs were smaller for the rFOV sequence compared to the OVS sequence. The higher inter-site reproducibility (for the same manufacturer and acquisition details, i.e. ZOOM data acquired at the two Philips sites) of rFOV compared to the OVS sequence supports the idea that making research options such as rFOV more widely available would improve accuracy of measurements obtained in multi-centre clinical trials. Future multi-centre studies should also aim to match the rFOV technique and signal-to-noise ratios in all sequences from different manufacturers/sites in order to avoid any bias in measured DTI parameters and ensure similar sensitivity to pathological changes.

Characterizing longitudinal changes in rabbit brains infected with Angiostrongylus Cantonensis based on diffusion anisotropy

Angiostrongylus cantonensis has become a global source of infection in recent years, and the differential diagnosis and timely follow-up are crucial in the management of the infection. Magnetic resonance imaging (MRI) has been suggested as a non-invasive technique in characterizing and localizing lesions during the parasitic infections in the brain. Non-invasive diffusion tensor imaging (DTI) can be used to distinguish microscopic cerebral structures but cannot resolve the more complicated neural structure. Several methods have been proposed to overcome this limitation. One such method, generalized q-sampling imaging (GQI), can be applied to a variety of datasets, including the single shell, multi-shell or grid sampling schemes, which are believed to resolve complicated crossing fibers. This study aimed to characterize angiostrongyliasis in the rabbit brain over a 6-week period using anatomical and diffusion MRI, including DTI and GQI. Our anatomical T2WI and R2 mapping results showed that the ventricle size of the rabbit brain increased after A. cantonensis larvae infection, and the DTI and GQI indices both showed pathological changes in the corpus callosum, hippocampus and cortex over a 6-week infection period. These results were consistent with our histopathological findings. Our results demonstrated that the diagnosis of larvae infection using anatomical and diffusion MRI is possible and that follow-up characterization is informative in revealing the effects of angiostrongyliasis in various brain areas. These support the use of anatomical and diffusion MRI was helpful for diagnosis of eosinophilic meningitis caused by A. cantonensis infection. This non-invasive MRI platform could be used to improve the management of eosinophilic meningitis or eosinophilic meningoencephalitis in humans.

Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

REDUCING CSF PARTIAL VOLUME EFFECTS TO ENHANCE DIFFUSION TENSOR IMAGING METRICS OF BRAIN MICROSTRUCTURE

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate "normal" age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of "normal" brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed.

Sleep reverts changes in human gray and white matter caused by wake-dependent training

Learning leads to rapid microstructural changes in gray (GM) and white (WM) matter. Do these changes continue to accumulate if task training continues, and can they be reverted by sleep? We addressed these questions by combining structural and diffusion weighted MRI and high-density EEG in 16 subjects studied during the physiological sleep/wake cycle, after 12 h and 24 h of intense practice in two different tasks, and after post-training sleep. Compared to baseline wake, 12 h of training led to a decline in cortical mean diffusivity. The decrease became even more significant after 24 h of task practice combined with sleep deprivation. Prolonged practice also resulted in decreased ventricular volume and increased GM and WM subcortical volumes. All changes reverted after recovery sleep. Moreover, these structural alterations predicted cognitive performance at the individual level, suggesting that sleep's ability to counteract performance deficits is linked to its effects on the brain microstructure. The cellular mechanisms that account for the structural effects of sleep are unknown, but they may be linked to its role in promoting the production of cerebrospinal fluid and the decrease in synapse size and strength, as well as to its recently discovered ability to enhance the extracellular space and the clearance of brain metabolites.

A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging

Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.

Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study

Microstructural changes in human brain white matter of young to middle-aged adults were studied using advanced diffusion Magnetic Resonance Imaging (dMRI). Multiple shell diffusion-weighted data were acquired using the Hybrid Diffusion Imaging (HYDI). The HYDI method is extremely versatile and data were analyzed using Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI), and q-space imaging approaches. Twenty-four females and 23 males between 18 and 55years of age were included in this study. The impact of age and sex on diffusion metrics were tested using least squares linear regressions in 48 white matter regions of interest (ROIs) across the whole brain and adjusted for multiple comparisons across ROIs. In this study, white matter projections to either the hippocampus or the cerebral cortices were the brain regions most sensitive to aging. Specifically, in this young to middle-aged cohort, aging effects were associated with more dispersion of white matter fibers while the tissue restriction and intra-axonal volume fraction remained relatively stable. The fiber dispersion index of NODDI exhibited the most pronounced sensitivity to aging. In addition, changes of the DTI indices in this aging cohort were correlated mostly with the fiber dispersion index rather than the intracellular volume fraction of NODDI or the q-space measurements. While men and women did not differ in the aging rate, men tend to have higher intra-axonal volume fraction than women. This study demonstrates that advanced dMRI using a HYDI acquisition and compartmental modeling of NODDI can elucidate microstructural alterations that are sensitive to age and sex. Finally, this study provides insight into the relationships between DTI diffusion metrics and advanced diffusion metrics of NODDI model and q-space imaging.

The controversial existence of the human superior fronto-occipital fasciculus: Connectome-based tractographic study with microdissection validation

The superior fronto-occipital fasciculus (SFOF), a long association bundle that connects frontal and occipital lobes, is well-documented in monkeys but is controversial in human brain. Its assumed role is in visual processing and spatial awareness. To date, anatomical and neuroimaging studies on human and animal brains are not in agreement about the existence, course, and terminations of SFOF. To clarify the existence of the SFOF in human brains, we applied deterministic fiber tractography to a template of 488 healthy subjects and to 80 individual subjects from the Human Connectome Project (HCP) and validated the results with white matter microdissection of post-mortem human brains. The imaging results showed that previous reconstructions of the SFOF were generated by two false continuations, namely between superior thalamic peduncle (STP) and stria terminalis (ST), and ST and posterior thalamic peduncle. The anatomical microdissection confirmed this finding. No other fiber tracts in the previously described location of the SFOF were identified. Hence, our data suggest that the SFOF does not exist in the human brain.

Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo

In addition to motor functions, it has become clear that in humans the cerebellum plays a significant role in cognition too, through connections with associative areas in the cerebral cortex. Classical anatomy indicates that neo-cerebellar regions are connected with the contralateral cerebral cortex through the dentate nucleus, superior cerebellar peduncle, red nucleus and ventrolateral anterior nucleus of the thalamus. The anatomical existence of these connections has been demonstrated using virus retrograde transport techniques in monkeys and rats ex vivo. In this study, using advanced diffusion MRI tractography we show that it is possible to calculate streamlines to reconstruct the pathway connecting the cerebellar cortex with contralateral cerebral cortex in humans in vivo. Corresponding areas of the cerebellar and cerebral cortex encompassed similar proportion (about 80%) of the tract, suggesting that the majority of streamlines passing through the superior cerebellar peduncle connect the cerebellar hemispheres through the ventrolateral thalamus with contralateral associative areas. This result demonstrates that this kind of tractography is a useful tool to map connections between the cerebellum and the cerebral cortex and moreover could be used to support specific theories about the abnormal communication along these pathways in cognitive dysfunctions in pathologies ranging from dyslexia to autism.

Investigation of resolution effects using a specialized diffusion tensor phantom

PURPOSE

The clinical potential of the diffusion imaging-based analysis of fine brain structures such as fornix or cingulum is high due to the central role of these structures in psychiatric diseases. However, the quantification of diffusion parameters in fine structures is especially prone to partial volume effects (PVEs).

METHODS

In this study, a phantom for the investigation of PVEs and their influence on diffusion parameters in fine structures of different diameter is presented. The phantom is produced by winding wet polyester fibers onto a spindle. The resulting fiber strands have well defined square cross-sections of 1-25 mm(2) and provide a homogeneous and high fractional anisotropy (FA ≈ 0.9).

RESULTS

Several PVEs are demonstrated and analyzed. It is shown that inferred results such as the fiber geometry and diffusion parameters strongly depend on the relative position of the structure of interest to the voxel-grid. Several implications of PVEs on post-processing methods such as Tract-based Spatial Statistics and fiber tractography are demonstrated.

CONCLUSION

These results show that the handling of PVEs in common post-processing tasks can be problematic, and that the presented phantom provides a valuable tool for the improvement and evaluation of these effects.

White matter microstructure between the pre-SMA and the cingulum bundle is related to response conflict in healthy subjects

INTRODUCTION

Response conflict involves selectively attending to relevant information and suppressing distracting, irrelevant information. The medial frontal cortex (MFC) is considered to be involved in response conflict. However, it remains unclear which white matter connectivity is associated with response conflict. This study aimed to delineate the neural connectivity of response conflict in healthy subjects and investigate the association between white matter microstructure and performance of a response conflict task.

METHOD

Twenty-eight healthy subjects underwent functional magnetic resonance imaging (fMRI) during the Flanker task and diffusion MRI. We identified the presupplementary motor area (pre-SMA) using fMRI. Furthermore, we delineated the white matter connectivity between the pre-SMA and the cingulum bundle (CB), which is located in the MFC, using probabilistic tractography. We calculated the mean diffusivity (MD), index of white matter microstructure, of this tract and evaluate the association between MD and performance of the Flanker task.

RESULT

The mean MD of this tract was significantly and positively associated with performance of the Flanker task.

CONCLUSION

The present study suggests the white matter connectivity between the pre-SMA and the CB is related to the response conflict in healthy subjects and finer white matter microstructure is associated with smaller response conflict.

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.

High-definition fiber tractography for the evaluation of perilesional white matter tracts in high-grade glioma surgery

Conventional white matter (WM) imaging approaches, such as diffusion tensor imaging (DTI), have been used to preoperatively identify the location of affected WM tracts in patients with intracranial tumors in order to maximize the extent of resection and potentially reduce postoperative morbidity. DTI, however, has limitations that include its inability to resolve multiple crossing fibers and its susceptibility to partial volume effects. Therefore, recent focus has shifted to more advanced WM imaging techniques such as high-definition fiber tractography (HDFT). In this paper, we illustrate the application of HDFT, which in our preliminary experience has enabled accurate depiction of perilesional tracts in a 3-dimensional manner in multiple anatomical compartments including edematous zones around high-grade gliomas. This has facilitated accurate surgical planning. This is illustrated by using case examples of patients with glioblastoma multiforme. We also discuss future directions in the role of these techniques in surgery for gliomas.

Longitudinal evaluation of corticospinal tract in patients with resected brainstem cavernous malformations using high-definition fiber tractography and diffusion connectometry analysis: preliminary experience

OBJECT

Brainstem cavernous malformations (CMs) are challenging due to a higher symptomatic hemorrhage rate and potential morbidity associated with their resection. The authors aimed to preoperatively define the relationship of CMs to the perilesional corticospinal tracts (CSTs) by obtaining qualitative and quantitative data using high-definition fiber tractography. These data were examined postoperatively by using longitudinal scans and in relation to patients' symptomatology. The extent of involvement of the CST was further evaluated longitudinally using the automated "diffusion connectometry" analysis.

METHODS

Fiber tractography was performed with DSI Studio using a quantitative anisotropy (QA)-based generalized deterministic tracking algorithm. Qualitatively, CST was classified as being "disrupted" and/or "displaced." Quantitative analysis involved obtaining mean QA values for the CST and its perilesional and nonperilesional segments. The contralateral CST was used for comparison. Diffusion connectometry analysis included comparison of patients' data with a template from 90 normal subjects.

RESULTS

Three patients (mean age 22 years) with symptomatic pontomesencephalic hemorrhagic CMs and varying degrees of hemiparesis were identified. The mean follow-up period was 37.3 months. Qualitatively, CST was partially disrupted and displaced in all. Direction of the displacement was different in each case and progressively improved corresponding with the patient's neurological status. No patient experienced neurological decline related to the resection. The perilesional mean QA percentage decreases supported tract disruption and decreased further over the follow-up period (Case 1, 26%-49%; Case 2, 35%-66%; and Case 3, 63%-78%). Diffusion connectometry demonstrated rostrocaudal involvement of the CST consistent with the quantitative data.

CONCLUSIONS

Hemorrhagic brainstem CMs can disrupt and displace perilesional white matter tracts with the latter occurring in unpredictable directions. This requires the use of tractography to accurately define their orientation to optimize surgical entry point, minimize morbidity, and enhance neurological outcomes. Observed anisotropy decreases in the perilesional segments are consistent with neural injury following hemorrhagic insults. A model using these values in different CST segments can be used to longitudinally monitor its craniocaudal integrity. Diffusion connectometry is a complementary approach providing longitudinal information on the rostrocaudal involvement of the CST.

Fractional anisotropy shows differential reduction in frontal-subcortical fiber bundles-A longitudinal MRI study of 76 middle-aged and older adults

Motivated by the frontal- and white matter (WM) retrogenesis hypotheses and the assumptions that fronto-striatal circuits are especially vulnerable in normal aging, the goal of the present study was to identify fiber bundles connecting subcortical nuclei and frontal areas and obtain site-specific information about age related fractional anisotropy (FA) changes. Multimodal magnetic resonance image acquisitions [3D T1-weighted and diffusion weighted imaging (DWI)] were obtained from healthy older adults (N = 76, range 49-80 years at inclusion) at two time points, 3 years apart. A subset of the participants (N = 24) was included at a third time-point. In addition to the frontal-subcortical fibers, the anterior callosal fiber (ACF) and the corticospinal tract (CST) was investigated by its mean FA together with tract parameterization analysis. Our results demonstrated fronto-striatal structural connectivity decline (reduced FA) in normal aging with substantial inter-individual differences. The tract parameterization analysis showed that the along tract FA profiles were characterized by piece-wise differential changes along their extension rather than being uniformly affected. To the best of our knowledge, this is the first longitudinal study detecting age-related changes in frontal-subcortical WM connections in normal aging.

Diffusion tensor imaging of the human cerebellar pathways and their interplay with cerebral macrostructure

Cerebellar white matter (WM) connections to the central nervous system are classified functionally into the Spinocerebellar (SC), vestibulocerebellar (VC), and cerebrocerebellar subdivisions. The SC pathways project from spinal cord to cerebellum, whereas the VC pathways project from vestibular organs of the inner ear. Cerebrocerebellar connections are composed of feed forward and feedback connections between cerebrum and cerebellum including the cortico-ponto-cerebellar (CPC) pathways being of cortical origin and the dentate-rubro-thalamo-cortical (DRTC) pathway being of cerebellar origin. In this study we systematically quantified the whole cerebellar system connections using diffusion tensor magnetic resonance imaging (DT-MRI). Ten right-handed healthy subjects (7 males and 3 females, age range 20–51 years) were studied. DT-MRI data were acquired with a voxel size = 2 mm × 2 mm × 2 mm at a 3.0 Tesla clinical MRI scanner. The DT-MRI data were prepared and analyzed using anatomically-guided deterministic tractography methods to reconstruct the SC, DRTC, fronto-ponto-cerebellar (FPC), parieto-ponto-cerebellar (PPC), temporo-ponto-cerebellar (TPC) and occipito-ponto-cerebellar (OPC). The DTI-attributes or the cerebellar tracts along with their cortical representation (Brodmann areas) were presented in standard Montréal Neurological Institute space. All cerebellar tract volumes were quantified and correlated with volumes of cerebral cortical, subcortical gray matter (GM), cerebral WM and cerebellar GM, and cerebellar WM. On our healthy cohort, the ratio of total cerebellar GM-to-WM was ~3.29 ± 0.24, whereas the ratio of cerebral GM-to-WM was approximately 1.10 ± 0.11. The sum of all cerebellar tract volumes is ~25.8 ± 7.3 mL, or a percentage of 1.6 ± 0.45 of the total intracranial volume (ICV).

Quantitative assessment of diffusional kurtosis anisotropy

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments.

Compromised white matter integrity in obesity

Obesity is associated with both structural and functional changes of the central nervous system. While gray matter alterations in obesity point to a consistent reduction with increasing body mass index (BMI), volumetric changes in white matter are more complex and less conclusive. Hence, more recently, diffusion tensor imaging (DTI) has been employed as a highly sensitive tool to investigate microstructural changes in white matter structure. Parameters of diffusivity and anisotropy are used to evaluate white matter and fibre integrity as well as axonal and myelin degeneration. Fractional anisotropy (FA) is the most commonly used parameter as it is the best estimate of fibre integrity. The focus of this review was on the relationship between obesity and brain alterations assessed by DTI. Altogether, these studies have shown a loss of white matter integrity with obesity-related factors, especially in tracts within the limbic system and those connecting the temporal and frontal lobe. More specifically, multiple studies found an inverse association between BMI and FA in the corpus callosum, fornix, cingulum and corona radiata in elderly and young adults as well as children. Furthermore, significant interactions were observed between BMI and age, pointing to accelerated ageing of white matter structure in obese.

New insights into the developing rabbit brain using diffusion tensor tractography and generalized q-sampling MRI

The use of modern neuroimaging methods to characterize the complex anatomy of brain development at different stages reveals an enormous wealth of information in understanding this highly ordered process and provides clues to detect neurological and neurobehavioral disorders that have their origin in early structural and functional cerebral maturation. Non-invasive diffusion tensor magnetic resonance imaging (DTI) is able to distinguish cerebral microscopic structures, especially in the white matter regions. However, DTI is unable to resolve the complicated neural structure, i.e., the fiber crossing that is frequently observed during the maturation process. To overcome this limitation, several methods have been proposed. One such method, generalized q-sampling imaging (GQI), can be applied to a variety of datasets, including the single shell, multi-shell or grid sampling schemes that are believed to be able to resolve the complicated crossing fibers. Rabbits have been widely used for neurodevelopment research because they exhibit human-like timing of perinatal brain white matter maturation. Here, we present a longitudinal study using both DTI and GQI to demonstrate the changes in cerebral maturation of in vivo developing rabbit brains over a period of 40 weeks. Fractional anisotropy (FA) of DTI and generalized fractional anisotropy (GFA) of GQI indices demonstrated that the white matter anisotropy increased with age, with GFA exhibiting an increase in the hippocampus as well. Normalized quantitative anisotropy (NQA) of GQI also revealed an increase in the hippocampus, allowing us to observe the changes in gray matter as well. Regional and whole brain DTI tractography also demonstrated refinement in fiber pathway architecture with maturation. We concluded that DTI and GQI results were able to characterize the white matter anisotropy changes, whereas GQI provided further information about the gray matter hippocampus area. This developing rabbit brain DTI and GQI database could also be used for educational purposes and neuroscience investigations.

Regional White Matter Variation Associated with Domain-specific Metacognitive Accuracy

The neural mechanisms that mediate metacognitive ability (the capacity to accurately reflect on one's own cognition and experience) remain poorly understood. An important question is whether metacognitive capacity is a domain-general skill supported by a core neuroanatomical substrate or whether regionally specific neural structures underlie accurate reflection in different cognitive domains. Providing preliminary support for the latter possibility, recent findings have shown that individual differences in metacognitive ability in the domains of memory and perception are related to variation in distinct gray matter volume and resting-state functional connectivity. The current investigation sought to build on these findings by evaluating how metacognitive ability in these domains is related to variation in white matter microstructure. We quantified metacognitive ability across memory and perception domains and used diffusion spectrum imaging to examine the relation between high-resolution measurements of white matter microstructure and individual differences in metacognitive accuracy in each domain. We found that metacognitive accuracy for perceptual decisions and memory were uncorrelated across individuals and that metacognitive accuracy in each domain was related to variation in white matter microstructure in distinct brain areas. Metacognitive accuracy for perceptual decisions was associated with increased diffusion anisotropy in white matter underlying the ACC, whereas metacognitive accuracy for memory retrieval was associated with increased diffusion anisotropy in the white matter extending into the inferior parietal lobule. Together, these results extend previous findings linking metacognitive ability in the domains of perception and memory to variation in distinct brain structures and connections.

Application of high-definition fiber tractography in the management of supratentorial cavernous malformations: a combined qualitative and quantitative approach

BACKGROUND

High-definition fiber tractography (HDFT), an advanced white matter (WM) imaging technique, was evaluated in the management of supratentorial cavernous malformations.

OBJECTIVE

To investigate the relationship of cavernous malformations to the relevant perilesional WM tracts with HDFT and to characterize associated changes first qualitatively and then quantitatively with our novel imaging measure, quantitative anisotropy (QA).

METHODS

Imaging analysis was carried out by researchers blinded to the clinical details. Contralateral WM tracts were used for comparison. Mean QA values were obtained for whole WM tracts. Qualitatively affected superior longitudinal fasciculus/arcuate fibers and corticospinal tracts were further analyzed with the use of mean QA values for the perilesional segments.

RESULTS

Of 10 patients, HDFT assisted with the decision-making process and the offer of surgical resection in 2 patients, lesion approach and removal in 7 patients, and conservative management in 1 patient. Of 17 analyzed WM tracts, HDFT demonstrated partial disruption in 2 tracts, complete disruption in 2 tracts, a combination of displacement and partial disruption in 1 tract, displacement only in 7 tracts, and no change in 5 tracts. Qualitative changes correlated with clinical symptoms. Mean QA values for the whole WM tracts were similar, with the exception of 1 case demonstrating complete disruption of 2 WM tracts. QA-based perilesional segment analysis was consistent with qualitative data in 5 assessed WM tracts.

CONCLUSION

HDFT illustrated the precise spatial relationship of cavernous malformations to multiple WM tracts in a 3-dimensional fashion, optimizing surgical planning, and demonstrated associated disruption and/or displacement, with both occurring perilesionally. These changes were supported by our quantitative marker, which needs further validation.

Microstructural white matter tissue characteristics are modulated by homocysteine: a diffusion tensor imaging study

Homocysteine level can lead to adverse effects on the brain white matter through endothelial dysfunction, microstructural inflammation, and neurotoxin effects. Despite previously observed associations between elevated homocysteine and macroscopic structural brain changes, it is still unknown whether microstructural associations of homocysteine on brain tissue properties can be observed in healthy subjects with routine MRI. To this end, we investigated potential relationships between homocysteine levels and microstructural measures computed with diffusion tensor imaging (DTI) in a cohort of 338 healthy participants. Significant positive correlations were observed between homocysteine levels and diffusivity measures in the bilateral temporal WM, the brainstem, and the bilateral cerebellar peduncle. This is the first study demonstrating that DTI is sufficiently sensitive to relate microstructural WM properties to homocysteine levels in healthy subjects.

Understanding fiber mixture by simulation in 3D Polarized Light Imaging

3D Polarized Light Imaging (3D-PLI) is a neuroimaging technique that has opened up new avenues to study the complex architecture of nerve fibers in postmortem brains. The spatial orientations of the fibers are derived from birefringence measurements of unstained histological brain sections that are interpreted by a voxel-based analysis. This, however, implies that a single fiber orientation vector is obtained for each voxel and reflects the net effect of all comprised fibers. The mixture of various fiber orientations within an individual voxel is a priori not accessible by a standard 3D-PLI measurement. In order to better understand the effects of fiber mixture on the measured 3D-PLI signal and to improve the interpretation of real data, we have developed a simulation method referred to as SimPLI. By means of SimPLI, it is possible to reproduce the entire 3D-PLI analysis starting from synthetic fiber models in user-defined arrangements and ending with measurement-like tissue images. For the simulation, each synthetic fiber is considered as an optical retarder, i.e., multiple fibers within one voxel are described by multiple retarder elements. The investigation of different synthetic crossing fiber arrangements generated with SimPLI demonstrated that the derived fiber orientations are strongly influenced by the relative mixture of crossing fibers. In case of perpendicularly crossing fibers, for example, the derived fiber direction corresponds to the predominant fiber direction. The derived fiber inclination turned out to be not only influenced by myelin density but also systematically overestimated due to signal attenuation. Similar observations were made for synthetic models of optic chiasms of a human and a hooded seal which were opposed to experimental 3D-PLI data sets obtained from the chiasms of both species. Our study showed that SimPLI is a powerful method able to test hypotheses on the underlying fiber structure of brain tissue and, therefore, to improve the reliability of the extraction of nerve fiber orientations with 3D-PLI.

Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, tractography and the ability to recover complex white-matter pathways, differences between results due to choice of analysis method, and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work, we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment "ball and stick" model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm(2)) common to clinical studies. We found that the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for complex three-fiber crossing regions. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project "Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations" at http://www.nitrc.org/projects/sim_dwi_brain.

An investigation of diffusion imaging techniques in the evaluation of spinocerebellar ataxia and multisystem atrophy

Multisystem system atrophy and spinocerebellar ataxia are rare neurodegenerative ataxias that can be difficult to diagnose, with important prognostic and treatment implications. The purpose of this study is to evaluate various methods of diffusion imaging and tractography in their effectiveness at differentiating these diseases from control subjects. Our secondary aim is determining whether diffusion abnormalities correspond with clinical disease severity. Diffusion imaging and tractography were performed on five patients and seven age-matched controls. Fractional anisotropy, generalized fractional anisotropy, and apparent diffusion coefficient values and corticospinal tract volumes were measured within various diffusion and probabilistic tractography models, including standard diffusion tensor and Q-ball tractography. Standard diffusion based fractional anisotropy and apparent diffusion coefficient values were significantly altered in patients versus controls in the middle cerebellar peduncles and central pons. Tractography based fractional anisotropy and generalized fractional anisotropy values were significantly lower in patients versus controls when corticospinal tracts were drawn in a craniocaudal direction (bilaterally using Q-ball imaging, only on the right using diffusion tensor imaging). The right corticospinal tract volume was significantly smaller in patients versus controls when created using Q-ball imaging in a caudocranial direction. There was no correlation between diffusion alteration and clinical symptomatology. In conclusion, various diffusion-based techniques can be effective in differentiating ataxic patients from control subjects, although the selection of diffusion algorithm and tract growth technique and direction is non-trivial.