Analysis of partial volume effects in diffusion-tensor MRI

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r=0.70,p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data

PURPOSE

Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated.

METHODS

This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters.

RESULTS

The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort.

CONCLUSION

The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

Diffusion tensor imaging of the visual pathway in dogs with primary angle-closure glaucoma

OBJECTIVE

To describe measurements of in vivo structures of the visual pathway beyond the retina and optic nerve head associated with canine primary angle-closure glaucoma (PACG).

METHODS

A prospective pilot study was conducted using magnetic resonance diffusion tensor imaging (DTI) to obtain quantitative measures of the optic nerve, chiasm, tract, and lateral geniculate nucleus (LGN) in dogs with and without PACG. 3-Tesla DTI was performed on six affected dogs and five breed, age- and sex-matched controls. DTI indices of the optic nerve, optic chiasm, optic tracts, and LGN were compared between normal, unilateral PACG, and bilateral PACG groups. Intra-class correlation coefficient (ICC) was calculated to assess intra-observer reliability.

RESULTS

Quantitative measurements of the optic nerve, optic tract, optic chiasm, and LGN were obtained in all dogs. There was a trend for reduced fractional anisotropy (FA) associated with disease for all structures assessed. Compared to the same structure in normal dogs, FA, and radial diffusivity (RD) of the optic nerve was consistently higher in the unaffected eye in dogs with unilateral PACG. Intra-observer reliability was excellent for measurements of the optic nerve (ICC: 0.92), good for measurements of the optic tract (ICC: 0.89) and acceptable for measures of the optic chiasm (ICC: 0.71) and lateral geniculate nuclei (ICC: 0.76).

CONCLUSION

Diffusivity and anisotropy measures provide a quantifiable means to evaluate the visual pathway in dogs. DTI has potential to provide in vivo measures of axonal and myelin injury and transsynaptic degeneration in canine PACG.

The effects of internal jugular vein compression for modulating and preserving white matter following a season of American tackle football: A prospective longitudinal evaluation of differential head impact exposure

The purpose of this clinical trial was to examine whether internal jugular vein compression (JVC)-using an externally worn neck collar-modulated the relationships between differential head impact exposure levels and pre- to postseason changes in diffusion tensor imaging (DTI)-derived diffusivity and anisotropy metrics of white matter following a season of American tackle football. Male high-school athletes (n = 284) were prospectively assigned to a non-collar group or a collar group. Magnetic resonance imaging data were collected from participants pre- and postseason and head impact exposure was monitored by accelerometers during every practice and game throughout the competitive season. Athletes' accumulated head impact exposure was systematically thresholded based on the frequency of impacts of progressively higher magnitudes (10 g intervals between 20 to 150 g) and modeled with pre- to postseason changes in DTI measures of white matter as a function of JVC neck collar wear. The findings revealed that the JVC neck collar modulated the relationships between greater high-magnitude head impact exposure (110 to 140 g) and longitudinal changes to white matter, with each group showing associations that varied in directionality. Results also revealed that the JVC neck collar group partially preserved longitudinal changes in DTI metrics. Collectively, these data indicate that a JVC neck collar can provide a mechanistic response to the diffusion and anisotropic properties of brain white matter following the highly diverse exposure to repetitive head impacts in American tackle football. Clinicaltrials.gov: NCT# 04068883.

White Matter Integrity Is Associated With the Amount of Physical Activity in Older Adults With Super-aging

Previous studies have introduced the concept of “SuperAgers,” defined as older adults with youthful memory performance associated with the increased cortical thickness of the anterior cingulate cortex. Given that age-related structural brain changes are observed earlier in the white matter (WM) than in the cortical areas, we investigated whether WM integrity is different between the SuperAgers (SA) and typical agers (TA) and whether it is associated with superior memory performance as well as a healthy lifestyle. A total of 35 SA and 55 TA were recruited for this study. Further, 3.0-T magnetic resonance imaging (MRI), neuropsychological tests, and lifestyle factors related to cognitive function, such as physical activity and duration of sleep, were evaluated in all participants. SA was defined as individuals demonstrating the youthful performance of verbal and visual memory, as measured by the Seoul Verbal Learning Test (SVLT) and the Rey-Osterrieth Complex Figure Test (RCFT), respectively. Tract-based spatial statistics (TBSS) analysis was used to compare the diffusion values such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) between the SA and TA. SA exhibited better performance in memory, attention, visuospatial, and frontal executive functions than the TA did. SA also exhibited greater amounts of physical activity than the TA did. As compared to TA, SA demonstrated higher FA with lower MD, RD, and AD in the corpus callosum and higher FA and lower RD in the right superior longitudinal fasciculus (SLF), which is significantly associated with memory function. Interestingly, FA values of the body of corpus callosum were correlated with the amount of physical activity. Our findings suggest that WM integrity of the corpus callosum is associated with superior memory function and a higher level of physical activities in SA compared to TA.

Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril

Diffusion-weighted magnetic resonance imaging (dMRI) is a popular tool for noninvasively assessing properties of white matter in the brain. Among other uses, dMRI data can be used to produce estimates of anatomical connectivity on the basis of tractography. However, direct comparisons of anatomical connectivity as estimated through invasive neural tract-tracing experiments and dMRI-derived connectivity have shown only a moderate relationship in nonhuman primate (particularly macaque) studies. Tractography is plagued by known problems associated with resolution, crossing fibers, and curving fibers, among others. These problems lead to deficits in both sensitivity and specificity, which trade off with each other in multiple datasets. Although not yet examined quantitatively, there is reason to believe that some large white matter bundles, those with more topographic organization, may produce more accurate results than others. Moving forward, sophisticated analytical approaches and anatomical constraints may improve tractography accuracy. However, broadly speaking, dMRI-derived estimates of brain connectivity should be approached with caution.

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Individual variations of the human corticospinal tract and its hand-related motor fibers using diffusion MRI tractography

The corticospinal tract (CST) is one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we propose a novel method, using manually drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heat maps from 37 subjects for both the aforementioned tracts, which could be utilized as a reference for future studies with clinical focus to explore neuropathology in both trauma and disease states.

Dentate Gyrus Volume Mediates the Effect of Fornix Microstructure on Memory Formation in Older Adults

Age-related deterioration in white and gray matter is linked to cognitive deficits. Reduced microstructure of the fornix, the major efferent pathway of the hippocampus, and volume of the dentate gyrus (DG), may cause age-associated memory decline. However, the linkage between these anatomical determinants and memory retrieval in healthy aging are poorly understood. In 30 older adults, we acquired diffusion tensor and T1-weighted images for individual deterministic tractography and volume estimation. A memory task, administered outside of the scanner to assess retrieval of learned associations, required discrimination of previously acquired picture-word pairs. The results showed that fornix fractional anisotropy (FA) and left DG volumes were related to successful retrieval. These brain-behavior associations were observed for correct rejections, but not hits, indicating specificity of memory network functioning for detecting false associations. Mediation analyses showed that left DG volume mediated the effect of fornix FA on memory (48%), but not vice versa. These findings suggest that reduced microstructure induces volume loss and thus negatively affects retrieval of learned associations, complementing evidence of a pivotal role of the fornix in healthy aging. Our study offers a neurobehavioral model to explain variability in memory retrieval in older adults, an important prerequisite for the development of interventions to counteract cognitive decline.

Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography

Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.

Dedicated diffusion phantoms for the investigation of free water elimination and mapping: insights into the influence of T2 relaxation properties

Conventional diffusion-weighted (DW) MRI suffers from free water contamination due to the finite voxel size. The most common case of free water contamination occurs with cerebrospinal fluid (CSF) in voxels located at the CSF-tissue interface, such as at the ventricles in the human brain. Another case refers to intra-tissue free water as in vasogenic oedema. In order to avoid the bias in diffusion metrics, several multi-compartment methods have been introduced, which explicitly model the presence of a free water compartment. However, fitting multi-compartment models in DW MRI represents a well known ill conditioned problem. Although during the last decade great effort has been devoted to mitigating this estimation problem, the research field remains active. The aim of this work is to introduce the design, characterise the NMR properties and demonstrate the use of two dedicated anisotropic diffusion fibre phantoms, useful for the study of free water elimination (FWE) and mapping models. In particular, we investigate the recently proposed FWE diffusion tensor imaging approach, which takes explicit account of differences in the transverse relaxation times between the free water and tissue compartments.

Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression

Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.

The role of diffusion tractography in refining glial tumor resection

Primary brain tumors are notoriously hard to resect surgically. Due to their infiltrative nature, finding the optimal resection boundary without damaging healthy tissue can be challenging. One potential tool to help make this decision is diffusion-weighted magnetic resonance imaging (dMRI) tractography. dMRI exploits the diffusion of water molecule along axons to generate a 3D modelization of the white matter bundles in the brain. This feature is particularly useful to visualize how a tumor affects its surrounding white matter and plan a surgical path. This paper reviews the different ways in which dMRI can be used to improve brain tumor resection, its benefits and also its limitations. We expose surgical tools that can be paired with dMRI to improve its impact on surgical outcome, such as loading the 3D tractography in the neuronavigation system and direct electrical stimulation to validate the position of the white matter bundles of interest. We also review articles validating dMRI findings using other anatomical investigation techniques, such as postmortem dissections, manganese-enhanced MRI, electrophysiological stimulations, and phantom studies with known ground truth. We will be discussing the areas of the brain where dMRI performs well and where the future challenges are. We will conclude this review with suggestions and take home messages for neurosurgeons, tractographers, and vendors for advancing the field and on how to benefit from tractography's use in clinical practice.

Diffusion maps of Bacillus subtilis biofilms via magnetic resonance imaging highlight a complex network of channels

Bacillus subtilis microorganism when cultivated under chemically-defined conditions developed a biofilm with an unusual pattern of wrinkles on the surface. Some questions were raised about whether there was a special function of these wrinkles for the biofilm itself, since they resembled microchannels that could be involved in the transport of nutrients within the biofilm. Since the diffusion is the main mechanism for nutrient transport to biofilm from the medium, the role of these wrinkled structures in the whole diffusion within the biofilm was investigated using diffusion-weighted magnetic resonance imaging (DW-MRI). Data from these diffusion images was used to generate 2D diffusion maps which highlighted the striking channel features of the biofilm surface. The diffusion maps revealed a network of interconnected channels, with self-diffusion coefficients higher in the microchannels than in other regions of the biofilms. Polar plots made from 2D diffusion maps obtained from the plane of the biofilm show an anisotropy of the diffusion inside the microchannels, with the diffusion higher when along the principal direction of the microchannels. These results agree with the model, that the buckling of the biofilm surface from the B. subtilis creates microchannels that can enhance diffusion throughout the biofilm.

Fast and accurate initialization of the free-water imaging model parameters from multi-shell diffusion MRI

Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free-Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free-water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free-water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free-water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single-shell diffusion MRI data. However, by using data from multi-shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi-shell data requires high computational cost, with a non-linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non-linear model fitting.

Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI

Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.

The emergence of dyslexia in the developing brain

Developmental dyslexia, a severe deficit in literacy learning, is a neurodevelopmental learning disorder. Yet, it is not clear whether existing neurobiological accounts of dyslexia capture potential predispositions of the deficit or consequences of reduced reading experience. Here, we longitudinally followed 32 children from preliterate to school age using functional and structural magnetic resonance imaging techniques. Based on standardised and age-normed reading and spelling tests administered at school age, children were classified as 16 dyslexic participants and 16 controls. This longitudinal design allowed us to disentangle possible neurobiological predispositions for developing dyslexia from effects of individual differences in literacy experience. In our sample, the disorder can be predicted already before literacy learning from auditory cortex gyrification and aberrant downstream connectivity within the speech processing system. These results provide evidence for the notion that dyslexia may originate from an atypical maturation of the speech network that precedes literacy instruction.

Orientation Prior and Consistent Model Selection Increase Sensitivity of Tract-Based Spatial Statistics in Crossing-Fiber Regions

The goal of this paper is to increase the statistical power of crossing-fiber statistics in voxelwise analyses of diffusion-weighted magnetic resonance imaging (DW-MRI) data. In the proposed framework, a fiber orientation atlas and a model complexity atlas were used to fit the ball-and-sticks model to diffusion-weighted images of subjects in a prospective population-based cohort study. Reproducibility and sensitivity of the partial volume fractions in the ball-and-sticks model were analyzed using TBSS (tract-based spatial statistics) and compared to a reference framework. The reproducibility was investigated on two scans of 30 subjects acquired with an interval of approximately three weeks by studying the intraclass correlation coefficient (ICC). The sensitivity to true biological effects was evaluated by studying the regression with age on 500 subjects from 65 to 90 years old. Compared to the reference framework, the ICC improved significantly when using the proposed framework. Higher t-statistics indicated that regression coefficients with age could be determined more precisely with the proposed framework and more voxels correlated significantly with age. The application of a fiber orientation atlas and a model complexity atlas can significantly improve the reproducibility and sensitivity of crossing-fiber statistics in TBSS.

Evaluation of white matter microstructure in patients with Parkinson's disease using microscopic fractional anisotropy

PURPOSE

Micro fractional anisotropy (μFA) is more accurate than conventional fractional anisotropy (FA) for assessing microscopic tissue properties and can overcome limitations related to crossing white matter fibres. We compared μFA and FA for evaluating white matter changes in patients with Parkinson's disease (PD).

METHODS

We compared FA and μFA measures between 25 patients with PD and 25 age- and gender-matched healthy controls using tract-based spatial statistics (TBSS) analysis. We also examined potential correlations between changes, revealed by conventional FA or μFA, and disease duration or Unified Parkinson's Disease Rating Scale (UPDRS)-III scores.

RESULTS

Compared with healthy controls, patients with PD had significantly reduced μFA values, mainly in the anterior corona radiata (ACR). In the PD group, μFA values (primarily those from the ACR) were significantly negatively correlated with UPDRS-III motor scores. No significant changes or correlations with disease duration or UPDRS-III scores with tissue properties were detected using conventional FA.

CONCLUSION

μFA can evaluate microstructural changes that occur during white matter degeneration in patients with PD and may overcome a key limitation of FA.

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Compartmental diffusion MRI models that account for intravoxel incoherent motion (IVIM) of blood perfusion allow for estimation of the fractional volume of the microvascular compartment. Conventional IVIM models are known to be biased by not accounting for partial volume effects caused by free water and cerebrospinal fluid (CSF), or for tissue-dependent relaxation effects. In this work, a three-compartment model (tissue, free water and blood) that includes relaxation terms is introduced. To estimate the model parameters, in vivo human data were collected with multiple echo times (TE), inversion times (TI) and b-values, which allowed a direct relaxation estimate alongside estimation of perfusion, diffusion and fractional volume parameters. Compared to conventional two-compartment models (with and without relaxation compensation), the three-compartment model showed less effects of CSF contamination. The proposed model yielded significantly different volume fractions of blood and tissue compared to the non-relaxation-compensated model, as well as to the conventional two-compartment model, suggesting that previously reported parameter ranges, using models that do not account for relaxation, should be reconsidered.

White matter changes in the perforant path area in patients with amyotrophic lateral sclerosis

OBJECTIVE

The aim of this study was to test the hypothesis that white matter degeneration of the perforant path - as part of the Papez circuit - is a key feature of amyotrophic lateral sclerosis (ALS), even in the absence of frontotemporal dementia (FTD) or deposition of pTDP-43 inclusions in hippocampal granule cells.

METHODS

We used diffusion Magnetic Resonance Imaging (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of post mortem hippocampus specimens from controls (n = 5) and ALS patients (n = 14) to study white matter degeneration in the perforant path.

RESULTS

diffusion Magnetic Resonance Imaging demonstrated a decrease in fractional anisotropy (P = 0.01) and an increase in mean diffusivity (P = 0.01) in the perforant path in ALS compared to controls. PLI-myelin density was lower in ALS (P = 0.05) and correlated with fractional anisotropy (r = 0.52, P = 0.03). These results were confirmed by immunohistochemistry; both myelin (proteolipid protein, P = 0.03) and neurofilaments (SMI-312, P = 0.02) were lower in ALS. Two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus, but with comparable myelination levels in the perforant path to other ALS cases.

CONCLUSION

We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of cognitive decline to the amount of white matter atrophy in the perforant path.

Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network

PURPOSE

In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) method has been explored for reconstructing full fODFs. To achieve a reliable fitting, similar to other model-based approaches, a large number of diffusion measurements is typically required for MSMT-CSD method. The prolonged acquisition is, however, not feasible in practical clinical routine and is prone to motion artifacts. To accelerate the acquisition, we proposed a method to reconstruct the fODF from downsampled diffusion-weighted images (DWIs) by leveraging the strong inference ability of the deep convolutional neural network (CNN).

METHODS

The method treats spherical harmonics (SH)-represented DWI signals and fODF coefficients as inputs and outputs, respectively. To compensate for the reduced gradient directions with reduced number of DWIs in acquisition in each voxel, its surrounding voxels are incorporated by the network for exploiting their spatial continuity. The resulting fODF coefficients are fitted with applying the CNN in a multi-target regression model. The network is composed of two convolutional layers and three fully connected layers. To obtain an initial evaluation of the method, we quantitatively measured its performance on a simulated dataset. Then, for in vivo tests, we employed data from 24 subjects from the Human Connectome Project (HCP) as training set and six subjects as test set. The performance of the proposed method was primarily compared to the super-resolved MSMT-CSD with the decreasing number of DWIs. The fODFs reconstructed by MSMT-CSD from all available 288 DWIs were used as training labels and the reference standard. The performance was quantitatively measured by the angular correlation coefficient (ACC) and the mean angular error (MAE).

RESULTS

For the simulated dataset, the proposed method exhibited the potential advantage over the model reconstruction. For the in vivo dataset, it achieved superior results over the MSMT-CSD in all the investigated cases, with its advantage more obvious when a limited number of DWIs were used. As the number of DWIs was reduced from 95 to 25, the median ACC ranged from 0.96 to 0.91 for the CNN, but 0.93 to 0.77 for the MSMT-CSD (with perfect score of 1). The angular error in the typical regions of interest (ROIs) was also much lower, especially in multi-fiber regions. The average MAE for the CNN method in regions containing one, two, three fibers was, respectively, 1.09°, 2.75°, and 8.35° smaller than the MSMT-CSD method. The visual inception of the fODF further confirmed this superiority. Moreover, the tractography results validated the effectiveness of the learned fODF, in preserving known major branching fibers with only 25 DWIs.

CONCLUSION

Experiments on HCP datasets demonstrated the feasibility of the proposed method in recovering fODFs from up to 11-fold reduced number of DWIs. The proposed method offers a new streamlined reconstruction procedure and exhibits promising potential in acquisition acceleration for the reconstruction of fODFs with good accuracy.

Diffusion tensor distribution imaging

Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.

Effects of sustained cognitive activity on white matter microstructure and cognitive outcomes in healthy middle-aged adults: A systematic review

Adults who remain cognitively active may be protected from age-associated changes in white matter (WM) and cognitive decline. To determine if cognitive activity is a precursor for WM plasticity, the available literature was systematically searched for Region of Interest (ROI) and whole-brain studies assessing the efficacy of cognitive training (CT) on WM microstructure using Diffusion Tensor Imaging (DTI) in healthy adults (> 40 years). Seven studies were identified and included in this review. Results suggest there are beneficial effects to WM microstructure after CT in frontal and medial brain regions, with some studies showing improved performance in cognitive outcomes. Benefits of CT were shown to be protective against age-related WM microstructure decline by either maintaining or improving WM after training. These results have implications for determining the capacity for training-dependent WM plasticity in older adults and whether CT can be utilised to prevent age-associated cognitive decline. Additional studies with standardised training and imaging protocols are needed to confirm these outcomes.

Perivascular space fluid contributes to diffusion tensor imaging changes in white matter

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.

White matter microstructure disruptions mediate the adverse relationships between hypertension and multiple cognitive functions in cognitively intact older adults

Although hypertension is a prominent vascular risk factor for late-life cognitive decline, the underlying pathophysiological mechanism remains unclear. Accordingly, the aim of this study was to examine the role of white matter microstructural integrity in hypertension-related cognitive detriments. We recruited 66 cognitively normal older adults, comprising 41 hypertensive patients and 25 normotensive controls. All participants underwent a comprehensive neuropsychological battery. White matter microstructural integrity was assessed using a tract-based automatic analysis approach derived from diffusion spectrum imaging. Mediating effects of white matter integrity were evaluated using structural equation modeling analyses. The results revealed that hypertensive older adults displayed poorer processing speed, executive function, and memory encoding. Lower white matter microstructural integrity was observed in the hypertensive elderly patients, primarily in long-range association fiber bundles. In particular, low microstructural integrity in specific tract bundles connecting frontal and posterior cerebral regions was found to underlie the adverse relationships between hypertension and multiple cognitive domains, including processing speed, executive function, memory encoding, and memory retention. Our findings suggest that hypertension may impair multiple cognitive functions by undermining white matter microstructures, even in cognitively intact older adults, thus further highlighting the necessity of monitoring vascular health to prevent cognitive decline.

Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease

Introduction

Although increased mean diffusivity of the white matter has been repeatedly linked to Alzheimer’s disease pathology, the underlying mechanism is not known.

Methods

Here, we used ADNI-3 multishell diffusion magnetic resonance imaging data to separate the diffusion signal of the parenchyma from less hindered fluid pools within the white matter such as perivascular space fluid and fluid-filled cavities.

Results

We found that the source of the pathological increase of the mean diffusivity is the increased nonparenchymal fluid, often found in lacunes and perivascular spaces. In this cohort, the cognitive decline was significantly associated with the fluid increase and not with the microstructural changes of the white matter parenchyma itself. The white matter fluid increase was dominantly observed in the sagittal stratum and anterior thalamic radiation.

Discussion

These findings are positive steps toward understanding the pathophysiology of white matter alteration and its role in the cognitive decline.

Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: An empirical study using 7T MRI

BACKGROUND

Although diffusion gradient directions and b-values have been optimized for diffusion kurtosis imaging (DKI), little is known about the effect of signal averaging on DKI reliability.

PURPOSE

To evaluate how signal averaging influences the reliability of DKI indices using two gradient encoding schemes with three spatial resolutions.

STUDY TYPE

Prospective.

ANIMAL MODEL

Fifteen naïve Sprague-Dawley rats.

FIELD STRENGTH/SEQUENCE

DKI was performed at 7T using two schemes (30 directions with three b-values [30d-3b] and six directions with 15 b-values [6d-15b]), three resolutions, and eight repetitions.

ASSESSMENT

DKI reliability was assessed using voxelwise relative error (σ) and test-retest error of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) within gray matter (GM) and white matter (WM). The number of excitations (NEX) was optimized by considering DKI reliability. The influence of the partial volume effect (PVE) was also assessed.

STATISTICAL TEST

One-way analysis of variance.

RESULTS

The 30d-3b scheme, compared with the 6d-15b scheme, exhibited apparently smaller σ_FA and σ_MK (eg, at NEX 1, in GM, for three resolutions, σ_FA : 19.9-38.2% vs. 34.2-61.4%, σ_MK : 6.9-11.4% vs. 14.1-15.4%) and similar σ_MD (all differences between two schemes <1.6%). The optimal NEX was determined as 2 for enabling a reliable measurement of DKI-derived indices. The PVE at the lowest resolution apparently increased σ_FA for both schemes (19.9% for 30d-3b and 34.2% for 6d-15b) and σ_MK for the 6d-15b scheme (14.7%) in GM, and exerted lower effects on MK values for the 30d-3b scheme (P > 0.05).

DATA CONCLUSION

A higher number of diffusion directions would benefit FA and MK estimation. A higher spatial resolution helps to reduce PVE. By using the 30d-3b scheme, MK is considered a robust index to reflect microstructural changes in GM and WM. We propose a systematic approach to determine the optimal DKI protocols for appropriate preclinical settings.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1593-1603.

Imaging brain microstructure with diffusion MRI: practicality and applications

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term.

A review of diffusion MRI of typical white matter development from early childhood to young adulthood

Understanding typical, healthy brain development provides a baseline from which to detect and characterize brain anomalies associated with various neurological or psychiatric disorders and diseases. Diffusion MRI is well suited to study white matter development, as it can virtually extract individual tracts and yield parameters that may reflect alterations in the underlying neural micro-structure (e.g. myelination, axon density, fiber coherence), though it is limited by its lack of specificity and other methodological concerns. This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood (4-35 years). Conclusions about anatomical location, rates, and timing of white matter development with age are discussed, as well as the influence of image acquisition, analysis, age range/sample size, and statistical model. Despite methodological variability between studies, some consistent findings have emerged from the literature. Specifically, diffusion studies of neurodevelopment overwhelmingly demonstrate regionally varying increases of fractional anisotropy and decreases of mean diffusivity during childhood and adolescence, some of which continue into adulthood. While most studies use linear fits to model age-related changes, studies with sufficient sample sizes and age range provide clear evidence that white matter development (as indicated by diffusion) is non-linear. Several studies further suggest that maturation in association tracts with frontal-temporal connections continues later than commissural and projection tracts. The emerging contributions of more advanced diffusion methods are also discussed, as they may reveal new aspects of white matter development. Although non-specific, diffusion changes may reflect increases of myelination, axonal packing, and/or coherence with age that may be associated with changes in cognition.

Diffusion MRI fiber tractography of the brain

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

Subtle white matter alterations in schizophrenia identified with a new measure of fiber density

Altered cerebral connectivity is one of the core pathophysiological mechanism underlying the development and progression of information-processing deficits in schizophrenia. To date, most diffusion tensor imaging (DTI) studies used fractional anisotropy (FA) to investigate disrupted white matter connections. However, a quantitative interpretation of FA changes is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of white matter alterations could be achieved by measuring fiber density (FD) - a novel non-tensor-derived diffusion marker. This study investigates, for the first time, FD alterations in schizophrenia patients. FD and FA maps were derived from diffusion data of 25 healthy controls (HC) and 21 patients with schizophrenia (SZ). Using tract-based spatial statistics (TBSS), group differences in FD and FA were investigated across the entire white matter. Furthermore, we performed a region of interest (ROI) analysis of frontal fasciculi to detect potential correlations between FD and positive symptoms. As a result, whole brain TBSS analysis revealed reduced FD in SZ patients compared to HC in several white matter tracts including the left and right thalamic radiation (TR), superior longitudinal fasciculus (SLF), corpus callosum (CC), and corticospinal tract (CST). In contrast, there were no significant FA differences between groups. Further, FD values in the TR were negatively correlated with the severity of positive symptoms and medication dose in SZ patients. In summary, a novel diffusion-weighted data analysis approach enabled us to identify widespread FD changes in SZ patients with most prominent white matter alterations in the frontal and subcortical regions. Our findings suggest that the new FD measure may be more sensitive to subtle changes in the white matter microstructure compared to FA, particularly in the given population. Therefore, investigating FD may be a promising approach to detect subtle changes in the white matter microstructure of altered connectivity in schizophrenia.

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.

A Practical Guide to Variable Selection in Structural Equation Models with Regularized MIMIC Models

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters. We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p setting, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors and small sample size. We illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short term memory, as well as identifying demographic correlates of stress, anxiety and depression. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.

Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA^DTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA^TDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA^DTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FA^TDF detected robust widespread associations with clinical measures, while FA^DTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

Clinical Applications for Diffusion MRI and Tractography of Cranial Nerves Within the Posterior Fossa: A Systematic Review

Objective: This paper presents a systematic review of diffusion MRI (dMRI) and tractography of cranial nerves within the posterior fossa. We assess the effectiveness of the diffusion imaging methods used and examine their clinical applications. Methods: The Pubmed, Web of Science and EMBASE databases were searched from January 1st 1997 to December 11th 2017 to identify relevant publications. Any study reporting the use of diffusion imaging and/or tractography in patients with confirmed cranial nerve pathology was eligible for selection. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool. Results: We included 41 studies comprising 16 studies of patients with trigeminal neuralgia (TN), 22 studies of patients with a posterior fossa tumor and three studies of patients with other pathologies. Most acquisition protocols used single-shot echo planar imaging (88%) with a single b-value of 1,000 s/mm2 (78%) but there was significant variation in the number of gradient directions, in-plane resolution, and slice thickness between studies. dMRI of the trigeminal nerve generated interpretable data in all cases. Analysis of diffusivity measurements found significantly lower fractional anisotropy (FA) values within the root entry zone of nerves affected by TN and FA values were significantly lower in patients with multiple sclerosis. Diffusivity values within the trigeminal nerve correlate with the effectiveness of surgical treatment and there is some evidence that pre-operative measurements may be predictive of treatment outcome. Fiber tractography was performed in 30 studies (73%). Most studies evaluating fiber tractography involved patients with a vestibular schwannoma (82%) and focused on generating tractography of the facial nerve to assist with surgical planning. Deterministic tractography using diffusion tensor imaging was performed in 93% of cases but the reported success rate and accuracy of generating fiber tracts from the acquired diffusion data varied considerably. Conclusions: dMRI has the potential to inform our understanding of the microstructural changes that occur within the cranial nerves in various pathologies. Cranial nerve tractography is a promising technique but new avenues of using dMRI should be explored to optimize and improve its reliability.

A registration method for improving quantitative assessment in probabilistic diffusion tractography

Diffusion MRI-based probabilistic tractography is a powerful tool for non-invasively investigating normal brain architecture and alterations in structural connectivity associated with disease states. Both voxelwise and region-of-interest methods of analysis are capable of integrating population differences in tract amplitude (streamline count or density), given proper alignment of the tracts of interest. However, quantification of tract differences (between groups, or longitudinally within individuals) has been hampered by two related features of white matter. First, it is unknown to what extent healthy individuals differ in the precise location of white matter tracts, and to what extent experimental factors influence perceived tract location. Second, white matter lacks the gross neuroanatomical features (e.g., gyri, histological subtyping) that make parcellation of grey matter plausible - determining where tracts "should" lie within larger white matter structures is difficult. Accurately quantifying tractographic connectivity between individuals is thus inherently linked to the difficulty of identifying and aligning precise tract location. Tractography is often utilized to study neurological diseases in which the precise structural and connectivity abnormalities are unknown, underscoring the importance of accounting for individual differences in tract location when evaluating the strength of structural connectivity. We set out to quantify spatial variance in tracts aligned through a standard, whole-brain registration method, and to assess the impact of location mismatch on groupwise assessments of tract amplitude. We then developed a method for tract alignment that enhances the existing standard whole brain registration, and then tested whether this method improved the reliability of groupwise contrasts. Specifically, we conducted seed-based probabilistic diffusion tractography from primary motor, supplementary motor, and visual cortices, projecting through the corpus callosum. Streamline counts decreased rapidly with movement from the tract center (-35% per millimeter); tract misalignment of a few millimeters caused substantial compromise of amplitude comparisons. Alignment of tracts "peak-to-peak" is essential for accurate amplitude comparisons. However, for all transcallosal tracts registered through the whole-brain method, the mean separation distance between an individual subject's tract and the average tract (3.2 mm) precluded accurate comparison: at this separation, tract amplitudes were reduced by 74% from peak value. In contrast, alignment of subcortical tracts (thalamo-putaminal, pallido-rubral) was substantially better than alignment for cortical tracts; whole-brain registration was sufficient for these subcortical tracts. We demonstrated that location mismatches in cortical tractography were sufficient to produce false positive and false negative amplitude estimates in both groupwise and longitudinal comparisons. We then showed that our new tract alignment method substantially reduced location mismatch and improved both reliability and statistical power of subsequent quantitative comparisons.

Tractography for Surgical Neuro-Oncology Planning: Towards a Gold Standard

Magnetic resonance imaging tractography permits in vivo visualization of white matter structures. Aside from its academic value, tractography has been proven particularly useful to neurosurgeons for preoperative planning. Preoperative tractography permits both qualitative and quantitative analyses of tumor effects upon surrounding white matter, allowing the surgeon to specifically tailor their operative approach. Despite its benefits, there is controversy pertaining to methodology, implementation, and interpretation of results in this context. High-definition fiber tractography (HDFT) is one of several non-tensor tractography approaches permitting visualization of crossing white matter trajectories at high resolutions, dispensing with the well-known shortcomings of diffusion tensor imaging (DTI) tractography. In this article, we provide an overview of the advantages of HDFT in a neurosurgical context, derived from our considerable experience implementing the technique for academic and clinical purposes. We highlight nuances of qualitative and quantitative approaches to using HDFT for brain tumor surgery planning, and integration of tractography with complementary operative adjuncts, and consider areas requiring further research.

Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments

Precise quantification of brain tissue micro-architecture using diffusion MRI is hampered by the conflation of diffusion-attenuated signals from micro-environments that can be orientationally heterogeneous due to complex fiber configurations, such as crossing, fanning, and bending, and compartmentally heterogeneous due to variability in tissue organization. In this paper, we introduce a method, called Spherical Mean Spectrum Imaging (SMSI), for quantification of tissue microstructure. SMSI does not assume a fixed number of compartments, but characterizes the signal as a spectrum of fine- to coarse-scale diffusion processes. Using SMSI, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (μFA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that SMSI is fast, accurate, and can overcome biases in state-of-the-art microstructure models. We demonstrate its application in probing microstructural changes in the baby brain during the first two years of life.

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood

In this article, we used High Angular Resolution Diffusion Imaging (HARDI) with advanced anatomically constrained particle filtering tractography to investigate the role of the arcuate fasciculus (AF) and the middle longitudinal fasciculus (MdLF) in speech perception in noise in younger and older adults. Fourteen young and 15 elderly adults completed a syllable discrimination task in the presence of broadband masking noise. Mediation analyses revealed few effects of age on white matter (WM) in these fascicles but broad effects of WM on speech perception, independently of age, especially in terms of sensitivity and criterion (response bias), after controlling for individual differences in hearing sensitivity and head size. Indirect (mediated) effects of age on speech perception through WM microstructure were also found, after controlling for individual differences in hearing sensitivity and head size, with AF microstructure related to sensitivity, response bias and phonological priming, and MdLF microstructure more strongly related to response bias. These findings suggest that pathways of the perisylvian region contribute to speech processing abilities, with relatively distinct contributions for the AF (sensitivity) and MdLF (response bias), indicative of a complex contribution of both phonological and cognitive processes to age-related speech perception decline. These results provide new and important insights into the roles of these pathways as well as the factors that may contribute to elderly speech perception deficits. They also highlight the need for a greater focus to be placed on studying the role of WM microstructure to understand cognitive aging.

Gadolinium-enhanced imaging of pediatric thoracic lymphoma: is intravenous contrast really necessary?

OBJECTIVES

Increasing awareness of potential side effects from gadolinium-based contrast agents has underlined the need for contrast-free magnetic resonance imaging (MRI). Numerous recent articles evaluated the risk of potential brain deposits, with the result that research is putting the focus more on alternative unenhanced imaging techniques. The aim of this study was to determine the need for contrast media for chest MRI in primary staging and follow-up care of lymphoma.

METHODS

This monocentric, retrospective study encompassed patients under 25 years of age who had undergone histopathological examination of thoracic lymph nodes and at least one chest MRI examination with unenhanced and contrast-enhanced sequences. Seven different thoracic lymph node stations including mediastinal, hilar, periclavicular, and axillary regions were evaluated by two readers regarding lesion diameter, number, shape, necrosis, and infiltration of surrounding structures. Findings were categorized into suspicious (> 1 cm; round; necrosis; infiltration) or non-suspicious.

RESULTS

Fifty-one patients (mean age, 16.0 ± 3.7 yrs) with thoracic Hodgkin (70.6%) and non-Hodgkin lymphoma (25.5%) and lymphadenopathy (3.9%) were retrospectively included. Most lymph nodes categorized as suspicious were located in the mediastinal station (86.4%). High agreement (κ = 0.81) between unenhanced and contrast-enhanced sequences was found for both suspicious and non-suspicious lymph nodes. Significant (p < 0.001), but small difference (1 mm) was observed only in sizing mediastinal lymph nodes (all other p > 0.05). No significant difference (smallest p = 0.08) was shown for the use of five different types of contrast media.

CONCLUSION

MRI in young patients with thoracic lymphoma can safely be done without the use of contrast agent.

KEY POINT

• Thoracic magnetic resonance imaging in young lymphoma patients can safely be done without gadolinium-based contrast agents.

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning

PURPOSE

To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging.

METHODS

Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures.

RESULTS

The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations.

CONCLUSIONS

Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.

Linking spherical mean diffusion weighted signal with intra-axonal volume fraction

Diffusion MRI has been widely used to assess brain tissue microstructure. However, the conventional diffusion tensor imaging (DTI) is inadequate for characterizing fiber direction or fiber density in voxels with crossing fibers in brain white matter. The constrained spherical deconvolution (CSD) technique has been proposed to measure the complex fiber orientation distribution (FOD) using a single high b-value (b ≥ 3000 s/mm²) to derive the intra-axonal volume fraction (V_in) from the calculated FOD. Recently, the spherical mean technique (SMT) was developed to fit V_in directly from a multi-compartment model with multi-shell b-values. Although different numbers of b-values are needed in the two techniques, both methods have been suggested to be related to the spherical mean diffusion weighted signal (S¯). The current study compared the two techniques on the same high-quality Human Connectome Project diffusion data and investigated the relation between S¯ and V_in systematically. At high b-values (b ≥ 3000 s/mm²), S¯ is linearly related to V_in, and S¯ provides similar contrast with V_in in white matter. At low b-values (b ~ 1000 s/mm²), the linear relation between S¯ and V_in is sensitive to the variations of intrinsic diffusivity. These results demonstrate that S¯ measured with the typical b-value of 1000 s/mm² is not an indicator of V_in, and previous DTI studies acquired with b = 1000 s/mm² cannot be re-analyzed to provide V_in-weighted contrast.

Re-examining age-related differences in white matter microstructure with free-water corrected diffusion tensor imaging

Diffusion tensor imaging (DTI) has been used extensively to investigate white matter (WM) microstructural changes during healthy adult aging. However, WM fibers are known to shrink throughout the lifespan, leading to larger interstitial spaces with age. This could allow more extracellular free water molecules to bias DTI metrics, which are relied upon to provide WM microstructural information. Using a cohort of 212 participants, we demonstrate that WM microstructural changes in aging are potentially less pronounced than previously reported once the free water compartment is eliminated. After free water elimination, DTI parameters show age-related differences that match histological evidence of myelin degradation and debris accumulation. The fraction of free water is further shown to associate better with age than any of the conventional DTI parameters. Our findings suggest that DTI analyses involving free water are likely to yield novel insight into retrospective re-analysis of data and to answer new questions in ongoing DTI studies of brain aging.

Novel insights into in-vivo diffusion tensor cardiovascular magnetic resonance using computational modeling and a histology-based virtual microstructure

Purpose

To develop histology‐informed simulations of diffusion tensor cardiovascular magnetic resonance (DT‐CMR) for typical in‐vivo pulse sequences and determine their sensitivity to changes in extra‐cellular space (ECS) and other microstructural parameters.

Methods

We synthesised the DT‐CMR signal from Monte Carlo random walk simulations. The virtual tissue was based on porcine histology. The cells were thickened and then shrunk to modify ECS. We also created idealised geometries using cuboids in regular arrangement, matching the extra‐cellular volume fraction (ECV) of 16–40%. The simulated voxel size was 2.8 × 2.8 × 8.0 mm³ for pulse sequences covering short and long diffusion times: Stejskal–Tanner pulsed‐gradient spin echo, second‐order motion‐compensated spin echo, and stimulated echo acquisition mode (STEAM), with clinically available gradient strengths.

Results

The primary diffusion tensor eigenvalue increases linearly with ECV at a similar rate for all simulated geometries. Mean diffusivity (MD) varies linearly, too, but is higher for the substrates with more uniformly distributed ECS. Fractional anisotropy (FA) for the histology‐based geometry is higher than the idealised geometry with low sensitivity to ECV, except for the long mixing time of the STEAM sequence. Varying the intra‐cellular diffusivity (D_IC) results in large changes of MD and FA. Varying extra‐cellular diffusivity or using stronger gradients has minor effects on FA. Uncertainties of the primary eigenvector orientation are reduced using STEAM.

Conclusions

We found that the distribution of ECS has a measurable impact on DT‐CMR parameters. The observed sensitivity of MD and FA to ECV and D_IC has potentially interesting applications for interpreting in‐vivo DT‐CMR parameters.

Altered white matter connectivity in patients with schizophrenia: An investigation using public neuroimaging data from SchizConnect

Several studies have produced extensive evidence on white matter abnormalities in schizophrenia (SZ). However, optimum consistency and reproducibility have not been achieved, and reported low white matter tract integrity in patients with SZ varies between studies. A whole-brain imaging study with a large sample size is needed. This study aimed to investigate white matter integrity in the corpus callosum and connections between regions of interests (ROIs) in the same hemisphere in 122 patients with SZ and 129 healthy controls with public neuroimaging data from SchizConnect. For each diffusion-weighted image (DWI), two-tensor full-brain tractography was performed; DWIs were parcellated by processing and registering T1 images with FreeSurfer and Advanced Normalization Tools. White matter query language was used to extract white matter fiber tracts. We evaluated group differences in means of diffusion measures between the patients and controls, and correlations of diffusion measures with the severity of clinical symptoms and cognitive impairment in the patients using the Positive and Negative Syndrome Scale (PANSS), a letter-number sequencing (LNS) test, vocabulary test, letter fluency test, category fluency test, and trail-making test, part A. To correct for multiple comparisons, a false discovery rate of q < 0.05 was applied. In patients with SZ, we observed significant radial diffusivity (RD) and trace (TR) increases in left thalamo-occipital tracts and the right uncinate fascicle, and a significant RD increase in the right middle longitudinal fascicle (MDLF) and the right superior longitudinal fascicle ii. Correlations were present between TR of left thalamo-occipital tracts, and the letter fluency test and the LNS test, and RD in the right MDLF and PANSS positive subscale score. However, these correlations were not significant after correction for multiple comparisons. These results indicated widespread white matter fiber tract abnormalities in patients with SZ, contributing to SZ pathophysiology.