Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI

Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models and tractometry

Late-life depression (LLD) is both common and disabling and doubles the risk of dementia onset. Apathy might constitute an additional risk of cognitive decline but clear understanding of its pathophysiology is lacking. While white matter (WM) alterations have been assessed using diffusion tensor imaging (DTI), this model cannot accurately represent WM microstructure. We hypothesized that a more complex multi-compartment model would provide new biomarkers of LLD and apathy. Fifty-six individuals (LLD n = 35, 26 females, 75.2 ± 6.4 years, apathy evaluation scale scores (41.8 ± 8.7) and Healthy controls, n = 21, 16 females, 74.7 ± 5.2 years) were included. In this article, a tract-based approach was conducted to investigate novel diffusion model biomarkers of LLD and apathy by interpolating microstructural metrics directly along the fiber bundle. We performed multivariate statistical analysis, combined with principal component analysis for dimensional data reduction. We then tested the utility of our framework by demonstrating classically reported from the literature modifications in LDD while reporting new results of biological-basis of apathy in LLD. Finally, we aimed to investigate the relationship between apathy and microstructure in different fiber bundles. Our study suggests that new fiber bundles, such as the striato-premotor tracts, may be involved in LLD and apathy, which bring new light of apathy mechanisms in major depression. We also identified statistical changes in diffusion MRI metrics in 5 different tracts, previously reported in major cognitive disorders dementia, suggesting that these alterations among these tracts are both involved in motivation and cognition and might explain how apathy is a prodromal phase of degenerative disorders.

Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links Through Daily Activity Patterns and Brain Connectivity Analysis

BACKGROUND

Better understanding apathy in late-life depression would help improve prediction of poor prognosis of diseases such as dementia. Actimetry provides an objective and ecological measure of apathy from patients' daily motor activity. We aimed to determine whether patterns of motor activity were associated with apathy and brain connectivity in networks that underlie goal-directed behaviors.

METHODS

Resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging were collected from 38 nondemented participants with late-life depression. Apathy was evaluated using the diagnostic criteria for apathy, Apathy Evaluation Scale, and Apathy Motivation Index. Functional principal components (fPCs) of motor activity were derived from actimetry recordings taken for 72 hours. Associations between fPCs and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPCs were identified via threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry.

RESULTS

We found 2 fPCs associated with apathy: mean diurnal activity, negatively associated with Apathy Evaluation Scale scores, and an early chronotype, negatively associated with Apathy Motivation Index scores. Mean diurnal activity was associated with increased connectivity in the default mode, cingulo-opercular, and frontoparietal networks, while chronotype was associated with a more heterogeneous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs.

CONCLUSIONS

Our findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in late-life depression, associated with modified functional connectivity of brain networks that underlie goal-directed behaviors.

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord

INTRODUCTION

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models.

METHODS

We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy.

RESULTS

Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue.

CONCLUSION

Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.

versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database

The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

Evaluation of white matter microstructure in pediatric onset multiple sclerosis with diffusion compartment imaging

BACKGROUND AND PURPOSE

Pediatric-onset multiple sclerosis (POMS) shows earlier axonal involvement and greater axonal loss than in adults. We aim to characterize the white matter (WM) microstructural changes in POMS using a diffusion compartment imaging (DCI) model and compare it to standard diffusion tensor imaging (DTI).

METHODS

Eleven patients (2 males, mean age 18.8 ± 3.9 years) with a diagnosis of relapsing and remitting POMS (mean age at disease onset 13.8 ± 2.9 years, mean duration 5.1 ± 1.9 years) and healthy controls (8 males, mean age 26.4 ± 6.5 years) were recruited and imaged at 3 T. A 90-gradient set Cube and Sphere acquisition and a novel DCI model known as DIstribution of Anisotropic MicrOstructural eNvironments with Diffusion-weighted imaging (DIAMOND) were used to calculate a single anisotropic compartment, an isotropic compartment, and a free diffusion compartment. Lesions and contralateral normal-appearing white matter (NAWM) in patients and whole brain WM for controls were labeled.

RESULTS

Eleven patients and 11 controls were recruited. When comparing lesions and contralateral NAWM in patients using DCI, compartmental axial diffusivity, radial diffusivity (cRD), and mean diffusivity (cMD) were higher in lesions. Conversely, compartmental fractional anisotropy (cFA) and heterogeneity index were lower in lesions. An analysis of DTI equivalents showed the same trends. In whole-brain NAWM of patients compared to controls, cRD and cMD were higher and cFA was lower in patients.

CONCLUSION

Lesions in POMS can be accurately characterized by a DCI model. Incipient changes in NAWM seen in DCI may not be readily observable by DTI.

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging

Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.

Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

Constrained spherical deconvolution (CSD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW-MRI signal on the sphere can be represented as the spherical convolution of a single-fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW-MRI data is sampled shell-wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b-values. On shell-wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell-wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.

Myofiber organization in the failing systemic right ventricle

BACKGROUND

The right ventricle (RV) often fails when functioning as the systemic ventricle, but the cause is not understood. We tested the hypothesis that myofiber organization is abnormal in the failing systemic right ventricle.

METHODS

We used diffusion-weighted cardiovascular magnetic resonance imaging to examine 3 failing hearts explanted from young patients with a systemic RV and one structurally normal heart with postnatally acquired RV hypertrophy for comparison. Diffusion compartment imaging was computed to separate the free diffusive component representing free water from an anisotropic component characterizing the orientation and diffusion characteristics of myofibers. The orientation of each anisotropic compartment was displayed in glyph format and used for qualitative description of myofibers and for construction of tractograms. The helix angle was calculated across the ventricular walls in 5 locations and displayed graphically. Scalar parameters (fractional anisotropy and mean diffusivity) were compared among specimens.

RESULTS

The hypertrophied systemic RV has an inner layer, comprising about 2/3 of the wall, composed of hypertrophied trabeculae and an epicardial layer of circumferential myofibers. Myofibers within smaller trabeculae are aligned and organized with parallel fibers while larger, composite bundles show marked disarray, largely between component trabeculae. We observed a narrow range of helix angles in the outer, compact part of the wall consistent with aligned, approximately circumferential fibers. However, there was marked variation of helix angle in the inner, trabecular part of the wall consistent with marked variation in fiber orientation. The apical whorl was disrupted or incomplete and we observed myocardial whorls or vortices at other locations. Fractional anisotropy was lower in abnormal hearts while mean diffusivity was more variable, being higher in 2 but lower in 1 heart, compared to the structurally normal heart.

CONCLUSIONS

Myofiber organization is abnormal in the failing systemic RV and might be an important substrate for heart failure and arrhythmia. It is unclear if myofiber disorganization is due to hemodynamic factors, developmental problems, or both.

Evaluation of multi-shell diffusion MRI acquisition strategy on quantitative analysis using multi-compartment models

Background

Multi-compartment diffusion models such as Neurite Orientation Dispersion and Density Imaging (NODDI) have been increasingly used for diffusion MRI (dMRI) data processing in biomedical research. However, those models usually require multiple HARDI shells that may increase scanning duration substantially, and their application can be hindered in uncooperative patients (like infants) accordingly. Also, it is highly expected that the same dataset can be explored with multiple diffusion models for retrieving complementary information.

Methods

Multiple gradient-encoding schemes which consisted of 4-6 shells, moderate b-values (bmax =1,500 or 2,000 s/mm²), and 32-80 gradient directions were explored. The corresponding time of acquisition (TA) for a single scan ranged from 3 to 8 minutes respectively. The dMRI protocols were tested on macaque monkeys using a 3T clinical setting. The data were analysed using both NODDI and diffusion basic spectrum imaging (DBSI) models.

Results

The maps of orientation dispersion index (ODI) and CSF were consistent across the 4-6 shell sampling schemes. However, the corresponding intra-cellular volume fraction (ICVF) maps showed reduced pixel counts [1,100±98 (80 directions) vs. 806±70 (32 directions), one slice] in white matter when fewer gradient directions or lower b-value was applied. The hindered diffusion and CSF ratio maps were comparable across these sampling schemes. The maps of restricted diffusion ratio varied across the schemes. However, its mean ratios (0.23±0.02 vs. 0.22±0.01) and pixel counts (1,540±70 vs. 1,510±38, one slice) between the schemes of 80 and 32 directions with b=2,000 s/mm² were comparable.

Conclusions

The present study reports a fast multi-shell dMRI data acquisition and processing strategy which allows for obtaining complementary information about microstructural alteration and inflammation from a single dMRI data set with both NODDI and DBSI models. The proposed approach may be particularly useful for characterizing the neurodegenerative disorders in uncooperative patients like children or acute stroke patients in which brain injury is associated with inflammation.

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.

Altered Cellular White Matter But Not Extracellular Free Water on Diffusion MRI in Individuals at Clinical High Risk for Psychosis

OBJECTIVE

Detecting brain abnormalities in clinical high-risk populations before the onset of psychosis is important for tracking pathological pathways and for identifying possible intervention strategies that may impede or prevent the onset of psychotic disorders. Co-occurring cellular and extracellular white matter alterations have previously been implicated after a first psychotic episode. The authors investigated whether or not cellular and extracellular alterations are already present in a predominantly medication-naive cohort of clinical high-risk individuals experiencing attenuated psychotic symptoms.

METHODS

Fifty individuals at clinical high risk, of whom 40 were never medicated, were compared with 50 healthy control subjects, group-matched for age, gender, and parental socioeconomic status. 3-T multishell diffusion MRI data were obtained to estimate free-water imaging white matter measures, including fractional anisotropy of cellular tissue (FA_T) and the volume fraction of extracellular free water (FW).

RESULTS

Significantly lower FA_T was observed in the clinical high-risk group compared with the healthy control group, but no statistically significant FW alterations were observed between groups. Lower FA_T in the clinical high-risk group was significantly associated with a decline in Global Assessment of Functioning Scale (GAF) score compared with highest GAF score in the previous 12 months.

CONCLUSIONS

Cellular but not extracellular alterations characterized the clinical high-risk group, especially in those who experienced a decline in functioning. These cellular changes suggest an early deficit that possibly reflects a predisposition to develop attenuated psychotic symptoms. In contrast, extracellular alterations were not observed in this clinical high-risk sample, suggesting that previously reported extracellular abnormalities may reflect an acute response to psychosis, which plays a more prominent role closer to or at onset of psychosis.

The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.

White matter microstructure correlates with mathematics but not word reading performance in 13-year-old children born very preterm and full-term

Individuals born very preterm (VPT; <32 weeks' gestational age) are at increased risk of impaired mathematics and word reading performance, as well as widespread white matter microstructural alterations compared with individuals born full term (FT; ≥37 weeks' gestational age). To date, the link between academic performance and white matter microstructure is not well understood. This study aimed to investigate the associations between mathematics and reading performance with white matter microstructure in 114 VPT and 36 FT 13-year-old children. Additionally, we aimed to investigate whether the association of mathematics and reading performance with white matter microstructure in VPT children varied as a function of impairment. To do this, we used diffusion tensor imaging and advanced diffusion modelling techniques (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique), combined with a whole-brain analysis approach (Tract-Based Spatial Statistics). Mathematics performance across VPT and FT groups was positively associated with white matter microstructural measurements of fractional anisotropy and neurite density, and negatively associated with radial and mean diffusivities in widespread, bilateral regions. Furthermore, VPT children with a mathematics impairment (>1 standard deviation below FT mean) had significantly reduced neurite density compared with VPT children without an impairment. Reading performance was not significantly associated with any of the white matter microstructure parameters. Additionally, the associations between white matter microstructure and mathematics and reading performance did not differ significantly between VPT and FT groups. Our findings suggest that alterations in white matter microstructure, and more specifically lower neurite density, are associated with poorer mathematics performance in 13-year-old VPT and FT children. More research is required to understand the association between reading performance and white matter microstructure in 13-year-old children.

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Diffusion tensor and neurite density metrics were associated with mathematics.

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Associations were present in very preterm and full-term children.

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Associations were widespread throughout the white matter microstructure.

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Decreased neurite density was evident in children with a mathematics impairment.

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Limited evidence of associations between white matter microstructure and reading.

Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition

PURPOSE

To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

METHODS

Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

RESULTS

We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

CONCLUSION

The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer's disease

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

Longitudinal In Vivo Diffusion Magnetic Resonance Imaging Remote from the Lesion Site in Rat Spinal Cord Injury

Diffusion tensor imaging (DTI) has demonstrated success as a biomarker of spinal cord injury (SCI) severity as shown from numerous pre-clinical studies. However, artifacts from stabilization hardware at the lesion have precluded its use for longitudinal assessments. Previous research has documented ex vivo diffusion changes in the spinal cord both caudal and cranial to the injury epicenter. The aim of this study was to use a rat contusion model of SCI to evaluate the utility of in vivo cervical DTI after a thoracic injury. Forty Sprague-Dawley rats underwent a thoracic contusion (T8) of mild, moderate, severe, or sham severity. Magnetic resonance imaging (MRI) of the cervical cord was performed at 2, 30, and 90 days post-injury, and locomotor performance was assessed weekly using the Basso, Bresnahan, and Beattie (BBB) scoring scale. The relationships between BBB scores and MRI were assessed using region of interest analysis and voxel-wise linear regression of DTI, and free water elimination (FWE) modeling to reduce partial volume effects. At 90 days, axial diffusivity (AD_FWE), mean diffusivity (MD_FWE), and free water fraction (FWF_FWE) using the FWE model were found to be significantly correlated with BBB score. FWE was found to be more predictive of injury severity than conventional DTI, specifically at later time-points. This study validated the use of FWE technique in spinal cord and demonstrated its sensitivity to injury remotely.

Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex

INTRODUCTION

Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC.

METHODS

186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves.

RESULTS

TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting.

CONCLUSIONS

Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.

Assessing the validity of the approximation of diffusion-weighted-MRI signals from crossing fascicles by sums of signals from single fascicles

PURPOSE

To assess the validity of the superposition approximation for crossing fascicles, i.e., the assumption that the total diffusion-weighted MRI signal is the sum of the signals arising from each fascicle independently, even when the fascicles intermingle in a voxel.

METHODS

Monte Carlo simulations were used to study the impact of the approximation on the diffusion-weighted MRI signal and to assess whether this approximate model allows microstructural features of interwoven fascicles to be accurately estimated, despite signal differences.

RESULTS

Small normalized signal differences were observed, typically 10-3-10-2. The use of the approximation had little impact on the estimation of the crossing angle, the axonal density index, and the radius index in clinically realistic scenarios wherein the acquisition noise was the predominant source of errors. In the absence of noise, large systematic errors due to the superposition approximation only persisted for the radius index, mainly driven by a low sensitivity of diffusion-weighted MRI signals to small radii in general.

CONCLUSION

The use of the superposition approximation rather than a model of interwoven fascicles does not adversely impact the estimation of microstructural features of interwoven fascicles in most current clinical settings. Magn Reson Med 79:2332-2345, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Estimation of individual axon bundle properties by a Multi-Resolution Discrete-Search method

A stable, accurate and robust-to-noise method for the estimation of the intra-voxel bundle-wise diffusion properties for diffusion-weighted magnetic resonance imaging is presented. The proposed method overcomes some of the limitations of most of the multi-fiber algorithms in the literature and extends them to estimate the diffusion profiles, improving the estimation of the intra-voxel geometry at challenging microstructure configurations, that is to say: relatively small crossing angles, different voxel-wise anisotropic diffusion profiles and low SNR. The proposed methodology is based on four key novel ideas: (i) A Multi-Resolution Discrete-Search determines the orientation of the fiber bundles accurately and naturally constrains the sparsity on the recovered solutions; (ii) the determination of the number of fiber bundles using the F-test combined with a Rician bias correction; (iii) a Simultaneous Denoising and Fitting procedure that exploits the spatial redundancy of the axon bundles to achieve robustness with respect to noise; and (iv) a general framework for the estimation of the axial and radial diffusivity parameters independently for each voxel. A new useful evaluation metric is also proposed, which combines the information of the success rate in the estimated number of bundles and the angular error, avoiding in this way, some of the limitations these metrics have individually. A novel methodology for the evaluation of the methods on in-vivo data is also proposed. This work presents an extensive evaluation: the proposed methodology has been tested on state-of-the-art biophysical synthetic data for a variety of conditions, on the challenging spatially coherent phantom used on the HARDI reconstruction Challenge 2012, and on the recently released in-vivo MASSIVE data-set. Our results present significant improvements on the estimation of the number and orientation of the fiber bundles over the Spherical Deconvolution algorithm for multi-shell data, which is one of the most widely used multi-fiber algorithm. The results also show that, by the voxel-wise estimation of the diffusion profiles, the axial and radial diffusivity parameters are robustly estimated, being this essential for a better understanding of the individual bundle diffusion properties at challenging structural configurations.

Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET)

Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighboring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer's loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer's loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery. To prevent such impairment, achieving an accurate delineation of Meyer's loop with tractography is thus of utmost importance. However, current algorithms tend to under-estimate the full extent of Meyer's loop, mainly attributed to the aforementioned rule for connectivity which requires a direction to be chosen across a field of orientations. In this article, it was demonstrated that MAGNEtic Tractography (MAGNET) can benefit Meyer's loop delineation by incorporating anatomical knowledge of the expected fiber orientation to overcome local ambiguities. A new ROI-mechanism was proposed which supplies additional information to streamline reconstruction algorithms by the means of oriented priors. Their results showed that MAGNET can accurately generate Meyer's loop in all of our 15 child subjects (8 males; mean age 10.2 years ± 3.1). It effectively improved streamline coverage when compared with deterministic tractography, and significantly reduced the distance between the anterior-most portion of Meyer's loop and the temporal pole by 16.7 mm on average, a crucial landmark used for preoperative planning of temporal lobe surgery. Hum Brain Mapp 38:509-527, 2017. © 2016 Wiley Periodicals, Inc.

Analytical performance bounds for multi-tensor diffusion-MRI

PURPOSE

To examine the effects of MR acquisition parameters on brain white matter fiber orientation estimation and parameter of clinical interest in crossing fiber areas based on the Multi-Tensor Model (MTM).

MATERIAL AND METHODS

We compute the Cramér-Rao Bound (CRB) for the MTM and the parameter of clinical interest such as the Fractional Anisotropy (FA) and the dominant fiber orientations, assuming that the diffusion MRI data are recorded by a multi-coil, multi-shell acquisition system. Considering the sum-of-squares method for the reconstructed magnitude image, we introduce an approximate closed-form formula for Fisher Information Matrix that has the simplicity and easy interpretation advantages. In addition, we propose to generalize the FA and the mean diffusivity to the multi-tensor model.

RESULTS

We show the application of the CRB to reduce the scan time while preserving a good estimation precision. We provide results showing how the increase of the number of acquisition coils compensates the decrease of the number of diffusion gradient directions. We analyze the impact of the b-value and the Signal-to-Noise Ratio (SNR). The analysis shows that the estimation error variance decreases with a quadratic rate with the SNR, and that the optimum b-values are not unique but depend on the target parameter, the context, and eventually the target cost function.

CONCLUSION

In this study we highlight the importance of choosing the appropriate acquisition parameters especially when dealing with crossing fiber areas. We also provide a methodology for the optimal tuning of these parameters using the CRB.

Investigating white matter fibre density and morphology using fixel-based analysis

Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.

Strengths and weaknesses of state of the art fiber tractography pipelines--A comprehensive in-vivo and phantom evaluation study using Tractometer

Many different tractography approaches and corresponding isolated evaluation attempts have been presented over the last years, but a comparative and quantitative evaluation of tractography algorithms still remains a challenge, particularly in-vivo. The recently presented evaluation framework Tractometer is the first attempt to approach this challenge in a quantitative, comparative, persistent and open-access way. Tractometer is currently based on the evaluation of several global connectivity and tract-overlap metrics on hardware phantom data. The work presented in this paper focuses on extending Tractometer with a metric that enables the assessment of the local consistency of tractograms with the underlying image data that is not only applicable to phantom dataset but allows the quantitative and purely data-driven evaluation of in-vivo tractography. We furthermore present an extensive reference-based evaluation study of 25,000 tractograms obtained on phantom and in-vivo datasets using the presented local metric as well as all the methods already established in Tractometer. The experiments showed that the presented local metric successfully reflects the behavior of in-vivo tractography under different conditions and that it is consistent with the results of previous studies. Additionally our experiments enabled a multitude of conclusions with implications for fiber tractography in general, including recommendations regarding optimal choice of a local modeling technique, tractography algorithm, and parameterization, confirming and complementing the results of earlier studies.

Fiber Orientation and Compartment Parameter Estimation From Multi-Shell Diffusion Imaging

Diffusion MRI offers the unique opportunity of assessing the structural connections of human brains in vivo. With the advance of diffusion MRI technology, multi-shell imaging methods are becoming increasingly practical for large scale studies and clinical application. In this work, we propose a novel method for the analysis of multi-shell diffusion imaging data by incorporating compartment models into a spherical deconvolution framework for fiber orientation distribution (FOD) reconstruction. For numerical implementation, we develop an adaptively constrained energy minimization approach to efficiently compute the solution. On simulated and real data from Human Connectome Project (HCP), we show that our method not only reconstructs sharp and clean FODs for the modeling of fiber crossings, but also generates reliable estimation of compartment parameters with great potential for clinical research of neurological diseases. In comparisons with publicly available DSI-Studio and BEDPOSTX of FSL, we demonstrate that our method reconstructs sharper FODs with more precise estimation of fiber directions. By applying probabilistic tractography to the FODs computed by our method, we show that more complete reconstruction of the corpus callosum bundle can be achieved. On a clinical, two-shell diffusion imaging data, we also demonstrate the feasibility of our method in analyzing white matter lesions.

Improved fidelity of brain microstructure mapping from single-shell diffusion MRI

Diffusion weighted imaging (DWI) is sensitive to alterations in the diffusion of water molecules caused by microstructural barriers. Different microstructural compartments are characterized by differences in DWI signal. Diffusion tensor imaging conflates the signal from these compartments into a single tensor, which poorly represents multiple white matter fascicles and extra-axonal space. Diffusion compartment imaging (DCI) models overcome this limitation by providing parametric representations for the signal contribution of each compartment, thereby improving the fidelity of brain microstructure mapping. However, current approaches fail to identify DCI model parameters from conventional single-shell DWI with the desired accuracy. It has been demonstrated that part of this inaccuracy is due to the ill-posedness of the estimation of DCI model parameters from conventional single-shell acquisitions. In this paper, we propose to regularize the estimation problem for single-shell DWI by learning a prior distribution of DCI model parameters from DWI acquired at multiple b-values in an external population of subjects. We demonstrate that this population-informed prior enables, for the first time, accurate estimation of DCI models from single-shell DWI typically acquired in clinical practice. We validated our approach on synthetic and in vivo data of healthy subjects and patients with autism spectrum disorder. We applied the approach to population studies of brain microstructure in autism and found that introducing a population-informed prior leads to reliable detection of group differences. Our algorithm enables novel investigation from large existing DWI datasets in normal development and in disease and injury.

Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure

On the analysis of the Diffusion-Weighted Magnetic Resonance Images, multi-compartment models overcome the limitations of the well-known Diffusion Tensor model for fitting in vivo brain axonal orientations at voxels with fiber crossings, branching, kissing or bifurcations. Some successful multi-compartment methods are based on diffusion dictionaries. The diffusion dictionary-based methods assume that the observed Magnetic Resonance signal at each voxel is a linear combination of the fixed dictionary elements (dictionary atoms). The atoms are fixed along different orientations and diffusivity profiles. In this work, we present a sparse and adaptive diffusion dictionary method based on the Diffusion Basis Functions Model to estimate in vivo brain axonal fiber populations. Our proposal overcomes the following limitations of the diffusion dictionary-based methods: the limited angular resolution and the fixed shapes for the atom set. We propose to iteratively re-estimate the orientations and the diffusivity profile of the atoms independently at each voxel by using a simplified and easier-to-solve mathematical approach. As a result, we improve the fitting of the Diffusion-Weighted Magnetic Resonance signal. The advantages with respect to the former Diffusion Basis Functions method are demonstrated on the synthetic data-set used on the 2012 HARDI Reconstruction Challenge and in vivo human data. We demonstrate that improvements obtained in the intra-voxel fiber structure estimations benefit brain research allowing to obtain better tractography estimations. Hence, these improvements result in an accurate computation of the brain connectivity patterns.

Tubers are neither static nor discrete: Evidence from serial diffusion tensor imaging

OBJECTIVE

To assess the extent and evolution of tissue abnormality of tubers, perituber tissue, and normal-appearing white matter (NAWM) in patients with tuberous sclerosis complex using serial diffusion tensor imaging.

METHODS

We applied automatic segmentation based on a combined global-local intensity mixture model of 3T structural and 35 direction diffusion tensor MRIs (diffusion tensor imaging) to define 3 regions: tuber tissue, an equal volume perituber rim, and the remaining NAWM. For each patient, scan, lobe, and tissue type, we analyzed the averages of mean diffusivity (MD) and fractional anisotropy (FA) in a generalized additive mixed model.

RESULTS

Twenty-five patients (mean age 5.9 years; range 0.5-24.5 years) underwent 2 to 6 scans each, totaling 70 scans. Average time between scans was 1.2 years (range 0.4-2.9). Patient scans were compared with those of 73 healthy controls. FA values were lowest, and MD values were highest in tubers, next in perituber tissue, then in NAWM. Longitudinal analysis showed a positive (FA) and negative (MD) correlation with age in tubers, perituber tissue, and NAWM. All 3 tissue types followed a biexponential developmental trajectory, similar to the white matter of controls. An additional qualitative analysis showed a gradual transition of diffusion values across the tissue type boundaries.

CONCLUSIONS

Similar to NAWM, tuber and perituber tissues in tuberous sclerosis complex undergo microstructural evolution with age. The extent of diffusion abnormality decreases with distance to the tuber, in line with known extension of histologic, immunohistochemical, and molecular abnormalities beyond tuber pathology.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND)

PURPOSE

To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel.

THEORY AND METHODS

In prior work, a statistical model of the apparent diffusion coefficient was shown to well-characterize the diffusivity and heterogeneity of the mono-directional diffusion MRI signal. However, this model was unable to characterize the three-dimensional anisotropic diffusion observed in the brain. We introduce a new model that extends the statistical distribution representation to be fully tridimensional, in which apparent diffusion coefficients are extended to be diffusion tensors. The set of compartments present at a voxel is modeled by a finite sum of unimodal continuous distributions of diffusion tensors. Each distribution provides measures of each compartment microstructural diffusivity and heterogeneity.

RESULTS

The ability to estimate the tridimensional diffusivity and heterogeneity of multiple fascicles and of free diffusion is demonstrated.

CONCLUSION

Our novel tissue model allows for the characterization of the intra-voxel orientational heterogeneity, a prerequisite for accurate tractography while also characterizing the overall tridimensional diffusivity and heterogeneity of each tissue compartment. The model parameters can be estimated from short duration acquisitions. The diffusivity and heterogeneity microstructural parameters may provide novel indicator of the presence of disease or injury. Magn Reson Med 76:963-977, 2016. © 2015 Wiley Periodicals, Inc.

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

•

We introduce the fixel—a specific fibre population within a voxel.

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A novel method for whole-brain fixel-based analysis of diffusion MRI is presented.

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Structural connectivity between fixels is derived from template-based tractography.

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Connectivity information is used for tract-specific smoothing and enhancement.

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Quantitative assessment and an in vivo demonstration is performed.

Free water elimination diffusion tractography: A comparison with conventional and fluid-attenuated inversion recovery, diffusion tensor imaging acquisitions

PURPOSE

White matter tractography reconstructions using conventional diffusion tensor imaging (DTI) near cerebrospinal fluid (CSF) spaces are often adversely affected by CSF partial volume effects (PVEs). This study evaluates the ability of free water elimination (FWE) DTI methods to minimize the PVE of CSF for deterministic tractography applications.

MATERIALS AND METHODS

Ten healthy individuals were scanned with "traditional," FLAIR (fluid-attenuated inversion recovery), and FWE DTI scans. The fornix, corpus callosum, and cingulum bundles were reconstructed using deterministic tractography. The FWE DTI scan was performed twice to separately match total acquisition time (long FWE) and number of measurements (encoding directions, short FWE) to the FLAIR and "traditional" DTI scans. PVE resolution was determined based on reconstructed tract volume. All reconstructions underwent blinded review for anatomical correctness, symmetry, and completeness.

RESULTS

Reconstructions of the fornix demonstrated that the FWE and FLAIR scans produce more complete, anatomically plausible reconstructions than "traditional" DTI. Additionally, the tract reconstructions using FWE-DTI were significantly larger than when FLAIR was used with DTI (P < 0.0005). FLAIR and the FWE methods led to signal-to-noise ratio (SNR) reductions of 33% and 11%, respectively, compared with conventional DTI. The long and short FWE acquisitions did not significantly (P ≥ 0.31) differ from one another for any of the reconstructed tracts.

CONCLUSION

The FWE diffusion model overcomes CSF PVE without the time, SNR, and volumetric coverage penalties inherent to FLAIR DTI.

Diffusion MRI abnormalities detection with orientation distribution functions: a multiple sclerosis longitudinal study

We propose a new algorithm for the voxelwise analysis of orientation distribution functions between one image and a group of reference images. It relies on a generic framework for the comparison of diffusion probabilities on the sphere, sampled from the underlying models. We demonstrate that this method, combined to dimensionality reduction through a principal component analysis, allows for more robust detection of lesions on simulated data when compared to classical tensor-based analysis. We then demonstrate the efficiency of this pipeline on the longitudinal comparison of multiple sclerosis patients at an early stage of the disease: right after their first clinically isolated syndrome (CIS) and three months later. We demonstrate the predictive value of ODF-based scores for the early detection of lesions that will appear or heal.

Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Acquisition of a series of anisotropically oversampled acquisitions (so-called anisotropic "snapshots") and reconstruction in the image space has recently been proposed to increase the spatial resolution in diffusion weighted imaging (DWI), providing a theoretical 8x acceleration at equal signal-to-noise ratio (SNR) compared to conventional dense k-space sampling. However, in most works, each DW image is reconstructed separately and the fact that the DW images constitute different views of the same anatomy is ignored. In addition, current approaches are limited by their inability to reconstruct a high resolution (HR) acquisition from snapshots with different subsets of diffusion gradients: an isotropic HR gradient image cannot be reconstructed if one .of its anisotropic snapshots is missing, for example due to intra-scan motion, even if other snapshots for this gradient were successfully acquired. In this work, we propose a novel multi-snapshot DWI reconstruction technique that simultaneously achieves HR reconstruction and local tissue model estimation while enabling reconstruction from snapshots containing different subsets of diffusion gradients, providing increased robustness to patient motion and potential for acceleration. Our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We evaluate our approach with synthetic simulations, simulated multi-snapshot scenario and in vivo multi-snapshot imaging. We show that (1) our combined approach ultimately provides both better HR reconstruction and better tissue model estimation and (2) the error in the case of missing snapshots can be quantified. Our novel multi-snapshot technique will enable improved high spatial characterization of the brain connectivity and microstructure in vivo.

From expected propagator distribution to optimal q-space sample metric

We present a novel approach to determine a local q-space metric that is optimal from an information theoreticperspective with respect to the expected signal statistics. It should be noted that the approach does not attempt to optimize the quality of a pre-defined mathematical representation, the estimator. In contrast, our suggestion aims at obtaining the maximum amount of information without enforcing a particular feature representation. Results for three significantly different average propagator distributions are presented. The results show that the optimal q-space metric has a strong dependence on the assumed distribution in the targeted tissue. In many practical cases educated guesses can be made regarding the average propagator distribution present. In such cases the presented analysis can produce a metric that is optimal with respect to this distribution. The metric will be different at different q-space locations and is defined by the amount of additional information that is obtained when adding a second sample at a given offset from a first sample. The intention is to use the obtained metric as a guide for the generation of specific efficient q-space sample distributions for the targeted tissue.

Optimization of a free water elimination two-compartment model for diffusion tensor imaging

Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination model has previously been proposed to correct for the case of volume mixing. Here, this model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm(2)×number of directions) of 500×32 and 1500×32. The potential bias in fractional anisotropy induced by this two-compartment model was more than an order of magnitude less than the error of using the single diffusion tensor model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.

Intensive therapy induces contralateral white matter changes in chronic stroke patients with Broca's aphasia

Using a pre-post design, eleven chronic stroke patients with large left hemisphere lesions and nonfluent aphasia underwent diffusion tensor imaging and language testing before and after receiving 15 weeks of an intensive intonation-based speech therapy. This treated patient group was compared to an untreated patient group (n=9) scanned twice over a similar time period. Our results showed that the treated group, but not the untreated group, had reductions in fractional anisotropy in the white matter underlying the right inferior frontal gyrus (IFG, pars opercularis and pars triangularis), the right posterior superior temporal gyrus, and the right posterior cingulum. Furthermore, we found that greater improvements in speech production were associated with greater reductions in FA in the right IFG (pars opercularis). Thus, our findings showed that an intensive rehabilitation program for patients with nonfluent aphasia led to structural changes in the right hemisphere, which correlated with improvements in speech production.

Quantifying white matter tract diffusion parameters in the presence of increased extra-fiber cellularity and vasogenic edema

The effect of extra-fiber structural and pathological components confounding diffusion tensor imaging (DTI) computation was quantitatively investigated using data generated by both Monte-Carlo simulations and tissue phantoms. Increased extent of vasogenic edema, by addition of various amount of gel to fixed normal mouse trigeminal nerves or by increasing non-restricted isotropic diffusion tensor components in Monte-Carlo simulations, significantly decreased fractional anisotropy (FA) and increased radial diffusivity, while less significantly increased axial diffusivity derived by DTI. Increased cellularity, mimicked by graded increase of the restricted isotropic diffusion tensor component in Monte-Carlo simulations, significantly decreased FA and axial diffusivity with limited impact on radial diffusivity derived by DTI. The MC simulation and tissue phantom data were also analyzed by the recently developed diffusion basis spectrum imaging (DBSI) to simultaneously distinguish and quantify the axon/myelin integrity and extra-fiber diffusion components. Results showed that increased cellularity or vasogenic edema did not affect the DBSI-derived fiber FA, axial or radial diffusivity. Importantly, the extent of extra-fiber cellularity and edema estimated by DBSI correlated with experimentally added gel and Monte-Carlo simulations. We also examined the feasibility of applying 25-direction diffusion encoding scheme for DBSI analysis on coherent white matter tracts. Results from both phantom experiments and simulations suggested that the 25-direction diffusion scheme provided comparable DBSI estimation of both fiber diffusion parameters and extra-fiber cellularity/edema extent as those by 99-direction scheme. An in vivo 25-direction DBSI analysis was performed on experimental autoimmune encephalomyelitis (EAE, an animal model of human multiple sclerosis) optic nerve as an example to examine the validity of derived DBSI parameters with post-imaging immunohistochemistry verification. Results support that in vivo DBSI using 25-direction diffusion scheme correctly reflect the underlying axonal injury, demyelination, and inflammation of optic nerves in EAE mice.

Tensor metrics and charged containers for 3D Q-space sample distribution

This paper extends Jones' popular electrostatic repulsion based algorithm for distribution of single-shell Q-space samples in two fundamental ways. The first alleviates the single-shell requirement enabling full Q-space sampling. Such an extension is not immediately obvious since it requires distributing samples evenly in 3 dimensions. The extension is as elegant as it is simple: Add a container volume of the desired shape having a constant charge density and a total charge equal to the negative of the sum of the moving point charges. Results for spherical and cubic charge containers are given. The second extension concerns the way distances between sample point are measured. The Q-space samples represent orientation, rather than direction and it would seem appropriate to use a metric that reflects this fact, e.g. a tensor metric. To this end we present a means to employ a generalized metric in the optimization. Minimizing the energy will result in a 3-dimensional distribution of point charges that is uniform in the terms of the specified metric. The radically different distributions generated using different metrics pinpoints a fundamental question: Is there an inherent optimal metric for Q-space sampling? Our work provides a versatile tool to explore the role of different metrics and we believe it will be an important contribution to further the continuing debate and research on the matter.

A flocking based method for brain tractography

We propose a new method to estimate axonal fiber pathways from Multiple Intra-Voxel Diffusion Orientations. Our method uses the multiple local orientation information for leading stochastic walks of particles. These stochastic particles are modeled with mass and thus they are subject to gravitational and inertial forces. As result, we obtain smooth, filtered and compact trajectory bundles. This gravitational interaction can be seen as a flocking behavior among particles that promotes better and robust axon fiber estimations because they use collective information to move. However, the stochastic walks may generate paths with low support (outliers), generally associated to incorrect brain connections. In order to eliminate the outlier pathways, we propose a filtering procedure based on principal component analysis and spectral clustering. The performance of the proposal is evaluated on Multiple Intra-Voxel Diffusion Orientations from two realistic numeric diffusion phantoms and a physical diffusion phantom. Additionally, we qualitatively demonstrate the performance on in vivo human brain data.

A mathematical framework for the registration and analysis of multi-fascicle models for population studies of the brain microstructure

Diffusion tensor imaging (DTI) is unable to represent the diffusion signal arising from multiple crossing fascicles and freely diffusing water molecules. Generative models of the diffusion signal, such as multi-fascicle models, overcome this limitation by providing a parametric representation for the signal contribution of each population of water molecules. These models are of great interest in population studies to characterize and compare the brain microstructural properties. Central to population studies is the construction of an atlas and the registration of all subjects to it. However, the appropriate definition of registration and atlasing methods for multi-fascicle models have proven challenging. This paper proposes a mathematical framework to register and analyze multi-fascicle models. Specifically, we define novel operators to achieve interpolation, smoothing and averaging of multi-fascicle models. We also define a novel similarity metric to spatially align multi-fascicle models. Our framework enables simultaneous comparisons of different microstructural properties that are confounded in conventional DTI. The framework is validated on multi-fascicle models from 24 healthy subjects and 38 patients with tuberous sclerosis complex, 10 of whom have autism. We demonstrate the use of the multi-fascicle models registration and analysis framework in a population study of autism spectrum disorder.

A fully Bayesian inference framework for population studies of the brain microstructure

Models of the diffusion-weighted signal are of strong interest for population studies of the brain microstructure. These studies are typically conducted by extracting a scalar property from the model and subjecting it to null hypothesis significance testing. This process has two major limitations: the reported p-value is a weak predictor of the reproducibility of findings and evidence for the absence of microstructural alterations cannot be gained. To overcome these limitations, this paper proposes a Bayesian framework for population studies of the brain microstructure represented by multi-fascicle models. A hierarchical model is built over the biophysical parameters of the microstructure. Bayesian inference is performed by Hamiltonian Monte Carlo sampling and results in a joint posterior distribution over the latent microstructure parameters for each group. Inference from this posterior enables richer analyses of the brain microstructure beyond the dichotomy of significance testing. Using synthetic and in-vivo data, we show that our Bayesian approach increases reproducibility of findings from population studies and opens new opportunities in the analysis of the brain microstructure.

Cortical graph smoothing: a novel method for exploiting DWI-derived anatomical brain connectivity to improve EEG source estimation

The electroencephalography source estimation problem consists of inferring cortical activation from measurements of electrical potential taken on the scalp surface. This inverse problem is intrinsically ill-posed. In particular the dimensionality of cortical sources greatly exceeds the number of electrode measurements, and source estimation requires regularization to obtain a unique solution. In this work, we introduce a novel regularization function called cortical graph smoothing, which exploits knowledge of anatomical connectivity available from diffusion-weighted imaging. Given a weighted graph description of the anatomical connectivity of the brain, cortical graph smoothing penalizes the weighted sum of squares of differences of cortical activity across the graph edges, thus encouraging solutions with consistent activation across anatomically connected regions. We explore the performance of the cortical graph smoothing source estimates for analysis of the event related potential for simple motor tasks, and compare against the commonly used minimum norm, weighted minimum norm, LORETA and sLORETA source estimation methods. Evaluated over a series of 18 subjects, the proposed cortical graph smoothing method shows superior localization accuracy compared to the minimum norm method, and greater relative peak intensity than the other comparison methods.

Diffusion tensor imaging and related techniques in tuberous sclerosis complex: review and future directions

In this article, the authors aim to introduce the nonradiologist to diffusion tensor imaging (DTI) and its applications to both clinical and research aspects of tuberous sclerosis complex. Tuberous sclerosis complex is a genetic neurocutaneous syndrome with variable and unpredictable neurological comorbidity that includes refractory epilepsy, intellectual disability, behavioral abnormalities and autism spectrum disorder. DTI is a method for modeling water diffusion in tissue and can noninvasively characterize microstructural properties of the brain. In tuberous sclerosis complex, DTI measures reflect well-known pathological changes. Clinically, DTI can assist with detecting the epileptogenic tuber. For research, DTI has a putative role in identifying potential disease biomarkers, as DTI abnormalities of the white matter are associated with neurocognitive morbidity including autism. If indeed DTI changes parallel phenotypical changes related to the investigational treatment of epilepsy, cognition and behavior with mTOR inhibitors, it will facilitate future clinical trials.

Reliable selection of the number of fascicles in diffusion images by estimation of the generalization error

A number of diffusion models have been proposed to overcome the limitations of diffusion tensor imaging (DTI) which cannot represent multiple fascicles with heterogeneous orientations at each voxel. Among them, generative models such as multi-tensor models, CHARMED or NODDI represent each fascicle with a parametric model and are of great interest to characterize and compare white matter properties. However, the identification of the appropriate model, and particularly the estimation of the number of fascicles, has proven challenging. In this context, different model selection approaches have been proposed to identify the number of fascicles at each voxel. Most approaches attempt to maximize the quality of fit while penalizing complex models to avoid overfitting. However, the choice of a penalization strategy and the trade-off between penalization and quality of fit are rather arbitrary and produce highly variable results. In this paper, we propose for the first time to determine the number of fascicles at each voxel by assessing the generalization error. This criterion naturally prevents overfitting by comparing how the models predict new data not included in the model estimation. Since the generalization error cannot be directly computed, we propose to estimate it by the 632 bootstrap technique which has low bias and low variance. Results on synthetic phantoms and in vivo data show that our approach performs better than existing techniques, and is robust to the choice of decision threshold. Together with generative models of the diffusion signal, this technique will enable accurate identification of the model complexity at each voxel and accurate assessment of the white matter characteristics.

Estimation of a multi-fascicle model from single B-value data with a population-informed prior

Diffusion tensor imaging cannot represent heterogeneous fascicle orientations in one voxel. Various models propose to overcome this limitation. Among them, multi-fascicle models are of great interest to characterize and compare white matter properties. However, existing methods fail to estimate their parameters from conventional diffusion sequences with the desired accuracy. In this paper, we provide a geometric explanation to this problem. We demonstrate that there is a manifold of indistinguishable multi-fascicle models for single-shell data, and that the manifolds for different b-values intersect tangentially at the true underlying model making the estimation very sensitive to noise. To regularize it, we propose to learn a prior over the model parameters from data acquired at several b-values in an external population of subjects. We show that this population-informed prior enables for the first time accurate estimation of multi-fascicle models from single-shell data as commonly acquired in clinical context. The approach is validated on synthetic and in vivo data of healthy subjects and patients with autism. We apply it in population studies of the white matter microstructure in autism spectrum disorder. This approach enables novel investigations from large existing DWI datasets in normal development and in disease.

Characterizing the distribution of anisotropic micro-structural environments with diffusion-weighted imaging (DIAMOND)

Diffusion-weighted imaging (DWI) enables investigation of the brain microstructure by probing natural barriers to diffusion in tissues. In this work, we propose a novel generative model of the DW signal based on considerations of the tissue microstructure that gives rise to the diffusion attenuation. We consider that the DW signal can be described as the sum of a large number of individual homogeneous spin packets, each of them undergoing local 3-D Gaussian diffusion represented by a diffusion tensor. We consider that each voxel contains a number of large scale microstructural environments and describe each of them via a matrix-variate Gamma distribution of spin packets. Our novel model of DIstribution of Anisotropic MicrOstructural eNvironments in DWI (DIAMOND) is derived from first principles. It enables characterization of the extra-cellular space, of each individual white matter fascicle in each voxel and provides a novel measure of the microstructure heterogeneity. We determine the number of fascicles at each voxel with a novel model selection framework based upon the minimization of the generalization error. We evaluate our approach with numerous in-vivo experiments, with cross-testing and with pathological DW-MRI. We show that DIAMOND may provide novel biomarkers that captures the tissue integrity.