Temporal trajectories of normal myelination and axonal development assessed by quantitative macromolecular and diffusion MRI: Ultrastructural and immunochemical validation in a rabbit model.

INTRODUCTION: Quantitative and non-invasive measures of brain myelination and maturation during development are of great importance to both clinical and translational research communities. While the metrics derived from diffusion tensor imaging, are sensitive to developmental changes and some pathologies, they remain difficult to relate to the actual microstructure of the brain tissue. The advent of advanced model-based microstructural metrics requires histological validation. The purpose of the study was to validate novel, model-based MRI techniques, such as macromolecular proton fraction mapping (MPF) and neurite orientation and dispersion indexing (NODDI), against histologically derived indexes of myelination and microstructural maturation at various stages of development. METHODS: New Zealand White rabbit kits underwent serial in-vivo MRI examination at postnatal days 1, 5, 11, 18, and 25, and as adults. Multi-shell, diffusion-weighted experiments were processed to fit NODDI model to obtain estimates, intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Macromolecular proton fraction (MPF) maps were obtained from three source (MT-, PD-, and T1-weighted) images. After MRI sessions, a subset of animals was euthanized and regional samples of gray and white matter were taken for western blot analysis, to determine myelin basic protein (MBP), and electron microscopy, to estimate axonal, myelin fractions and g-ratio. RESULTS: MPF of white matter regions showed a period of fast growth between P5 and P11 in the internal capsule, with a later onset in the corpus callosum. This MPF trajectory was in agreement with levels of myelination in the corresponding brain region, as assessed by western blot and electron microscopy. In the cortex, the greatest increase of MPF occurred between P18 and P26. In contrast, myelin, according to MBP western blot, saw the largest hike between P5 and P11 in the sensorimotor cortex and between P11 and P18 in the frontal cortex, which then seemingly plateaued after P11 and P18 respectively. G-ratio by MRI markers decreased with age in the white matter. However, electron microscopy suggest a relatively stable g-ratio throughout development. CONCLUSION: Developmental trajectories of MPF accurately reflected regional differences of myelination rate in different cortical regions and white matter tracts. MRI-derived estimation of g-ratio was inaccurate during early development, likely due to the overestimation of axonal volume fraction by NODDI due to the presence of a large proportion of unmyelinated axons.

Widespread intra-axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment.

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole-brain voxel-based multicompartment diffusion MRI study on 316 participants (i.e., 158 patients and 158 matched healthy controls) employing four diffusion metrics: the mean diffusivity (MD) and FA estimated from DTI, and the intra-axonal signal fraction (IASF) and microscopic axonal parallel diffusivity (Dpar) derived from the spherical mean technique. Our findings provide novel evidence about widespread abnormalities in other diffusion metrics in BD. An extensive overlap between the FA and IASF results suggests that the lower FA in patients may be caused by a reduced intra-axonal volume fraction or a higher macromolecular content in the intra-axonal water. We also found a diffuse alteration in MD involving white and grey matter tissue and more localised changes in Dpar. A Machine Learning analysis revealed that FA, followed by IASF, were the most helpful metric for the automatic diagnosis of BD patients, reaching an accuracy of 72%. Number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium, antipsychotics, antidepressants, and antiepileptics were all significantly associated with microstructure abnormalities. Lithium treatment was associated with less microstructure abnormality.

Microscopic theory of spin-spin and spin-lattice relaxation of bound protons in cellular and myelin membranes-A lateral diffusion model (LDM).

PURPOSE: Deciphering salient features of biological tissue cellular microstructure in health and diseases is an ultimate goal of MRI. While most MRI approaches are based on studying MR properties of tissue "free" water indirectly affected by tissue microstructure, other approaches, such as magnetization transfer (MT), directly target signals from tissue-forming macromolecules. However, despite three-decades of successful applications, relationships between MT measurements and tissue microstructure remain elusive, hampering interpretation of experimental results. The goal of this paper is to develop microscopic theory connecting the structure of cellular and myelin membranes to their MR properties. THEORY AND METHODS: Herein we introduce a lateral diffusion model (LDM) that explains the T2 (spin-spin) and T1 (spin-lattice) MRI relaxation properties of the macromolecular-bound protons by their dipole-dipole interaction modulated by the lateral diffusion of long lipid molecules forming cellular and myelin membranes. RESULTS: LDM predicts anisotropic T1 and T2 relaxation of membrane-bound protons. Moreover, their T2 relaxation cannot be described in terms of a standard R2  = 1/T2 relaxation rate parameter, but rather by a relaxation rate function R2 (t) that depends on time t after RF excitation, having, in the main approximation, a logarithmic behavior: R2 (t) ∼ lnt. This anisotropic non-linear relaxation leads to an absorption lineshape that is different from Super-Lorentzian traditionally used in interpreting MT experiments. CONCLUSION: LDM-derived analytical equations connect the membrane-bound protons T1 and T2 relaxation with dynamic distances between protons in neighboring membrane-forming lipid molecules and their lateral diffusion. This sheds new light on relationships between MT parameters and microstructure of cellular and myelin membranes.

Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties.

The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metrics of quantitative gradient-recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B0 ) magnetic field inhomogeneities directly from the gradient-recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel-by-voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B0 -inhomogeneities contributions to multigradient-echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with low signal-to-noise ratio (SNR ~ 50-100), where DANSE-generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time-dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.

Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact.

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition.

Multiple studies have reported a significant dependence of the effective transverse relaxation rate constant (R2*) and the phase of gradient-echo based (GRE) signal on the orientation of white matter fibres in the human brain. It has also been hypothesized that magnetic susceptibility, as obtained by single-orientation quantitative susceptibility mapping (QSM), exhibits such a dependence. In this study, we investigated this hypothesized relationship in a cohort of healthy volunteers. We show that R2* follows the predicted orientation dependence consistently across white matter regions, whereas the apparent magnetic susceptibility is related differently to fibre orientation across the brain and often in a complex non-monotonic manner. In addition, we explored the effect of fractional anisotropy measured by diffusion-weighted MRI on the strength of the orientation dependence and observed only a limited influence in many regions. However, with careful consideration of such an impact and the limitations imposed by the ill-posed nature of the dipole inversion process, it is possible to study magnetic susceptibility anisotropy in specific brain regions with a single orientation acquisition.

Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome.

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design.

Axon diameter mapping using diffusion MRI in the living human brain has attracted growing interests with the increasing availability of high gradient strength MRI systems. A systematic assessment of the consistency of axon diameter estimates within and between individuals is needed to gain a comprehensive understanding of how such methods extend to quantifying differences in axon diameter index between groups and facilitate the design of neurobiological studies using such measures. We examined the scan-rescan repeatability of axon diameter index estimation based on the spherical mean technique (SMT) approach using diffusion MRI data acquired with gradient strengths up to 300 mT/m on a 3T Connectom system in 7 healthy volunteers. We performed statistical power analyses using data acquired with the same protocol in a larger cohort consisting of 15 healthy adults to investigate the implications for study design. Results revealed a high degree of repeatability in voxel-wise restricted volume fraction estimates and tract-wise estimates of axon diameter index derived from high-gradient diffusion MRI data. On the region of interest (ROI) level, across white matter tracts in the whole brain, the Pearson's correlation coefficient of the axon diameter index estimated between scan and rescan experiments was r = 0.72 with an absolute deviation of 0.18 µm. For an anticipated 10% effect size in studies of axon diameter index, most white matter regions required a sample size of less than 15 people to observe a measurable difference between groups using an ROI-based approach. To facilitate the use of high-gradient strength diffusion MRI data for neuroscientific studies of axonal microstructure, the comprehensive multi-gradient strength, multi-diffusion time data used in this work will be made publicly available, in support of open science and increasing the accessibility of such data to the greater scientific community.

Diffusion tensor imaging reveals neuronal microstructural changes in myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients suffer from a variety of physical and neurological complaints indicating the central nervous system plays a role in ME/CFS pathophysiology. Diffusion tensor imaging (DTI) has been used to study microstructural changes in neurodegenerative diseases. In this study, we evaluated DTI parameters to investigate microstructural abnormalities in ME/CFS patients. We estimated DTI parameters in 25 ME/CFS patients who met Fukuda criteria (ME/CFSFukuda ), 18 ME/CFS patients who met International Consensus Criteria (ICC) (ME/CFSICC ) only and 26 healthy control (HC) subjects. In addition to voxel-based DTI-parameter group comparisons, we performed voxel-based DTI-parameter interaction-with-group regressions with clinical and autonomic measures to test for abnormal regressions. Group comparisons between ME/CFSICC and HC detected significant clusters (a) with decreased axial diffusivity (p = .001) and mean diffusivity (p = .01) in the descending cortico-cerebellar tract in the midbrain and pons and (b) with increased transverse diffusivity in the medulla. The mode of anisotropy was significantly decreased (p = .001) in a cluster in the superior longitudinal fasciculus region. Voxel-based group comparisons between ME/CFSFukuda and HC did not detect significant clusters. For ME/CFSICC and HC, DTI parameter interaction-with-group regressions were abnormal for the clinical measures of information processing score, SF36 physical, sleep disturbance score and respiration rate in both grey and white matter regions. Our study demonstrated that DTI parameters are sensitive to microstructural changes in ME/CFSICC and could potentially act as an imaging biomarker of abnormal pathophysiology in ME/CFS. The study also shows that strict case definitions are essential in investigation of the pathophysiology of ME/CFS.

Lipid droplet-size mapping in human adipose tissue using a clinical 3T system.

PURPOSE: To develop a methodology for probing lipid droplet sizes with a clinical system based on a diffusion-weighted stimulated echo-prepared turbo spin-echo sequence and to validate the methodology in water-fat emulsions and show its applicability in ex vivo adipose-tissue samples. METHODS: A diffusion-weighted stimulated echo-prepared preparation was combined with a single-shot turbo spin-echo readout for measurements at different b-values and diffusion times. The droplet size was estimated with an analytical expression, and three fitting approaches were compared: magnitude-based spatial averaging with voxel-wise residual minimization, complex-based spatial averaging with voxel-wise residual minimization, and complex-based spatial averaging with neighborhood-regularized residual minimization. Simulations were performed to characterize the fitting residual landscape and the approaches' noise performance. The applicability was assessed in oil-in-water emulsions in comparison with laser deflection and in ten human white adipose tissue samples in comparison with histology. RESULTS: The fitting residual landscape showed a minimum valley with increasing extent as the droplet size increased. In phantoms, a very good agreement of the mean droplet size was observed between the diffusion-weighted MRI-based and the laser deflection measurements, showing the best performance with complex-based spatial averaging with neighborhood-regularized residual minimization processing (R2 /P: 0.971/0.014). In the human adipose-tissue samples, complex-based spatial averaging with neighborhood-regularized residual minimization processing showed a significant correlation (R2 /P: 0.531/0.017) compared with histology. CONCLUSION: The proposed acquisition and parameter-estimation methodology was able to probe restricted diffusion effects in lipid droplets. The methodology was validated using phantoms, and its feasibility in measuring an apparent lipid droplet size was demonstrated ex vivo in white adipose tissue.

Probing in vivo cortical myeloarchitecture in humans via line-scan diffusion acquisitions at 7 T with 250-500 micron radial resolution.

PURPOSE: The goal of this study was to measure diffusion signals within the cerebral cortex using the line-scan technique to achieve extremely high resolution in the radial direction (ie, perpendicular to the cortical surface) and to demonstrate the utility of these measurements for investigating laminar architecture in the living human brain. METHODS: Line-scan diffusion data with 250-500 micron radial resolution were acquired at 7 T on 8 healthy volunteers, with each line prescribed perpendicularly to primary somatosensory cortex (S1) and primary motor cortex (M1). Apparent diffusion coefficients, fractional anisotropy values, and radiality indices were measured as a function of cortical depth. RESULTS: In the deep layers of S1, we found evidence for high anisotropy and predominantly tangential diffusion, with low anisotropy observed in superficial S1. In M1, moderate anisotropy and predominantly radial diffusion was seen at almost all cortical depths. These patterns were consistent across subjects and were conspicuous without averaging data across different locations on the cortical sheet. CONCLUSION: Our results are in accord with the myeloarchitecture of S1 and M1, known from prior histology studies: in S1, dense bands of tangential myelinated fibers run through the deep layers but not the superficial ones, and in M1, radial myelinated fibers are prominent at most cortical depths. This work therefore provides support for the idea that high-resolution diffusion signals, measured with the line-scan technique and receiving a boost in SNR at 7 T, may serve as a sensitive probe of in vivo laminar architecture.

Radial trend in murine annulus fibrosus fiber orientation is best explained by vertebral growth.

PURPOSE: The intervertebral disc (IVD) annulus fibrosus (AF) is composed of concentric lamellae with alternating right- and left-handed helically oriented collagen fiber bundles. This arrangement results in anisotropic material properties, which depend on local fiber orientations. Prior measurements of fiber inclination angles in human lumbar and bovine caudal IVDs found a significantly higher inclination angle in the inner AF than outer, though it is currently unknown if this pattern is conserved in smaller mammalian species. Additionally, the physical mechanism behind this pattern remains un-determined. METHODS: In this study, AF fiber angles were measured histologically in murine caudal IVDs and compared to previously published values from bovine caudal IVDs. Fiber angles were also predicted using three theoretical models, including two based on adaptation to internal swelling pressure and one based on vertebral body growth. RESULTS: Fiber angle was found to significantly decrease from 49.5 ± 3.8° in the inner AF to 34.5 ± 6.6° in the outer AF. While steeper than in bovine discs at all locations, the trend with radial position was comparable between species. This trend was best fit by growth-based model and opposite of that predicted by the pressure vessel models. CONCLUSION: Trends in AF fiber orientation are conserved between mammalian species. Modeling results suggest that the AF tissue microstructure is more likely to be driven by adjacent vertebral body growth than adapted for optimal mechanical performance.

[Brain tissue microstructure parameters estimation method based on proximal gradient network].

Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.

Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI.

Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.

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.

A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal.

To date, numerical simulations of the brain tissue have been limited by their lack of realism and flexibility. The purpose of this work is to propose a controlled and flexible generative model for brain cell morphology and an efficient computational pipeline for the reliable and robust simulation of realistic cellular structures with application to numerical simulation of intra-cellular diffusion-weighted MR (DW-MR) signal features. Inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables users to simulate molecular diffusion within realistic digital brain cells, such as neurons, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology (no statistically different 3D Sholl metrics, P > 0.05) and simulated intra-cellular DW-MR signal (mean relative difference < 2%) of the generated digital model of brain cells and those of digital reconstruction of real brain cells from available open-access databases. We demonstrate the versatility and potential of the framework by showing a select set of examples of relevance for the DW-MR community. The computational models introduced here are useful for synthesizing intra-cellular DW-MR signals, similar to those one might measure from brain metabolites DW-MRS experiments. They also provide the foundation for a more complete simulation system that will potentially include signals from extra-cellular compartments and exchange processes, necessary for synthesizing DW-MR signals of relevance for DW-MRI experiments.

Age-related alterations in axonal microstructure in the corpus callosum measured by high-gradient diffusion MRI.

Cerebral white matter exhibits age-related degenerative changes during the course of normal aging, including decreases in axon density and alterations in axonal structure. Noninvasive approaches to measure these microstructural alterations throughout the lifespan would be invaluable for understanding the substrate and regional variability of age-related white matter degeneration. Recent advances in diffusion magnetic resonance imaging (MRI) have leveraged high gradient strengths to increase sensitivity toward axonal size and density in the living human brain. Here, we examined the relationship between age and indices of axon diameter and packing density using high-gradient strength diffusion MRI in 36 healthy adults (aged 22-72) in well-defined central white matter tracts in the brain. A recently validated method for inferring the effective axonal compartment size and packing density from diffusion MRI measurements acquired with 300 mT/m maximum gradient strength was applied to the in vivo human brain to obtain indices of axon diameter and density in the corpus callosum, its sub-regions, and adjacent anterior and posterior fibers in the forceps minor and forceps major. The relationships between the axonal metrics, corpus callosum area and regional gray matter volume were also explored. Results revealed a significant increase in axon diameter index with advancing age in the whole corpus callosum. Similar analyses in sub-regions of the corpus callosum showed that age-related alterations in axon diameter index and axon density were most pronounced in the genu of the corpus callosum and relatively absent in the splenium, in keeping with findings from previous histological studies. The significance of these correlations was mirrored in the forceps minor and forceps major, consistent with previously reported decreases in FA in the forceps minor but not in the forceps major with age. Alterations in the axonal imaging metrics paralleled decreases in corpus callosum area and regional gray matter volume with age. Among older adults, results from cognitive testing suggested an association between larger effective compartment size in the corpus callosum, particularly within the genu of the corpus callosum, and lower scores on the Montreal Cognitive Assessment, largely driven by deficits in short-term memory. The current study suggests that high-gradient diffusion MRI may be sensitive to the axonal substrate of age-related white matter degeneration reflected in traditional DTI metrics and provides further evidence for regionally selective alterations in white matter microstructure with advancing age.

Estimating axon conduction velocity in vivo from microstructural MRI.

The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4µm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.

Measuring non-parametric distributions of intravoxel mean diffusivities using a clinical MRI scanner.

We measure spectra of water mobilities (i.e., mean diffusivities) from intravoxel pools in brain tissues of healthy subjects with a non-parametric approach. Using a single-shot isotropic diffusion encoding (IDE) preparation, we eliminate signal confounds caused by anisotropic diffusion, including microscopic anisotropy, and acquire in vivo diffusion-weighted images (DWIs) over a wide range of diffusion sensitizations. We analyze the measured IDE signal decays using a regularized inverse laplace transform (ILT) to derive a probability distribution of mean diffusivities of tissue water in each voxel. Based on numerical simulations we assess the sensitivity and accuracy of our ILT analysis and optimize an experimental protocol for use with clinical MRI scanners. In vivo spectra of intravoxel mean diffusivities measured in healthy subjects generally show single-peak distributions throughout the brain parenchyma, with small differences in peak location and shape among white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Mean diffusivity distributions (MDDs) with multiple peaks are observed primarily in voxels at tissue interfaces and are likely due to partial volume contributions. To quantify tissue-specific MDDs with improved statistical power, we average voxel-wise normalized MDDs in corresponding regions-of-interest (ROIs). This non-parametric, rotation-invariant assessment of isotropic diffusivities of tissue water may reflect important microstructural information, such as cell packing and cell size, and active physiological processes, such as water transport and exchange, which may enhance biological specificity in the clinical diagnosis and characterization of ischemic stroke, cancer, neuroinflammation, and neurodegenerative disorders and diseases.

Measuring large lipid droplet sizes by probing restricted lipid diffusion effects with diffusion-weighted MRS at 3T.

PURPOSE: The in vivo probing of restricted diffusion effects in large lipid droplets on a clinical MR scanner remains a major challenge due to the need for high b-values and long diffusion times. This work proposes a methodology to probe mean lipid droplet sizes using diffusion-weighted MRS (DW-MRS) at 3T. METHODS: An analytical expression for restricted diffusion was used. Simulations were performed to evaluate the noise performance and the influence of particle size distribution. To validate the method, oil-in-water emulsions were prepared and examined using DW-MRS, laser deflection and light microscopy. The tibia bone marrow was scanned in volunteers to test the method repeatability and characterize microstructural differences at different locations. RESULTS: The simulations showed accurate and precise droplet size estimation when a sufficient SNR is reached with minor dependence on the size distribution. In phantoms, a good correlation between the measured droplet sizes by DW-MRS and by laser deflection (R2 = 0.98; P = 0.01) and microscopy (R2 = 0.99; P < 0.01) measurements was obtained. A mean coefficient of variation of 11.5 % was found for the lipid droplet diameter in vivo. The average diameter was smaller at a proximal (50.1 ± 7.3 µm) compared with a distal tibia location (61.1 ± 6.8 µm) (P < 0.01). CONCLUSION: The presented methods were able to probe restricted diffusion effects in lipid droplets using DW-MRS and to estimate lipid droplet size. The methodology was validated using phantoms and the in vivo feasibility in bone marrow was shown based on a good repeatability and findings in agreement with literature.