Explore the vast range of titles available.

There is a need for accurate quantitative non-invasive biomarkers to monitor myelin pathology in vivo and distinguish myelin changes from other pathological features including inflammation and axonal loss. Conventional MRI metrics such as T2, magnetization transfer ratio and radial diffusivity have proven sensitivity but not specificity. In highly coherent white matter bundles, compartment-specific white matter tract integrity (WMTI) metrics can be directly derived from the diffusion and kurtosis tensors: axonal water fraction, intra-axonal diffusivity, and extra-axonal radial and axial diffusivities. We evaluate the potential of WMTI to quantify demyelination by monitoring the effects of both acute (6weeks) and chronic (12weeks) cuprizone intoxication and subsequent recovery in the mouse corpus callosum, and compare its performance with that of conventional metrics (T2, magnetization transfer, and DTI parameters). The changes observed in vivo correlated with those obtained from quantitative electron microscopy image analysis. A 6-week intoxication produced a significant decrease in axonal water fraction (p<0.001), with only mild changes in extra-axonal radial diffusivity, consistent with patchy demyelination, while a 12-week intoxication caused a more marked decrease in extra-axonal radial diffusivity (p=0.0135), consistent with more severe demyelination and clearance of the extra-axonal space. Results thus revealed increased specificity of the axonal water fraction and extra-axonal radial diffusivity parameters to different degrees and patterns of demyelination. The specificities of these parameters were corroborated by their respective correlations with microstructural features: the axonal water fraction correlated significantly with the electron microscopy derived total axonal water fraction (ρ=0.66; p=0.0014) but not with the g-ratio, while the extra-axonal radial diffusivity correlated with the g-ratio (ρ=0.48; p=0.0342) but not with the electron microscopy derived axonal water fraction. These parameters represent promising candidates as clinically feasible biomarkers of demyelination and remyelination in the white matter. •We monitored de- and remyelination with compartment-specific diffusion MRI metrics.•Axonal water fraction decreased with patchy demyelination (6weeks of cuprizone).•Extra-axonal radial diffusivity increased with widespread demyelination (12weeks).•Correlations with electron microscopy supported each metric's specificity.•These metrics are promising candidates as clinical biomarkers of demyelination.

The myelin g-ratio, defined as the ratio between the inner and the outer diameter of the myelin sheath, is a fundamental property of white matter that can be computed from a simple formula relating the myelin volume fraction to the fiber volume fraction or the axon volume fraction. In this paper, a unique combination of magnetization transfer, diffusion imaging and histology is presented, providing a novel method for in vivo magnetic resonance imaging of the axon volume fraction and the myelin g-ratio. Our method was demonstrated in the corpus callosum of one cynomolgus macaque, and applied to obtain full-brain g-ratio maps in one healthy human subject and one multiple sclerosis patient. In the macaque, the g-ratio was relatively constant across the corpus callosum, as measured by both MRI and electron microscopy. In the human subjects, the g-ratio in multiple sclerosis lesions was higher than in normal appearing white matter, which was in turn higher than in healthy white matter. Measuring the g-ratio brings us one step closer to fully characterizing white matter non-invasively, making it possible to perform in vivo histology of the human brain during development, aging, disease and treatment. [Display omitted] •A multi-modal imaging technique for whole brain myelin g-ratio mapping in vivo is presented.•The g-ratio framework decouples the relative myelin thickness from the fiber volume fraction.•The g-ratio is computed in the macaque using MRI and histology.•The potential of g-ratio mapping for understanding disease is demonstrated in a multiple sclerosis patient.

Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~ 17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the “apparent” inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.

The noninvasive measurement of the mechanical properties of brain tissue using magnetic resonance elastography (MRE) has emerged as a promising method for investigating neurological disorders. To date, brain MRE investigations have been limited to reporting global mechanical properties, though quantification of the stiffness of specific structures in the white matter architecture may be valuable in assessing the localized effects of disease. This paper reports the mechanical properties of the corpus callosum and corona radiata measured in healthy volunteers using MRE and atlas-based segmentation. Both structures were found to be significantly stiffer than overall white matter, with the corpus callosum exhibiting greater stiffness and less viscous damping than the corona radiata. Reliability of both local and global measures was assessed through repeated experiments, and the coefficient of variation for each measure was less than 10%. Mechanical properties within the corpus callosum and corona radiata demonstrated correlations with measures from diffusion tensor imaging pertaining to axonal microstructure. •MR elastography generates spatially resolved mechanical property maps of the brain.•Mechanical properties of human corpus callosum and corona radiata are quantified.•Structures are mechanically distinct and stiffer than white matter in general.•Strong correlations exist between MRE and DTI properties within each structure.

The recovery of microstructure-related features of the brain's white matter is a current challenge in diffusion MRI. To robustly estimate these important features from multi-shell diffusion MRI data, we propose to analytically regularize the coefficient estimation of the Mean Apparent Propagator (MAP)-MRI method using the norm of the Laplacian of the reconstructed signal. We first compare our approach, which we call MAPL, with competing, state-of-the-art functional basis approaches. We show that it outperforms the original MAP-MRI implementation and the recently proposed modified Spherical Polar Fourier (mSPF) basis with respect to signal fitting and reconstruction of the Ensemble Average Propagator (EAP) and Orientation Distribution Function (ODF) in noisy, sparsely sampled data of a physical phantom with reference gold standard data. Then, to reduce the variance of parameter estimation using multi-compartment tissue models, we propose to use MAPL's signal fitting and extrapolation as a preprocessing step. We study the effect of MAPL on the estimation of axon diameter using a simplified Axcaliber model and axonal dispersion using the Neurite Orientation Dispersion and Density Imaging (NODDI) model. We show the positive effect of using it as a preprocessing step in estimating and reducing the variances of these parameters in the Corpus Callosum of six different subjects of the MGH Human Connectome Project. Finally, we correlate the estimated axon diameter, dispersion and restricted volume fractions with Fractional Anisotropy (FA) and clearly show that changes in FA significantly correlate with changes in all estimated parameters. Overall, we illustrate the potential of using a well-regularized functional basis together with multi-compartment approaches to recover important microstructure tissue parameters with much less variability, thus contributing to the challenge of better understanding microstructure-related features of the brain's white matter. [Display omitted] •We propose MAPL: Fast, analytic Laplacian regularization of the MAP-MRI basis.•Using MAPL we estimate microstructural indices with fewer samples and lower b-values.•MAPL improves signal and ODF reconstruction w.r.t. to state-of-the-art (MAP-MRI/mSPF).•Using MAPL as preprocessing improves NODDI/Axcaliber microstructure estimation.•MAPL preprocessing reduces variability in axonal diameter and dispersion estimation.

Very preterm-born infants are at risk of adverse neurodevelopmental outcomes. Brain magnetic resonance imaging (MRI) at term equivalent age (TEA) can probe tissue microstructure and morphology, and demonstrates potential in the early prediction of outcomes. In this study, we use the recently introduced fixel-based analysis method for diffusion MRI to investigate the association between microstructure and morphology at TEA, and motor and cognitive development at 1 and 2 years corrected age (CA). Eighty infants born <31 weeks’ gestation successfully underwent diffusion MRI (3T; 64 directions; b ​= ​2000s/mm2) at term equivalent age, and had neurodevelopmental follow-up using the Bayley-III motor and cognitive assessments at 1 year (n ​= ​78) and/or 2 years (n ​= ​76) CA. Diffusion MRI data were processed using constrained spherical deconvolution (CSD) and aligned to a study-specific fibre orientation distribution template, yielding measures of fibre density (FD), fibre-bundle cross-section (FC), and fibre density and bundle cross-section (FDC). The association between FD, FC, and FDC at TEA, and motor and cognitive composite scores at 1 and 2 years CA, and change in composite scores from 1 to 2 years, was assessed using whole-brain fixel-based analysis. Additionally, the association between diffusion tensor imaging (DTI) metrics (fractional anisotropy FA, mean diffusivity MD, axial diffusivity AD, radial diffusivity RD) and outcomes was investigated. Motor function at 1 and 2 years CA was associated with CSD-based measures of the bilateral corticospinal tracts and corpus callosum. Cognitive function was associated with CSD-based measures of the midbody (1-year outcomes only) and splenium of the corpus callosum, as well as the bilateral corticospinal tracts. The change in motor/cognitive outcomes from 1 to 2 years was associated with CSD-based measures of the splenium of the corpus callosum. Analysis of DTI-based measures showed overall less extensive associations. Post-hoc analysis showed that associations were weaker for 2-year outcomes than they were for 1-year outcomes. Infants with better neurodevelopmental outcomes demonstrated higher FD, FC, and FDC at TEA, indicating better information transfer capacity which may be related to increased number of neurons, increased myelination, thicker bundles, and/or combinations thereof. The fibre bundles identified here may serve as the basis for future studies investigating the predictive ability of these metrics. •Brain measures at term are associated with outcomes at 1 and 2 years.•Infants with higher FD, FC and FDC at term perform better on Bayley-III.•DTI measures (FA, MD, AD, RD) show limited associations with outcomes.•Associations are stronger for 1-year outcomes than for 2-year outcomes.

The size and complexity of the human brain require optimally sized and myelinated fibers. White matter fibers facilitate fast communication between distant areas, but also connect adjacent cortical regions via short association fibers. The difference in length and packing density of long and short association fibers pose different requirements on their optimal size and degree of myelination. The fundamental questions of (i) how thick the short association fibers are and (ii) how strongly they are myelinated as compared to long fibers, however, remain unanswered. We present a comprehensive two-dimensional transmission electron microscopic analysis of ~400,000 fibers of human white matter regions with long (corpus callosum) and short fibers (superficial white matter). Using a deep learning approach, we demonstrate a substantially higher fiber diameter and higher myelination thickness (both approximately 25% higher) in corpus callosum than in superficial white matter. Surprisingly, we do not find a difference in the ratio between axon diameter and myelin thickness (g-ratio), which is close to the theoretically optimal value of ~0.6 in both areas (0.54). This work reveals a fundamental principle of brain organization that provides a key foundation for understanding the human brain.

The intra-axonal water exchange time (τi), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R2 =0.99, τi: R2 =0.84, intrinsic diffusivity d: R2 =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τi). Finally, we find a statistically significant decrease in τi in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τi>=310ms/330ms/350ms) compared to the WT group (<τi>=370ms/370ms/380ms). This is in line with our expectations that τi is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .

Mild traumatic brain injury (mTBI) is an increasing public health concern as repetitive injuries can exacerbate existing neuropathology and result in increased neurologic deficits. In contrast to other models of repeated mTBI (rmTBI), our study focused on long-term white-matter abnormalities after bilateral mTBIs induced 7 days apart. A controlled cortical impact (CCI) was used to induce an initial mTBI to the right cortex of Single and rmTBI Sprague Dawley rats, followed by a second injury to the left cortex of rmTBI animals. Shams received only a craniectomy. Ex vivo diffusion tensor imaging (DTI), transmission electron microscopy (TEM), and histology were performed on the anterior corpus callosum at 60 days after injury. The rmTBI animals showed a significant bilateral increase in radial diffusivity (myelin), while only modest changes in axial diffusivity (axonal) were seen between the groups. Further, the rmTBI group showed an increased g-ratio and axon caliber in addition to myelin sheath abnormalities using TEM. Our DTI results indicate ongoing myelin changes, while the TEM data show continuing axonal changes at 60 days after rmTBI. These data suggest that bilateral rmTBI induced 7 days apart leads to progressive alterations in white matter that are not observed after a single mTBI.

MRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here, we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter and identify its origins by Monte Carlo simulations in realistic substrates from 3-dimensional electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the potential sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique. Hong-Hsi Lee et al. identify a characteristic power-law scaling in the time-dependent diffusion MRI signal that can detect micrometer-scale variations in axon caliber or pathological beading. They validate this signature trait using Monte Carlo simulations in axonal microstructure based on electron microscopy of mouse corpus callosum and illustrate the sensitivity of this method by detecting axon beading in a neurodegenerative disorder.