Intra-axonal Water Research Articles

Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI.

to evaluate microstructural changes in the corpus callosum (CC) using diffusion MRI (D-MRI) approaches in cSVD patients with different severity of CI and reveal the most sensitive correlations of diffusion metrics with CI. the study included 166 cSVD patients (51.8% women; 60.4 ± 7.6 years) and 44 healthy volunteers (65.9% women; 59.6 ± 6.8 years). All subjects underwent D-MRI (3T) with signal (diffusion tensor and kurtosis) and biophysical (neurite orientation dispersion and density imaging, NODDI, white matter tract integrity, WMTI, multicompartment spherical mean technique, MC-SMT) modeling in three CC segments as well as a neuropsychological assessment. in cSVD patients, microstructural changes were found in all CC segments already at the subjective CI stage, which was found to worsen into mild CI and dementia. More pronounced changes were observed in the forceps minor. Among the signal models FA, MD, MK, RD, and RK, as well as among the biophysical models, MC-SMT (EMD, ETR) and WMTI (AWF) metrics exhibited the largest area under the curve (>0.85), characterizing the loss of microstructural integrity, the severity of potential demyelination, and the proportion of intra-axonal water, respectively. Conclusion: the study reveals the relevance of advanced D-MRI approaches for the assessment of brain tissue changes in cSVD. The identified diffusion biomarkers could be used for the clarification and observation of CI progression.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

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.

Disruption of corpus callosum microstructural integrity by diffusion MRI as a predictor of progression of cerebral microangiopathy

To assess the microstructural integrity of the corpus callosum in patients with cerebral small vessel disease (cSVD) using signal and biophysical diffusion MRI models and to identify the most sensitive markers of disease progression. Diffusion MRI (3 Tesla) was performed in 166 patients (51.8% women; mean age 60.4±7.6) with cSVD and cognitive impairment of varying severity and in 44 healthy volunteers (65.9% women; mean age 59.6±6.8), followed by calculation of signal (diffusion tensor and diffusion kurtosis) and biophysical (WMTI, NODDI, MC-SMT) models, from which profiles of three corpus callosum segments were constructed. The best results were obtained for metrics in the forceps minor and body of the corpus callosum. Among the metrics of the signal models in the forceps minor, fraction anisotropy (FA) and mean diffusion (MD), which characterize the overall loss of microstructural integrity and increase in extra-axonal water, as well as indirect markers of demyelination when considering transverse diffusion parameters (radial diffusion and radial kurtosis), had the larger area under the curve according to the ROC analysis. Among the metrics of the biophysical models in the forceps minor, a larger area under the curve was found in the MC-SMT model for extra-axonal transverse diffusion (ETR), mean diffusion (EMD), and intra-axonal water fraction (INTRA), and in the WMTI model for intra-axonal water fraction (AWF). ETR had high inverse correlations with INTRA and AWF, while INTRA and AWF had high direct intercorrelations. Metrics of signaling (FA, MD, RD, RK) and biophysical patterns (ETR, EMD, INTRA, AWF) in the forceps minor and the corpus callosum body can be considered as indicators of cSVD progression. They indicate disease progression, mainly by an increase in extra-axonal water with the development of demyelination and tissue degeneration in the corpus callosum.

Deuterium double quantum-filtered NMR studies of peripheral and optic nerves.

Characterization of the nerve components by deuterium double quantum-filtered magnetization transfer (DQF-MT) NMR. Nerves were equilibrated in deuterated saline and 2H single-pulse and 2H DQF-MT NMR spectra were measured, enabling the separation of the different water compartments, according to their quadrupolar splittings. Rat sciatic and brachial nerves and porcine optic nerve immersed in deuterated saline yielded 2H DQF spectra composed of three pairs of quadrupolar-split signals assigned to the water in the collagenous compartments and the myelin bilayer and one narrow signal assigned to the axonal water. Stretching of the nerves, application of osmotic stress and incubation in collagenase did not affect the quadrupolar splitting of the myelin water. The signals of myelin and axonal water were shown to decay during Wallerian degeneration and to rise during maturation. The chemical exchange between the myelin and the intra-axonal water was measured for optic nerve during maturation. The quadrupolar splitting of the signal of myelin water was not sensitive to its orientation relative to the magnetic field. This resembles liquid crystalline behavior, but leaves its mechanism open for interpretation. 2H DQF-MT NMR characterizes the different components of nerves, the water exchange between them and their changes during processes such as nerve maturation and Wallerian degeneration.

Measuring compartmental [formula omitted]-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-[formula omitted] correlation MRI

The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhance the interpretation of MRI signal changes in development, ageing and disease and ultimately improve clinical diagnosis. Using a novel experimental setup, this work studies the contributions of the intra- and extra-axonal water to the orientation-dependence of one of the most clinically-studied parameters, apparent transverse relaxation T2. Specifically, a tiltable receive coil is interfaced with an ultra-strong gradient MRI scanner to acquire multidimensional MRI data with an unprecedented range of acquisition parameters. Using this setup, compartmental T2 can be disentangled based on differences in diffusional-anisotropy, and its orientation-dependence further elucidated by re-orienting the head with respect to the main magnetic field B→0. A dependence of (compartmental) T2 on the fibre orientation w.r.t. B→0 was observed, and further quantified using characteristic representations for susceptibility- and magic angle effects. Across white matter, anisotropy effects were dominated by the extra-axonal water signal, while the intra-axonal water signal decay varied less with fibre-orientation. Moreover, the results suggest that the stronger extra-axonal T2 orientation-dependence is dominated by magnetic susceptibility effects (presumably from the myelin sheath) while the weaker intra-axonal T2 orientation-dependence may be driven by a combination of microstructural effects. Even though the current design of the tiltable coil only offers a modest range of angles, the results demonstrate an overall effect of tilt and serve as a proof-of-concept motivating further hardware development to facilitate experiments that explore orientational anisotropy. These observations have the potential to lead to white matter microstructural models with increased compartmental sensitivity to disease, and can have direct consequences for longitudinal and group-wise T2- and diffusion-MRI data analysis, where the effect of head-orientation in the scanner is commonly ignored.

Multi-compartment analysis of the complex gradient-echo signal quantifies myelin breakdown in premanifest Huntington's disease

White matter (WM) alterations have been identified as a relevant pathological feature of Huntington’s disease (HD). Increasing evidence suggests that WM changes in this disorder are due to alterations in myelin‐associated biological processes. Multi-compartmental analysis of the complex gradient-echo MRI signal evolution in WM has been shown to quantify myelin in vivo, therefore pointing to the potential of this technique for the study of WM myelin changes in health and disease. This study first characterized the reproducibility of metrics derived from the complex multi-echo gradient-recalled echo (mGRE) signal across the corpus callosum in healthy participants, finding highest reproducibility in the posterior callosal segment. Subsequently, the same analysis pipeline was applied in this callosal region in a sample of premanifest HD patients (n = 19) and age, sex and education matched healthy controls (n = 21). In particular, we focused on two myelin-associated derivatives: i. the myelin water signal fraction (fm), a parameter dependent on myelin content; and ii. The difference in frequency between myelin and intra-axonal water pools (Δω), a parameter dependent on the ratio between the inner and the outer axonal radii. fm was found to be lower in HD patients (β = −0.13, p = 0.03), while Δω did not show a group effect. Performance in tests of working memory, executive function, social cognition and movement was also assessed, and a greater age-related decline in executive function was detected in HD patients (β = −0.06, p = 0.006), replicating previous evidence of executive dysfunction in HD. Finally, the correlation between fm, executive function, and proximity to disease onset was explored in patients, and a positive correlation between executive function and fm was detected (r = 0.542; p = 0.02). This study emphasises the potential of complex mGRE signal analysis for aiding understanding of HD pathogenesis and progression. Moreover, expanding on evidence from pathology and animal studies, it provides novel in vivo evidence supporting myelin breakdown as an early feature of HD.

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

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 .

Intra-axonal diffusivity in brain white matter

Biophysical modeling lies at the core of evaluating tissue cellular structure using diffusion-weighted MRI, albeit with shortcomings. The challenges lie not only in the complexity of the diffusion phenomenon, but also in the need to know the diffusion-specific properties of diverse cellular compartments in vivo. The likelihood function obtained from the commonly acquired Stejskal-Tanner diffusion-weighted MRI data is degenerate with different parameter constellations explaining the signal equally well, thereby hindering an unambiguous parameter estimation.The aim of this study is to measure the intra-axonal water diffusivity which is one of the central parameters of white matter models. Estimating intra-axonal diffusivity is complicated by (i) the presence of other compartments, and (ii) the orientation dispersion of axons. Our measurement involves an efficient signal suppression of water in extra-axonal space and all cellular processes oriented outside a narrow cone around the principal fiber direction. This is achieved using a planar water mobility filter that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. After the planar filter, the diffusivity of the remaining intra-axonal signal is measured using linear and spherical diffusion encoding.We find the average intra-axonal diffusivity D0=2.25±0.03μm2/ms for the timing of the applied gradients, which gives D0(∞)≈2.0μm2/ms when extrapolated to infinite diffusion time. The result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI.

Measuring intra-axonal T2 in white matter with direction-averaged diffusion MRI.

To demonstrate how the T2 relaxation time of intra-axonal water (T2a ) in white matter can be measured with direction-averaged diffusion MRI. For b-values larger than about 4000 s/mm2 , the direction-averaged diffusion MRI signal from white matter is dominated by the contribution from water within axons, which enables T2a to be estimated by acquiring data for multiple TE values and fitting a mono-exponential decay curve. If given a value of the intra-axonal diffusivity, an extension of the method allows the extra-axonal relaxation time (T2e ) to be calculated also. This approach was applied to estimate T2a in white matter for 3 healthy subjects at 3 T, as well as T2e for a selected set of assumed intra-axonal diffusivities. The estimated T2a values ranged from about 50 ms to 110 ms, with considerable variation among white matter regions. For white matter tracts with primarily collinear fibers, T2a was found to depend on the angle of the tract relative to the main magnetic field, which is consistent with T2a being affected by magnetic field inhomogeneities arising from spatial differences in magnetic susceptibility. The T2e values were significantly smaller than the T2a values across white matter regions for several plausible choices of the intra-axonal diffusivity. The relaxation time for intra-axonal water in white matter can be determined in a straightforward manner by measuring the direction-averaged diffusion MRI signal with a large b-value for multiple TEs. In healthy brain, T2a is greater than T2e and varies considerably with anatomical region.

Assessment of quantitative magnetic resonance imaging metrics in the brain through the use of a novel phantom

ABSTRACTObjective: Multisite and longitudinal neuroimaging studies are important in uncovering trajectories of recovery and neurodegeneration following traumatic brain injury (TBI) and concussion through the use of diffusion tensor imaging (DTI) and other imaging modalities. This study assessed differences in anisotropic diffusion measurement across four scanners using a human and a novel phantom developed in conjunction with the Chronic Effects of Neurotrauma Consortium.Method: Human scans provided measurement within biological tissue, and the novel physical phantom provided measures of anisotropic intra-tubular diffusion to serve as a model for intra-axonal water diffusion. Intra- and inter-scanner measurement variances were compared, and the impact on effect size was calculated.Results: Intra-scanner test–retest reliability estimates for fractional anisotropy (FA) demonstrated relative stability over testing intervals. The human tissue and phantom showed similar FA ranges, high linearity and large within-device effect sizes. However, inter-scanner measures of FA indicated substantial differences, some of which exceeded typical DTI effect sizes in mild TBI.Conclusion: The diffusion phantom may be used to better elucidate inter-scanner variability in DTI-based measurement and provides an opportunity to better calibrate results obtained from scanners used in multisite and longitudinal studies. Novel solutions are being evaluated to understand and potentially overcome these differences.

Intra- and extra-axonal axial diffusivities in the white matter: Which one is faster?

A two-compartment model of diffusion in white matter, which accounts for intra- and extra-axonal spaces, is associated with two plausible mathematical scenarios: either the intra-axonal axial diffusivity Da,‖ is higher than the extra-axonal De,‖ (Branch 1), or the opposite, i.e. Da,‖ < De,‖ (Branch 2). This duality calls for an independent validation of compartment axial diffusivities, to determine which of the two cases holds. The aim of the present study was to use an intracerebroventricular injection of a gadolinium-based contrast agent to selectively reduce the extracellular water signal in the rat brain, and compare diffusion metrics in the genu of the corpus callosum before and after gadolinium infusion. The diffusion metrics considered were diffusion and kurtosis tensor metrics, as well as compartment-specific estimates of the WMTI-Watson two-compartment model. A strong decrease in genu T1 and T2 relaxation times post-Gd was observed (p < 0.001), as well as an increase of 48% in radial kurtosis (p < 0.05), which implies that the relative fraction of extracellular water signal was selectively decreased. This was further supported by a significant increase in intra-axonal water fraction as estimated from the two-compartment model, for both branches (p < 0.01 for Branch 1, p < 0.05 for Branch 2). However, pre-Gd estimates of axon dispersion in Branch 1 agreed better with literature than those of Branch 2. Furthermore, comparison of post-Gd changes in diffusivity and dispersion between data and simulations further supported Branch 1 as the biologically plausible solution, i.e. Da,‖ > De,‖. This result is fully consistent with other recent measurements of compartment axial diffusivities that used entirely different approaches, such as diffusion tensor encoding.

Characterizing intra-axonal water diffusion with direction-averaged triple diffusion encoding MRI.

For large diffusion weightings, the direction-averaged diffusion MRI (dMRI) signal from white matter is typically dominated by the contribution of water confined to axons. This fact can be exploited to characterize intra-axonal diffusion properties, which may be valuable for interpreting the biophysical meaning of diffusion changes associated with pathology. However, using just the classic Stejskal-Tanner pulse sequence, it has proven challenging to obtain reliable estimates for both the intrinsic intra-axonal diffusivity and the intra-axonal water fraction. Here we propose to apply a modification of the Stejskal-Tanner sequence designed for achieving such estimates. The key feature of the sequence is the addition of a set of extra diffusion encoding gradients that are orthogonal to the direction of the primary gradients, which corresponds to a specific type of triple diffusion encoding (TDE) MRI sequence. Given direction-averaged dMRI data for this TDE sequence, it is shown how the intra-axonal diffusivity and the intra-axonal water fraction can be determined by applying simple, analytic formulae. The method is illustrated with numerical simulations, which suggest that it should be accurate for b-values of about 4000s/mm2 or higher.

Estimating the axon diameter from intra-axonal water diffusion with arbitrary gradient waveforms: Resolution limit in parallel and dispersed fibers

Event Abstract Back to Event Estimating the axon diameter from intra-axonal water diffusion with arbitrary gradient waveforms: Resolution limit in parallel and dispersed fibers Markus Nilsson1* 1 Lund University, Lund University Bioimaging Center, Sweden Estimation of axon diameter from intra-axonal water diffusion may provide a valuable tool for non-invasive studies of brain connectivity. Techniques for axon diameter estimation have been developed on NMR spectrometers, and the feasibility demonstrated on fixated nerve tissue samples. Recently, attempts have been made to translate such techniques to clinical MRI scanners. The amplitudes of the gradients are, however, lower at clinical MRI scanners. For this reason accurate estimation of axon diameters in the human brain becomes challenging. The purpose of this study was to develop a framework to estimate the resolution limit, which we define as the diameter below which axons of different sizes cannot be told apart. Since there is a growing interest in using diffusion encoding techniques that go beyong the single diffusion encoding (SDE) experiment, we present a generalized framework that can predict the resolution limit regardless of the gradient waveform. The framework can be applied to understand the time dependence of SDE, double diffusio encoding, as well as more complex waveforms. We employed the framework to optimize gradient waveforms to minimize the resolution limit. The results show that for parallel fibers, the SDE experiment is optimal. In the presence of orientation dispersion, however, multiple diffusion encodings were optimal. The resolution limit was found to be between 2 and 4 micrometers, depending on maximal gradient amplitude and the level of orientation dispersion. Keywords: Diffusion, MRI, dMRI, multidimensional, diffusion encoding Conference: New dimensions in diffusion encoding, Fjälkinge, Sweden, 11 Jan - 14 Jan, 2016. Presentation Type: Oral presentation Topic: New Dimensions in Diffusion Encoding Citation: Nilsson M (2016). Estimating the axon diameter from intra-axonal water diffusion with arbitrary gradient waveforms: Resolution limit in parallel and dispersed fibers. Front. Phys. Conference Abstract: New dimensions in diffusion encoding. doi: 10.3389/conf.FPHY.2016.01.00006 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Jul 2016; Published Online: 07 Jul 2016. * Correspondence: Dr. Markus Nilsson, Lund University, Lund University Bioimaging Center, Lund, 221 85, Sweden, markus.nilsson@med.lu.se Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Markus Nilsson Google Markus Nilsson Google Scholar Markus Nilsson PubMed Markus Nilsson Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI?

White matter microstructural changes during the first three years of healthy brain development are characterized using two different models developed for limited clinical diffusion data: White Matter Tract Integrity (WMTI) metrics from Diffusional Kurtosis Imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI). Both models reveal a non-linear increase in intra-axonal water fraction and in tortuosity of the extra-axonal space as a function of age, in the genu and splenium of the corpus callosum and the posterior limb of the internal capsule. The changes are consistent with expected behavior related to myelination and asynchrony of fiber development. The intra- and extracellular axial diffusivities as estimated with WMTI do not change appreciably in normal brain development. The quantitative differences in parameter estimates between models are examined and explained in the light of each model's assumptions and consequent biases, as highlighted in simulations. Finally, we discuss the feasibility of a model with fewer assumptions.

Investigating the capability to resolve complex white matter structures with high b-value diffusion magnetic resonance imaging on the MGH-USC Connectom scanner.

One of the major goals of the NIH Blueprint Human Connectome Project was to map and quantify the white matter connections in the brain using diffusion tractography. Given the prevalence of complex white matter structures, the capability of resolving local white matter geometries with multiple crossings in the diffusion magnetic resonance imaging (dMRI) data is critical. Increasing b-value has been suggested for delineation of the finer details of the orientation distribution function (ODF). Although increased gradient strength and duration increase sensitivity to highly restricted intra-axonal water, gradient strength limitations require longer echo times (TE) to accommodate the increased diffusion encoding times needed to achieve a higher b-value, exponentially lowering the signal-to-noise ratio of the acquisition. To mitigate this effect, the MGH-USC Connectom scanner was built with 300 mT/m gradients, which can significantly reduce the TE of high b-value diffusion imaging. Here we report comparisons performed across b-values based on q-ball ODF metrics to investigate whether high b-value diffusion imaging on the Connectom scanner can improve resolving complex white matter structures. The q-ball ODF features became sharper as the b-value increased, with increased power fraction in higher order spherical harmonic series of the ODF and increased peak heights relative to the overall size of the ODF. Crossing structures were detected in an increasingly larger fraction of white matter voxels and the spatial distribution of two-way and three-way crossing structures was largely consistent with known anatomy. Results indicate that dMRI with high diffusion encoding on the Connectom system is a promising tool to better characterize, and ultimately understand, the underlying structural organization and motifs in the human brain.

Age-related microstructural alterations in deep gray matter: In vivo evidence from diffusional kurtosis imaging

[1] to compare 46 AD patients with 94 age-matched healthy volunteers (Australian Imaging Biomarkers & Lifestyle study). AFD is proportional to the image intensity, which is related to the quantity of intra-axonal water, and is therefore sensitive to the number of axons within a voxel (density). To perform voxel-wise comparisons of AFD, images were non-linearly warped to a common template image. We incorporated morphology information by modulating the AFD in each voxel based on the change to a fibre bundle’s cross-sectional area (atrophy). Modulation ensures the AFD in template space encapsulates both sources of axonal loss (axon density as measured by the MRI signal, and volume loss due to atrophy). We investigated whole-brain group AFD differences with and without modulation using novel tractography-based statistics. Results: Significant AFD decreases in AD were observed in white matter bundles known to be involved in language and memory (Fig.1). As shown in columns 1 & 2, the group AFD difference is more extensive when morphology information is included via AFDmodulation. Since AFD differences are associated with a voxel and direction (shown by the direction-colour-encoded tractogram in Fig.1) population differences can be attributed to a specific fibre bundle in regions with crossing-fibres (e.g. Arcuate Fasciculus).The absence of differences in fibre density (column 1) within regions previously detected using FA suggests that FA decreases detected in AD are largely caused by atrophy-induced partial volume changes. Conclusions: By incorporating morphology information, AFD is potentially a more sensitive and interpretable biomarker of axon degeneration compared to existing diffusion MRI measures based on image intensity alone.[1] Raffelt et al.2012,59(4):3976-94.

NMR properties of human median nerve at 3 T: Proton density, T1, T2, and magnetization transfer

To measure the proton density (PD), the T1 and T2 relaxation time, and magnetization transfer (MT) effects in human median nerve at 3 T and to compare them with the corresponding values in muscle. Measurements of the T1 and T2 relaxation time were performed with an inversion recovery and a Carr-Purcell-Meiboom-Gill (CPMG) imaging sequence, respectively. The MT ratio was measured by acquiring two sets of 3D spoiled gradient-echo images, with and without a Gaussian saturation pulse. The median nerve T1 was 1410 +/- 70 msec. The T2 decay consisted of two components, with average T2 values of 26 +/- 2 msec and 96 +/- 3 msec and normalized amplitudes of 78 +/- 4% and 22 +/- 4%, respectively. The dominant component is likely to reflect myelin water and connective tissue, and the less abundant component originates possibly from intra-axonal water protons. The value of proton density of MRI-visible protons in median nerve was 81 +/- 3% that of muscle. The MT ratio in median nerve (40.3 +/- 2.0%) was smaller than in muscle (45.4 +/- 0.5%). MRI-relevant properties, such as PD, T1 and T2 relaxation time, and MT ratio were measured in human median nerve at 3 T and were in many respects similar to those of muscle.

New Perspectives on the Sources of White Matter DTI Signal

A minimalist numerical model of white matter is presented, the objective of which is to help provide a biological basis for improved diffusion tensor imaging (DTI) analysis. Water diffuses, relaxes, and exchanges in three compartments-intracellular, extracellular, and myelin sheath. Exchange between compartments is defined so as to depend on the diffusion coefficients and the compartment sizes. Based on the model, it is proposed that an additive "baseline tensor" that correlates with intraaxonal water volume be included in the computation. Anisotropy and tortuosity calculated from such analysis may correspond better to tract ultrastructure than if calculated without the baseline. According to the model, reduced extracellular volume causes increased baseline and reduced apparent diffusion. Depending on the pulse sequence, reduced permeability can cause an increase in both the baseline and apparent diffusion.

Normal white matter development from infancy to adulthood: Comparing diffusion tensor and high b value diffusion weighted MR images

To evaluate the sensitivity of high b value diffusion weight magnetic resonance imaging (DWI) in detecting normal white matter maturation, compare it to conventional diffusion tensor imaging (DTI), and to obtain normative quantitative data using this method. High b value DWI (b(max) = 6000 sec/mm(2)) using q-space analysis and conventional DTI (b = 1000 sec/mm(2)) were performed on 36 healthy subjects aged 4 months to 23 years. Fractional-anisotropy (FA), apparent-displacement, and apparent-probability values were measured in all slices and in six regions of interest (ROIs) of large fiber tracks. Values were correlated with each other and with age using regression analysis. FA, displacement, and probability indices from all slices were highly correlated with each other (r > 0.87, P < 0.0001) and with age (r > 0.82, P < 0.0001). All age-related changes in the six pre-determined ROIs were best fitted by mono-exponential functions. Changes in the splenium extended to a later age when compared with the genu of the corpus-callosum, while the centrum semi-ovale demonstrated the latest changes with age. High b-value DWI and DTI showed changes in white matter from infancy through adulthood. However, high b-value detects a signal that is likely to originate mainly from the intra-axonal water population, and thus may represent different aspects of development and different sensitivity to pathology.