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Recent MRI studies at high field have observed that, in certain white matter fiber bundles, the signal in T2⁎-weighted MRI (i.e. MRI sensitized to apparent transverse relaxivity) is dependent on fiber orientation θ relative to B0. In this study, the characteristics of this dependency are quantitatively investigated at 7T using ex-vivo brain specimens, which allowed a large range of rotation angles to be measured. The data confirm the previously suggested variation of R2⁎ (=1/T2*) with θ and also indicate that this dependency takes the shape of a combination of sin2θ and sin4θ functions, with modulation amplitudes (=ΔR2⁎) reaching 6.44±0.15Hz (or ΔT2⁎=2.91±0.33ms) in the major fiber bundles of the corpus callosum. This particular dependency can be explained by a model of local, sub-voxel scale magnetic field changes resulting from magnetic susceptibility sources that are anisotropic. As an illustration of a potential use of the orientation dependence of R2⁎, the feasibility of generating fiber orientation maps from R2⁎ data is investigated. [Display omitted] ► Demonstrate orientation dependent T2* in white matter of fixed human brain. ► Present a susceptibility anisotropy model that closely matches to the T2* measurement. ► Present a novel fiber orientation map based on T2* and compare it with DTI.

Objective: Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly. In early stages of PD, patients typically display normal brain magnet resonance imaging (MRI) in routine screening. Advanced imaging approaches are necessary to discriminate early PD patients from healthy controls. In this study, microstructural changes in relevant brain regions of early PD patients were investigated by using quantitative MRI methods. Methods: Cerebral MRI at 3T was performed on 20 PD patients in early stages and 20 age and sex matched healthy controls. Brain relative proton density, T1, T2, and T2′ relaxation times were measured in 14 regions of interest (ROIs) in each hemisphere and compared between patients and controls to estimate PD related alterations. Results: In comparison to matched healthy controls, the PD patients revealed decreased relative proton density in contralateral prefrontal subcortical area, upper and lower pons, in ipsilateral globus pallidus, and bilaterally in splenium corporis callosi, caudate nucleus, putamen, thalamus, and mesencephalon. The T1 relaxation time was increased in contralateral prefrontal subcortical area and centrum semiovale, putamen, nucleus caudatus and mesencephalon, whereas T2 relaxation time was elevated in upper pons bilaterally and in centrum semiovale ipsilaterally. T2′ relaxation time did not show significant changes. Conclusion: Early Parkinson’s disease is associated with a distinct profile of brain microstructural changes which may relate to clinical symptoms. The quantitative MR method used in this study may be useful in early diagnosis of Parkinson’s disease. Limitations of this study include a small sample size and manual selection of the ROIs. Atlas-based or statistical mapping methods would be an alternative for an objective evaluation. More studies are necessary to validate the measurement methods for clinical use in diagnostics of early Parkinson’s disease.

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.

Post mortem studies suggest protracted myelination of subcortical white matter into the middle age followed by gradual decline in the late adulthood. To date, however, establishing the proposed inverted-U pattern of age-myelin association proved difficult, as the most common method of investigating white matter, diffusion tensor imaging (DTI), usually reveals only linear associations between DTI indices and age among healthy adults. Here we use a novel method of estimating Myelin Water Fraction (MWF) based on modeling the short spin-spin (T2) relaxation component from multi-echo T2 relaxation imaging data and assess subcortical myelin content within six white matter tracts in a sample of healthy adults (N=61, age 18–84 years). Myelin content evidenced a quadratic relationship with age, in accord with the pattern observed postmortem studies. In contrast, DTI-derived indices that are frequently cited as proxies for myelination, fractional anisotropy (FA) and radial diffusivity (RD), exhibited linear or null relationships with age. Furthermore, the magnitude of age differences in MWF varied across the white matter tracts. Myelin content estimated by MWF was unrelated to FA and correlated with RD only in the splenium. These findings are consistent with the notion that myelination continues throughout the young adulthood into the middle age. The results demonstrate that single-tensor DTI cannot serve as a source of specific proxies for myelination of white matter tracts.

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging. •Conventional NODDI-derived compartment fractions are T2-weighted and TE-dependent.•MTE-NODDI disentangles non-T2-weighted signal fractions from T2 relaxation.•Robust intra-neurite T2 estimation in WM even with CSF contamination.•Non-T2-weighted fractions may improve the interpretability of neurodevelopmental studies.

Multi-echo T2 magnetic resonance images contain information about the distribution of T2 relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T2 spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X2) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X2, and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T2 spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T2 distributions and MWF maps. [Display omitted]

MRI at high field can be sensitized to the magnetic properties of tissues, which introduces a signal dependence on the orientation of white matter (WM) fiber bundles relative to the magnetic field. In addition, study of the NMR relaxation properties of this signal has indicated contributions from compartmentalized water environments inside and outside the myelin sheath that may be separable. Here we further investigated the effects of water compartmentalization on the MRI signal with the goal of extracting compartment-specific information. By comparing MRI measurements of human and marmoset brain at 7T with magnetic field modeling, we show that: (1) water between the myelin lipid bilayers, in the axonal, and in the interstitial space each experience characteristic magnetic field effects that depend on fiber orientation (2) these field effects result in characteristic relaxation properties and frequency shifts for these compartments; and (3) compartmental contributions may be separated by multi-component fitting of the MRI signal relaxation (i.e. decay) curve. We further show the potential application of these findings to the direct mapping of myelin content and assessment of WM fiber integrity with high field MRI. •Susceptibility contrast in cerebral white matter shows three T2⁎ relaxation components.•Field modeling suggests these components originate from distinct water environments.•Myelin, axonal, and interstitial water each experience characteristic magnetic fields.•Their contributions may be separated by multi-component fitting of the decay curve.

Background Magnetic resonance imaging (MRI) T2 and T1ρ relaxation are increasingly being proposed as imaging biomarkers potentially capable of detecting biochemical changes in articular cartilage before structural changes are evident. We aimed to: 1) summarize MRI methods of published studies investigating T2 and T1ρ relaxation time in participants at risk for but without radiographic knee OA; and 2) compare T2 and T1ρ relaxation between participants at-risk for knee OA and healthy controls. Methods We conducted a systematic review of studies reporting T2 and T1ρ relaxation data that included both participants at risk for knee OA and healthy controls. Participant characteristics, MRI methodology, and T1ρ and T2 relaxation data were extracted. Standardized mean differences (SMDs) were calculated within each study. Pooled effect sizes were then calculated for six commonly segmented knee compartments. Results 55 articles met eligibility criteria. There was considerable variability between scanners, coils, software, scanning protocols, pulse sequences, and post-processing. Moderate risk of bias due to lack of blinding was common. Pooled effect sizes indicated participants at risk for knee OA had lengthened T2 relaxation time in all compartments (SMDs from 0.33 to 0.74; p  < 0.01) and lengthened T1ρ relaxation time in the femoral compartments (SMD from 0.35 to 0.40; p  < 0.001). Conclusions T2 and T1ρ relaxation distinguish participants at risk for knee OA from healthy controls. Greater standardization of MRI methods is both warranted and required for progress towards biomarker validation.

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T 2 and T 1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T 1 -T 2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm 2 ) and multiple inversion and echo times. A second reduced diffusion-T 2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.