In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI ...signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor‐valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one‐dimensional Gamma fit, a generalized two‐term cumulant approach, and two‐dimensional and four‐dimensional Monte‐Carlo‐based inversion techniques, using a clinically feasible tensor‐valued acquisition scheme. In particular, we compare their performance at different signal‐to‐noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two‐dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite‐SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite‐SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two‐dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four‐dimensional Monte Carlo inversion presents no infinite‐SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.
We compare in silico the performances of four algorithms designed to invert tensor‐valued diffusion MRI data: a Gamma fitting (Gamma), a generalized cumulant approach (Cov), and two‐dimensional and four‐dimensional Monte‐Carlo‐based techniques (MC‐2D, MC‐4D). The statistical descriptors estimated from these methods present varying levels of accuracy and precision. Unlike Gamma and Cov, the MC techniques do not present biases at infinite signal‐to‐noise ratio. However, they are limited by their noise sensitivity and MC‐2D requires a powder‐averaged signal of sufficient rotational invariance.
Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels ...remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo‐times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub‐voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre‐specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation‐specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre‐tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.
Diffusion MRI techniques designed to resolve fibre crossings within a given white matter (WM) voxel typically assume that the voxel‐level microstructural features can be represented by a single signal response function; this precludes the investigation of microscopic differences between the sub‐voxel fibre populations. In this work, we build on a recently introduced 5D relaxation–diffusion correlation MRI framework and present an analysis protocol for deriving and visualising metrics informing on the relaxation rates and diffusivities of distinct fibres. Experiments on a healthy volunteer demonstrate that the presented approach can capture crossings between distinct WM tracts of the human brain and inform on their individual relaxation–diffusion properties.
Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the ...forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample's microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.
Diffusion tensor distribution (DTD) imaging builds on principles from diffusion, solid‐state and low‐field NMR spectroscopies, to quantify the contents of heterogeneous voxels as nonparametric ...distributions, with tensor “size”, “shape” and orientation having direct relations to corresponding microstructural properties of biological tissues. The approach requires the acquisition of multiple images as a function of the magnitude, shape and direction of the diffusion‐encoding gradients, leading to long acquisition times unless fast image read‐out techniques like EPI are employed. While in previous in vivo human brain studies performed at 3 T this proved a viable option, porting these measurements to very high magnetic fields and/or to heterogeneous organs induces B0‐ and B1‐inhomogeneity artifacts that challenge the limits of EPI. To overcome such challenges, we demonstrate here that high spatial resolution DTD of mouse brain can be carried out at 15.2 T with a surface‐cryoprobe, by relying on SPatiotemporal ENcoding (SPEN) imaging sequences. These new acquisition and data‐processing protocols are demonstrated with measurements on in vivo mouse brain, and validated with synthetic phantoms designed to mimic the diffusion properties of white matter, gray matter and cerebrospinal fluid. While still in need of full extensions to 3D mappings and of scanning additional animals to extract more general physiological conclusions, this work represents another step towards the model‐free, noninvasive in vivo characterization of tissue microstructure and heterogeneity in animal models, at ≈0.1 mm resolutions.
Diffusion tensor distribution (DTD) imaging allows to quantify the heterogeneity of tissues in terms of local “sizes”, “shapes” and orientations, which are in turn directly related to the microstructural properties of biological tissues. Here, a high‐definition DTD analysis of a mouse brain is presented, carried out at 15.2 T with a surface‐cryoprobe and relying on a SPatiotemporal ENcoding (SPEN) imaging sequence. These results enabled the model‐free, noninvasive characterization of tissue microstructure and heterogeneity in live animals, at ≈0.1 mm resolutions.
Anthrax is an infectious disease caused by Bacillus anthracis, a bioterrorism agent that develops resistance to clinically used antibiotics. Therefore, alternative mechanisms of action remain a ...challenge. Herein, we disclose deoxy glycosides responsible for specific carbohydrate-phospholipid interactions, causing phosphatidylethanolamine lamellar-to-inverted hexagonal phase transition and acting over B. anthracis and Bacillus cereus as potent and selective bactericides. Biological studies of the synthesized compound series differing in the anomeric atom, glycone configuration and deoxygenation pattern show that the latter is indeed a key modulator of efficacy and selectivity. Biomolecular simulations show no tendency to pore formation, whereas differential metabolomics and genomics rule out proteins as targets. Complete bacteria cell death in 10 min and cellular envelope disruption corroborate an effect over lipid polymorphism. Biophysical approaches show monolayer and bilayer reorganization with fast and high permeabilizing activity toward phosphatidylethanolamine membranes. Absence of bacterial resistance further supports this mechanism, triggering innovation on membrane-targeting antimicrobials.
With the current increase in meat and animal products consumption, there is a need to make production systems more sustainable. The use of microalgae in monogastric feeds, replacing widely used ...conventional feedstuffs such corn and soybean, can be a solution to overcome this problem. Several studies have shown promising results in the use of microalgae in feeding of both pigs and poultry. However, there are several important constraints associated to the production of microalgae. Such constraints are particularly limiting in the context of tropical regions. Research and scientific development on microalgae production systems are thus essential so that may be widely used in monogastric feeding. Herein, we conduct an overview of the major findings in the use of microalgae in the context of monogastric feeding and analyse the major constraints associated to its production and use, particularly in the specific context of tropical regions.
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that ...has the potential to provide a more detailed assessment of tissue microstructure—combined diffusion‐relaxometry imaging. Combined diffusion‐relaxometry acquisitions vary multiple MR contrast encodings—such as b‐value, gradient direction, inversion time, and echo time—in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters—such as diffusivity, T1, T2, and T2∗. This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single‐contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
Seasonal weight loss (SWL) is a major constraint to extensive animal production systems. The Australian sheep production is based on merino sheep, a European breed not tolerant to SWL. Tolerant ...alternative breeds such as the fat‐tailed Damara and the Dorper have been increasingly used in Australia and elsewhere, due to their robustness. The aim of this study was to understand the mineral profile of muscle and liver tissues of Australian Merino, Damara and Dorper, when subjected to SWL in order to understand SWL‐tolerance physiology. Twenty‐four lambs were divided randomly between growing (control) and nutritionally restricted groups for each breed. The trial lasted 42 days. Animals were weighed bi‐weekly and at the end of the trial, lambs were slaughtered. Liver and muscle samples were taken immediately after slaughter. Mineral assessment was carried out using inductively coupled plasma–optical emission spectrometry. Analysis of variance showed mineral concentrations were generally increased in the muscle of restricted animals, mainly because of fat tissue mobilization. An increase in Zn and Fe concentrations indicates an increase of enzymatic activity in the liver of restricted sheep as well as differential abundance of Fe‐containing proteins. High concentrations of Cu in the liver of Dorper indicate higher ability to accumulate this element, even under SWL.
Diffusion nuclear magnetic resonance (NMR) is a powerful technique for studying porous media, but yields ambiguous results when the sample comprises multiple regions with different pore sizes, ...shapes, and orientations. Inspired by solid-state NMR techniques for correlating isotropic and anisotropic chemical shifts, we propose a diffusion NMR method to resolve said ambiguity. Numerical data inversion relies on sparse representation of the data in a basis of radial and axial diffusivities. Experiments are performed on a composite sample with a cell suspension and a liquid crystal.
Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever ...the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation–diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation–diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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