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Purpose: Microscopic fractional anisotropy ({\mu}FA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate {\mu}FA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for {\mu}FA estimation. This study aims to evaluate {\mu}FA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. Methods: SDE experiments were performed at 16.4 T on an ex vivo mouse brain ({\Delta}/{\delta} = 12/1.5 ms). The {\mu}FA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments ({\Delta}/{\tau}/{\delta} = 12/12/1.5 ms), which allow a model-free estimation of {\mu}FA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. Results: {\mu}FA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). Conclusion: Powder-averaged SMT and (2-component) SM are unable to accurately report {\mu}FA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.

Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD, and its contrasts relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4 T, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, this may entail significant implications towards a much more specific and accurate (...)

PURPOSE: Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures. METHODS: PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared. RESULTS: Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of 2 to 4x, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution. CONCLUSION: Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

Purpose: Diffusion Magnetic Resonance Imaging (dMRI) is confounded by its long acquisition duration, thereby thwarting the detection of rapid microstructural changes, especially when diffusivity variations are accompanied by rapid changes in T2. The purpose of the present study is to accelerate dMRI to a single scan acquisition, and to enable a more accurate estimation of diffusivity as function of time. Methods: A general methodology termed Incomplete Initial Nutation Diffusion Imaging (INDI) capturing two diffusion contrasts in a single shot, is presented. INDI creates a longitudinal magnetization reservoir that facilitates the successive acquisition of two images separated by only a few milliseconds. INDI's theory is presented, followed by proof-of-concept ex- and in-vivo experiments at 16.4 T and 9.4 T. Results: Mean diffusivities (MDs) extracted from INDI were comparable with Diffusion Tensor Imaging (DTI) and the two-shot IDE in the water phantom. As expected in the brain tissues, DTI provided lower MD than UF-IDE and IDE, but IDE and UF-IDE were in excellent agreement. Simulations are presented for identifying the regimes where INDI is most beneficial. Conclusions: INDI accelerates dMRI acquisition to single-shot mode, which can be of great importance for mapping dynamic microstructural properties in-vivo without T2 bias.

The auditory pathway is widely distributed throughout the brain, and is perhaps one of the most interesting networks in the context of neuroplasticity. Accurate mapping of neural activity in the entire pathway, preferably noninvasively, and with high resolution, could be instrumental for understanding such longitudinal processes. Functional magnetic resonance imaging (fMRI) has clear advantages for such characterizations, as it is noninvasive, provides relatively high spatial resolution and lends itself for repetitive studies, albeit relying on an indirect neurovascular coupling to deliver its information. Indeed, fMRI has been previously used to characterize the auditory pathway in humans and in rats. In the mouse, however, the auditory pathway has insofar only been mapped using manganese-enhanced MRI. Here, we describe a novel setup specifically designed for high-resolution mapping of the mouse auditory pathway using high-field fMRI. Robust and consistent Blood-Oxygenation-Level-Dependent (BOLD) responses were documented along nearly the entire auditory pathway, from the cochlear nucleus (CN), through the superior olivary complex (SOC), nuclei of the lateral lemniscus (LL), inferior colliculus (IC) and the medial geniculate body (MGB). By contrast, clear BOLD responses were not observed in auditory cortex (AC) in this study. Diverse BOLD latencies were mapped ROI- and pixel-wise using coherence analysis, evidencing different averaged BOLD time courses in different auditory centers. Some degree of tonotopy was identified in the IC, SOC, and MGB in the pooled dataset though it could not be assessed per subject due to a lack of statistical power. Given the importance of the mouse model in plasticity studies, animal models, and optogenetics, and fMRI's potential to map dynamic responses to specific cues, this first fMRI study of the mouse auditory pathway paves the way for future (...)

Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modelling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human (...)

Mapping tissue microstructure accurately and noninvasively is one of the frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is at the forefront of such efforts, as it is capable of reporting on microscopic structures orders of magnitude smaller than the voxel size by probing restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating Diffusion Encoding (DODE) in particular, are highly promising for their ability to report on microscopic fractional anisotropy ({\mu}FA), a measure of the pore anisotropy in its own eigenframe, irrespective of orientation distribution. However, the underlying correlates of {\mu}FA have insofar not been studied. Here, we extract {\mu}FA from DDE and DODE measurements at ultrahigh magnetic field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We further endeavor to correlate {\mu}FA with Myelin Water Fraction (MWF) derived from multiexponential T2 relaxometry, as well as with literature-based spatially varying axonal diameters. In addition, a simple new method is presented for extracting unbiased {\mu}FA from three measurements at different b-values. Our findings reveal strong anticorrelations between {\mu}FA (derived from DODE) and axon diameter in the distinct spinal cord tracts; a moderate correlation was also observed between {\mu}FA derived from DODE and MWF. These findings suggest that axonal membranes strongly modulate {\mu}FA, which - owing to its robustness towards orientation dispersion effects - reflects axon diameter much better than its typical FA counterpart. The {\mu}FA exhibited modulations when measured via oscillating or blocked gradients, suggesting selective probing of different parallel path lengths and providing insight into how those modulate {\mu}FA metrics. Our findings thus shed light into the underlying microstructural correlates of {\mu}FA and are (...)

Multiexponential T2 (MET2) Relaxometry and Magnetization Transfer (MT) are among the most promising MRI-derived techniques for white matter (WM) characterization. Both techniques are shown to have histologically correlated sensitivity to myelin, but these correlations are not fully understood. Furthermore, MET2 and MT reports on different features of WM, thus being specific to different (patho)physiological states. Two-dimensional studies, such as those commonly used in NMR, have been rarely performed in this context. Here, off-resonance irradiation effects on MET2 components were evaluated in fixed rat spinal cord white matter at 16.4T. These 2D MT-MET2 experiments reveal that MT affects both short and long T2 in a tract-specific fashion. The spatially distinct modulations enhanced contrast between microstructurally-distinct spinal cord tracts. Two hypotheses to explain these findings were proposed: either selective elimination of a short T2 component through pre-saturation is combined with intercompartmental water exchange effects that occur on the timescale of irradiation; or other macromolecular species that exist within the tissue - other than myelin - such as neurofilaments, may be involved in the apparent microstructural segregation of the spinal cord (SC) from MET2. Though further investigation is required to elucidate the underlying mechanism, this phenomenon adds a new dimension for WM characterization.

Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and their animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals of variable disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T2* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results unveil a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. The changes we found in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability. These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and the MS mechanisms., Comment: 40 pages, 6 figures, 4 tables