Diffusion MRI detects early brain microstructure abnormalities in 2-month-old 3×Tg-AD mice.
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ABSTRACT:
SUBMITTER: Falangola MF
PROVIDER: S-EPMC7683375 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDespite considerable research on exercise-induced neuroplasticity in the brain, a major ongoing challenge in translating findings from animal studies to humans is that clinical and preclinical settings employ very different techniques.ObjectiveHere we aim to bridge this divide by using diffusion tensor imaging MRI (DTI), an advanced imaging technique commonly applied in human studies, in a longitudinal exercise study with mice.MethodsWild-type mice were exercised using voluntary free-wheel running, and MRI scans were at baseline and after four weeks and nine weeks of running.ResultsBoth hippocampal volume and fractional anisotropy, a surrogate for microstructural directionality, significantly increased with exercise. In addition, exercise levels correlated with effect size. Histological analysis showed more PDGFRα+ oligodendrocyte precursor cells in the corpus callosum of running mice.ConclusionsThese results provide compelling in vivo support for the concept that similar adaptive changes occur in the brains of mice and humans in response to exercise.
| S-EPMC7685674 | biostudies-literature
Project description:PurposeTo develop an in vivo diffusion magnetic resonance imaging (dMRI) technique to study embryonic mouse brain structure and injury.Materials and methodsPregnant CD-1 mice were examined on embryonic day 17 on an 11.7T scanner. Spatially selective excitation pulses were used to achieve localized imaging of individual mouse brains, in combination with a 3D fast imaging sequence to acquire dMRI at 0.16-0.2 mm isotropic resolution. Subject motions were corrected by navigator echoes and image registration. Further acceleration was achieved by simultaneous imaging of two embryos in an interleaved fashion. We applied this technique to detect embryonic brain injury in a mouse model of intrauterine inflammation.ResultsWith the localized imaging technique, we achieved in utero high-resolution T2 -weighted and dMRI of the embryonic mouse brain for the first time. Early embryonic brain structures were delineated from diffusion tensor images, and major white matter tracts were reconstructed in 3D. Comparison with ex vivo data showed significant changes in the apparent diffusion coefficient (ADC), but mostly unchanged fractional anisotropy. In the inflammation-affected embryonic brains, ADC in the cortical regions was reduced at 6 hours after the injury, potentially caused by cellular edema.ConclusionThe feasibility of in utero dMRI of embryonic mouse brains was demonstrated. The technique is important for noninvasive monitoring of embryonic mouse brain microstructure and injury.
| S-EPMC4523472 | biostudies-literature
Project description:ObjectivePhenylketonuria (PKU) is an autosomal recessive disorder whereby deficiencies in phenylalanine metabolism cause progressive neurological dysfunction. Managing PKU is challenging, with disease monitoring focussed on short-term phenylalanine control rather than measures of neuronal damage. Conventional imaging lacks sensitivity, however diffusion kurtosis imaging (DKI), a new MRI method may reveal subclinical white matter structural changes in PKU.MethodsThis cohort study involved adults with PKU recruited during routine clinical care. MRI, neurocognitive assessment and historical phenylalanine (Phe) levels were collected. A hypothesis-generating case study comparing diet-compliant and non-compliant siblings confirmed that DKI metrics are sensitive to dietary adherence and prompted a candidate metric (Krad/KFA ratio). We then tested this metric in a Replication cohort (PKU = 20; controls = 43).ResultsBoth siblings scored outside the range of controls for all DKI-based metrics, with severe changes in the periventricular white matter and a gradient of severity toward the cortex. Krad/KFA provided clear separation by diagnosis in the Replication cohort (p < 0.001 in periventricular, deep and pericortical compartments). The ratio also correlated negatively with attention (r = -0.51 & -0.50, p < 0.05) and positively with 3-year mean Phe (r = 0.45 & 0.58, p < 0.01).ConclusionDKI reveals regionally-specific, progressive abnormalities of brain diffusion characteristics in PKU, even in the absence of conspicuous clinical signs or abnormalities on conventional MRI. A DKI-based marker derived from these scores (Krad/KFA ratio) was sensitive to cognitive impairment and PKU control over the medium term and may provide a meaningful subclinical biomarker of end-organ damage.
| S-EPMC7814191 | biostudies-literature
Project description:PURPOSE:To assess which microstructural models best explain the diffusion-weighted MRI signal in the human placenta. METHODS:The placentas of nine healthy pregnant subjects were scanned with a multishell, multidirectional diffusion protocol at 3T. A range of multicompartment biophysical models were fit to the data, and ranked using the Bayesian information criterion. RESULTS:Anisotropic extensions to the intravoxel incoherent motion model, which consider the effect of coherent orientation in both microvascular structure and tissue microstructure, consistently had the lowest Bayesian information criterion values. Model parameter maps and model selection results were consistent with the physiology of the placenta and surrounding tissue. CONCLUSION:Anisotropic intravoxel incoherent motion models explain the placental diffusion signal better than apparent diffusion coefficient, intravoxel incoherent motion, and diffusion tensor models, in information theoretic terms, when using this protocol. Future work will aim to determine if model-derived parameters are sensitive to placental pathologies associated with disorders, such as fetal growth restriction and early-onset pre-eclampsia. Magn Reson Med 80:756-766, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
| S-EPMC5947291 | biostudies-literature
Project description:The purpose of this explorative study was to investigate whether diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameter changes are reliable measures of white matter integrity changes in Alzheimer's disease (AD) patients using a whole brain voxel-based analysis (VBA). Therefore, age- and gender-matched patients with mild cognitive impairment (MCI) due to AD (n = 18), dementia due to AD (n = 19), and age-matched cognitively healthy controls (n = 14) were prospectively included. The magnetic resonance imaging protocol included routine structural brain imaging and DKI. Datasets were transformed to a population-specific atlas space. Groups were compared using VBA. Differences in diffusion and mean kurtosis measures between MCI and AD patients and controls were shown, and were mainly found in the splenium of the corpus callosum and the corona radiata. Hence, DTI and DKI parameter changes are suggestive of white matter changes in AD.
| S-EPMC4927852 | biostudies-literature
Project description:Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results.
| S-EPMC5518773 | biostudies-literature
Project description:BackgroundAlthough pathological studies usually indicate pure dopaminergic neuronal degeneration in patients with parkin (PRKN) mutations, there is no evidence to date regarding white matter (WM) pathology. A previous diffusion MRI study has revealed WM microstructural alterations caused by systemic oxidative stress in idiopathic Parkinson's disease (PD), and we found that PRKN patients have systemic oxidative stress in serum biomarker studies. Thus, we hypothesized that PRKN mutations might lead to WM abnormalities.ObjectiveTo investigate whether there are WM microstructural abnormalities in early-onset PD patients with PRKN mutations using diffusion tensor imaging (DTI).MethodsNine PRKN patients and 15 age- and sex-matched healthy controls were recruited. DTI measures were acquired on a 3T MR scanner using a b value of 1,000 s/mm2 along 32 isotropic diffusion gradients. The DTI measures were compared between groups using tract-based spatial statistics (TBSS) analysis. Correlation analysis was also performed between the DTI parameters and several serum oxidative stress markers obtained in a previously conducted metabolomic analysis.ResultsAlthough the WM volumes were not significantly different, the TBSS analysis revealed a corresponding decrease in fractional anisotropy and an increase in mean diffusivity and radial diffusivity in WM areas, such as the anterior and superior corona radiata and uncinate fasciculus, in PRKN patients compared with controls. Furthermore, 9-hydroxystearate, an oxidative stress marker, and disease duration were positively correlated with several parameters in PRKN patients.ConclusionThis pilot study suggests that WM microstructural impairments occur in PRKN patients and are associated with disease duration and oxidative stress.
| S-EPMC8461664 | biostudies-literature
Project description:Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.
| S-EPMC7336899 | biostudies-literature
Project description:Probing microstructure with diffusion magnetic resonance imaging (dMRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in the tissue. The vast majority of these biophysical models of diffusion in white matter assume that the measured dMRI signal is the sum of the signals emanating from each of the constituent compartments, each of which exhibits a distinct behaviour in the b-value and/or orientation domain. Many of these models further assume that the dMRI behaviour of the oriented compartments (e.g. the intra-axonal space) is identical between distinct fibre populations, at least at the level of a single voxel. This implicitly assumes that any potential biological differences between fibre populations are negligible, at least as far as is measurable using dMRI. Here, we validate this assumption by means of a voxel-wise, model-free signal decomposition that, under the assumption above and in the absence of noise, is shown to be rank-1. We evaluate the effect size of signal components beyond this rank-1 representation and use permutation testing to assess their significance. We conclude that in the healthy adult brain, the dMRI signal is adequately represented by a rank-1 model, implying that biologically more realistic, but mathematically more complex fascicle-specific microstructure models do not capture statistically significant or anatomically meaningful structure, even in extended high-b diffusion MRI scans.
| S-EPMC7014821 | biostudies-literature
Project description:Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data.
| S-EPMC5159854 | biostudies-literature