Normal ageing is accompanied by a progressive decline in cognitive function but the mechanisms for this are not fully understood. Nevertheless, the importance of white matter degeneration is ...supported by diffusion tensor imaging (DTI) studies, although several important questions remain about the pattern and nature of age-related white matter degeneration. Firstly, there is a lack of longitudinal data determining the rate of change in DTI parameters with age, and whether this can be detected over short time periods. Secondly, it is unclear whether observed changes are uniform across the brain or whether accelerated white matter degeneration is localised to particular brain regions, as would support the frontal-ageing hypothesis. This study uses DTI techniques to quantify structural integrity change to determine whether regional differences are apparent in the rate of degeneration during longitudinal follow-up in a sample of healthy middle aged and older adults aged between 50 and 90years. Longitudinal differences in fractional anisotropy, axial and radial diffusivity are investigated using 1D coronal slice profiles, and 2D column maps in standard space, as well as using 3D tract-based spatial statistics (TBSS) to investigate local age-related structural changes on a voxel-by-voxel basis at baseline and two-year follow-up. Results indicate that DTI can detect age-related change in white matter structure over a relatively short follow-up period and that longitudinal analyses reveal significant changes in white matter integrity throughout the brain with no evidence of accelerated decline in the frontal lobe regions during a 2year period. Common changes across different diffusion characteristics are discussed.
Purpose
To develop a robust renal arterial spin labeling (ASL) acquisition and processing strategy for mapping renal blood flow (RBF) in a pediatric cohort with severe kidney disease.
Methods
A ...single‐shot background‐suppressed 3D gradient and spin‐echo (GRASE) flow‐sensitive alternating inversion recovery (FAIR) ASL acquisition method was used to perform 2 studies. First, an evaluation of the feasibility of single‐shot 3D‐GRASE and retrospective noise reduction methods was performed in healthy volunteers. Second, a pediatric cohort with severe chronic kidney disease underwent single‐shot 3D‐GRASE FAIR ASL and RBF was quantified following several retrospective motion correction pipelines, including image registration and threshold‐free weighted averaging. The effect of motion correction on the fit errors of saturation recovery (SR) images (required for RBF quantification) and on the perfusion‐weighted image (PWI) temporal signal‐to‐noise ratio (tSNR) was evaluated, as well as the intra‐ and inter‐session repeatability of renal longitudinal relaxation time (T1) and RBF.
Results
The mean cortical and/or functional renal parenchyma RBF in healthy volunteers and CKD patients was 295 ± 97 and 95 ± 47 mL/100 g/min, respectively. Motion‐correction reduced image artefacts in both T1 and RBF maps, significantly reduced SR fit errors, significantly increased the PWI tSNR and improved the improved the repeatability of T1 and RBF in the pediatric patient cohort.
Conclusion
Single‐shot 3D‐GRASE ASL combined with retrospective motion correction enabled repeatable non‐invasive RBF mapping in the first pediatric cohort with severe kidney disease undergoing ASL scans.
A number of two-compartment models have been developed for the analysis of arterial spin labeling (ASL) data, from which both cerebral blood flow (CBF) and capillary permeability-surface product (PS) ...can be estimated. To derive values of PS, the volume fraction of the ASL signal arising from the intravascular space (vbw) must be known a priori. We examined the use of diffusion-weighted imaging (DWI) and subsequent analysis using the intravoxel incoherent motion model to determine vbw in the human brain. These data were then used in a two-compartment ASL model to estimate PS. Imaging was performed in 10 healthy adult subjects, and repeated in five subjects to test reproducibility. In gray matter (excluding large arteries), mean voxel-wise vbw was 2.3 ± 0.2 mL blood/100 g tissue (all subjects mean ± s.d.), and CBF and PS were 44 ± 5 and 108 ± 2 mL per 100 g per minute, respectively. After spatial smoothing using a 6-mm full width at half maximum Gaussian kernel, the coefficient of repeatability of CBF, vbw and PS were 8 mL per 100 g per minute, 0.4 mL blood/100 g tissue, and 13 mL per 100 g per minute, respectively. Our results show that the combined use of ASL and DWI can provide a new, noninvasive methodology for estimating vbw and PS directly, with reproducibility that is sufficient for clinical use.
Purpose
To demonstrate the feasibility of multidimensional diffusion MRI to probe and quantify microscopic fractional anisotropy (µFA) in human kidneys in vivo.
Methods
Linear tensor encoded (LTE) ...and spherical tensor encoded (STE) renal diffusion MRI scans were performed in 10 healthy volunteers. Respiratory triggering and image registration were used to minimize motion artefacts during the acquisition. Kidney cortex–medulla were semi‐automatically segmented based on fractional anisotropy (FA) values. A model‐free analysis of LTE and STE signal dependence on b‐value in the renal cortex and medulla was performed. Subsequently, µFA was estimated using a single‐shell approach. Finally, a comparison of conventional FA and µFA is shown.
Results
The hallmark effect of µFA (divergence of LTE and STE signal with increasing b‐value) was observed in all subjects. A statistically significant difference between LTE and STE signal was found in the cortex and medulla, starting from b = 750 s/mm2 and b = 500 s/mm2, respectively. This difference was maximal at the highest b‐value sampled (b = 1000 s/mm2) which suggests that relatively high b‐values are required for µFA mapping in the kidney compared to conventional FA. Cortical and medullary µFA were, respectively, 0.53 ± 0.09 and 0.65 ± 0.05, both respectively higher than conventional FA (0.19 ± 0.02 and 0.40 ± 0.02).
Conclusion
The feasibility of combining LTE and STE diffusion MRI to probe and quantify µFA in human kidneys is demonstrated for the first time. By doing so, we show that novel microstructure information—not accessible by conventional diffusion encoding—can be probed by multidimensional diffusion MRI. We also identify relevant technical limitations that warrant further development of the technique for body MRI.
Aim
To examine if congenital visual impairment is associated with differences in brain anatomy in children.
Method
Ten children (8–12y) with congenital disorders of the peripheral visual system with ...severe visual impairment (SVI; >0.8 logMAR) or mild‐to‐moderate visual impairment (MVI; 0.6–0.8 logMAR) were compared to 21 typically sighted comparison (TSC) children. Thalamus volume, grey matter density, white matter microstructure, and integrity of visual tracts were investigated in SVI, MVI, and TSC groups with anatomical and diffusion‐weighted magnetic resonance imaging.
Results
Compared to the TSC group, the SVI group had lower white matter integrity in tracts of the visual system (optic radiations: SVI 0.35±0.015, TSC 0.39±0.007 p=0.022; posterior corpus callosum: SVI 0.37±0.019; TSC 0.42±0.009 p=0.033) and lower left thalamus volume (SVI 4.37±0.087; TSC 4.99±0.339 p=0.015). Neuroanatomical differences were greater in the SVI group, while no consistent differences between the MVI and TSC group were observed.
Interpretation
Posterior tracts of the visual system are compromised in children with congenital visual impairment versus those who are typically sighted. The severity of visual input appears to have affected neuroanatomical development as significant reductions were only found in the SVI group.
What this paper adds
Severe visual impairment in mid‐childhood is associated with reduced integrity of visual pathways and reduced thalamus volume.
What this paper adds
Severe visual impairment in mid‐childhood is associated with reduced integrity of visual pathways and reduced thalamus volume.
This article is commented on by Bauer on page 16 of this issue.
Population receptive field (pRF) mapping is a widely used approach to measuring aggregate human visual receptive field properties by recording non-invasive signals using functional MRI. Despite ...growing interest, no study to date has systematically investigated the effects of different stimulus configurations on pRF estimates from human visual cortex. Here we compared the effects of three different stimulus configurations on a model-based approach to pRF estimation: size-invariant bars and eccentricity-scaled bars defined in Cartesian coordinates and traveling along the cardinal axes, and a novel simultaneous "wedge and ring" stimulus defined in polar coordinates, systematically covering polar and eccentricity axes. We found that the presence or absence of eccentricity scaling had a significant effect on goodness of fit and pRF size estimates. Further, variability in pRF size estimates was directly influenced by stimulus configuration, particularly for higher visual areas including V5/MT+. Finally, we compared eccentricity estimation between phase-encoded and model-based pRF approaches. We observed a tendency for more peripheral eccentricity estimates using phase-encoded methods, independent of stimulus size. We conclude that both eccentricity scaling and polar rather than Cartesian stimulus configuration are important considerations for optimal experimental design in pRF mapping. While all stimulus configurations produce adequate estimates, simultaneous wedge and ring stimulation produced higher fit reliability, with a significant advantage in reduced acquisition time.
Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the ...measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invariant spherical convolutional neural networks can improve microstructural parameter estimation. We trained a spherical convolutional neural network to predict the ground-truth parameter values from efficiently simulated noisy data and applied the trained network to imaging data acquired in a clinical setting to generate microstructural parameter maps. Our network performed better than the spherical mean technique and multi-layer perceptron, achieving higher prediction accuracy than the spherical mean technique with less rotational variance than the multi-layer perceptron. Although we focused on a constrained two-compartment model of neuronal tissue, the network and training pipeline are generalizable and can be used to estimate the parameters of any Gaussian compartment model. To highlight this, we also trained the network to predict the parameters of a three-compartment model that enables the estimation of apparent neural soma density using tensor-valued diffusion encoding.
In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and ...microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states.
The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.
•Recently developed diffusion MRI methods quantify white matter micro-structure.•Combination of tractography and micro-structural measures define better of the connectome.•CONNECT established the first in-vivo atlas of brain micro-structural features.
The technique of diffusion tensor tractography is gaining increasing prominence as a non-invasive method for studying the architecture of the white matter pathways in the human brain. Numerous ...studies have been published that attempt to identify or reconstruct particular pathways of interest. An atlas or map of all the pathways in the white matter would be particularly useful for providing detailed anatomical data that is not available in studies based on conventional MRI data. In this paper we present a method for constructing a white matter atlas to define structures from diffusion tensor tractography by making use of the locations of the anatomical terminations of individual streamlines that pass through white matter. We show how a map of unique seed regions can be used to generate tracts of interest. This approach provides anatomical information that can be rapidly applied to MRI datasets for the clear identification of white matter tracts. We show close correspondence of the tracts generated from the atlas with tracts isolated with classical dissection of post-mortem brain tissue.
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