dMRI Techniques Research Articles

Diffusion-weighted magnetic resonance imaging and pediatric epilepsy

Diffusion-weighted magnetic resonance imaging (dMRI) is a unique tool, which allows non-invasive in-vivo assessment of the white matter integrity and characterization of the brain maturation process in normal and abnormal subjects. In this review, we briefly present dMRI techniques and methodologies for non-experts and we describe recent studies investigating the use of dMRI in pediatric epilepsy. The study of white matter connections can guide us in localizing epileptic foci. Moreover, it has been proven sensitive to white matter-invisible lesions, network reorganization, the effects of seizures on the brain network, and the development of lesions in children and adults. Taken together, the results reviewed here suggest that dMRI is a valuable tool for assessing childhood epilepsy. However, studying dMRI in pediatric population is a challenge due to the high variability between age groups. In the quest for a deeper understanding of childhood epilepsies and brain abnormalities, dMRI protocols should be tested to determine the most sensitive parameters.

Abstract T P91: Changes in Diffusion Measures Following Experimental Stroke and Rehabilitative Training

Diffusional kurtosis imaging (DKI) is an advanced diffusion MRI (dMRI) technique that can quantify several diffusion measures that are highly sensitive to microstructural changes in brain. Previously, we found enhanced sensitivity of DKI-derived diffusion metrics following acute stroke in the lesion core and peri-lesional area that correlated with infiltration of activated glia. Studies have demonstrated that skilled motor training after ischemic damage affecting the sensorimotor cortex (SMC) induces functional motor recovery and neural plasticity in the remaining motor cortex. In the current study, we investigated the relationship between dMRI measures and rehabilitation-induced forelimb functional recovery. Adult male rats (3-4 months old) were trained on a skilled reaching task and given an endothelin-1 (ET-1) induced unilateral ischemic stroke over the SMC. Animals then received 21 days of impaired forelimb rehabilitative training (RT) on a reaching task or no rehabilitative training (No-RT). All animals underwent MRI scans prior to injury, 5 days post-injury, and after 21 days of RT or No-RT procedures, using a 7T Bruker Biospec MRI scanner to acquire dMRI images. Diffusivity and diffusional kurtosis metrics were calculated with in-house software. Multi-slice regions-of-interest were drawn in the lesion core, peri-lesional remaining motor cortex and in the forelimb region of the SMC in the non-infarcted hemisphere. All animals had lesion-induced motor deficits following ET-1 strokes and RT improved reaching forelimb function compared to No-RT. Peri-lesional diffusional kurtosis measures remained significantly different between the lesion and non-lesion SMC at four weeks post-injury, at which time diffusivity differences were no longer detected. Fractional anisotropy of the corpus callosum, inclusive of areas with SMC interhemispheric connections was higher following RT versus No-RT. These data indicate that the kurtosis of peri-lesional motor cortex remains sensitive to chronic ischemic effects, unlike diffusivity measures, and that skilled motor RT likely alters white matter connectivity between the two hemispheres.

DTIPrep: quality control of diffusion-weighted images.

In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.

Mapping spatiotemporal diffusion inside the human brain using a numerical solution of the diffusion equation

Diffusion is an important mechanism for molecular transport in living biological tissues. Diffusion magnetic resonance imaging (dMRI) provides a unique probe to examine microscopic structures of the tissues in vivo, but current dMRI techniques usually ignore the spatiotemporal evolution process of the diffusive medium. In the present study, we demonstrate the feasibility to reveal the spatiotemporal diffusion process inside the human brain based on a numerical solution of the diffusion equation. Normal human subjects were scanned with a diffusion tensor imaging (DTI) technique on a 3-T MRI scanner, and the diffusion tensor in each voxel was calculated from the DTI data. The diffusion equation, a partial-derivative description of Fick's law for the diffusion process, was discretized into equivalent algebraic equations. A finite-difference method was employed to obtain the numerical solution of the diffusion equation with a Crank-Nicholson iteration scheme to enhance the numerical stability. By specifying boundary and initial conditions, the spatiotemporal evolution of the diffusion process inside the brain can be virtually reconstructed. Our results exhibit similar medium profiles and diffusion coefficients as those of light fluorescence dextrans measured in integrative optical imaging experiments. The proposed method highlights the feasibility to noninvasively estimate the macroscopic diffusive transport time for a molecule in a given region of the brain.

Extracting Brain Connectivity from Diffusion MRI [Life Sciences

This article gives a short introduction to dMRI tractography methods. Diffusion magnetic resonance imaging (dMRI) is an MRI modality that has gained tremendous popularity the past five years and is especially promising for imaging the white matter in brain. The dMRI technique has raised hopes in the neuroscience community for a better understanding of the fiber tract anatomy of the human brain. Various methods have been proposed to visualize the anatomy of fiber pathways and to derive connectivity between different parts of the brain in vivo. While there are strong indications that dMRI reveals information about the fiber pathways in the brain, it is important to stress that the explicit measurements are of water diffusion, and not of the axons themselves. This technique allows us to use fibers from multiple brains as input, and thereby obtain a simultaneous clustering and matching of the bundles in all brains.