Diffusion MRI and its Role in Neuropsychology

Abstract

Diffusion Magnetic Resonance Imaging (dMRI) is a popular method used by neuroscientists to uncover unique information about the structural connections within the brain. dMRI is a non-invasive imaging methodology in which image contrast is based on the diffusion of water molecules in tissue. While applicable to many tissues in the body, this review focuses exclusively on the use of dMRI to examine white matter in the brain. In this review, we begin with a definition of diffusion and how diffusion is measured with MRI. Next we introduce the diffusion tensor model, the predominant model used in dMRI. We then describe acquisition issues related to acquisition parameters and scanner hardware and software. Sources of artifacts are then discussed, followed by a brief review of analysis approaches. We provide an overview of the limitations of the traditional diffusion tensor model, and highlight several more sophisticated non-tensor models that better describe the complex architecture of the brain's white matter. We then touch on reliability and validity issues of diffusion measurements. Finally, we describe examples of ways in which dMRI has been applied to studies of brain disorders and how identified alterations relate to symptomatology and cognition.

Figure 1

Principles of diffusion magnetic resonance imaging (dMRI) from Le Bihan et al 2003. In the presence of a spatially varying magnetic field (induced through a magnetic field gradient, the amplitude and timing of which are characterized by a ‘b’ factor), moving molecules emit radiofrequency signals with slightly different phases. In a small volume (voxel) containing a large number of diffusing molecules, these phases become randomly distributed, directly reflecting the trajectory of individual molecules (that is, the diffusion process). This diffusion-related phase distribution of the signal results in an attenuation of the MRI signal. This attenuation (A) quantitatively depends on the gradient characteristics (embedded in the b factor) and the diffusion coefficient (D), according to A = e–bD. As diffusion effects are small, large gradient intensities must be used, which requires special MRI hardware (reprinted from (Le Bihan 2003) with permission).

Figure 2

Tensor matrix illustrated by Jellison et al 2004: Top left, Fiber tracts have an arbitrary orientation with respect to scanner geometry (x, y, z axes) and impose directional dependence (anisotropy) on diffusion measurements. Top right, The three-dimensional diffusivity is modeled as an ellipsoid whose orientation is characterized by three eigenvectors (1, 2, 3) and whose shape is characterized three eigenvalues (1, 2, 3). The eigenvectors represent the major, medium, and minor principal axes of the ellipsoid, and the eigenvalues represent the diffusivities in these three directions, respectively. Bottom, This ellipsoid model is fitted to a set of at least six noncollinear diffusion measurements by solving a set of matrix equations involving the diffusivities (apparent diffusion coefficient, ADC) and requiring a procedure known as matrix diagonalization. The major eigenvector (that eigenvector associated with the largest of the three eigenvalues) reflects the direction of maximum diffusivity, which, in turn, reflects the orientation of fiber tracts. Superscript T indicates the matrix transpose (reprinted from (Jellison et al. 2004) with permission).

Figure 3

Illustration of the effect of subject motion during the acquisition of a dMRI volume. Slices were acquired using an interleaved acquisition, in which the odd slices were acquired sequentially followed by the even slices. A clear stair step pattern is seen in the sagittal and coronal views due to motion between the first (odd slices) and second (even slices) parts of the acquisition. In the axial projection the slices appear unaffected by the motion.

Figure 4

Illustration of the effect of fast individual subject motion on dMRI data. Nine spatially consecutive slices are shown from one volume of an interleaved type dMRI acquisition. One slice (red box) has a near absence of signal while a second slice (yellow box) shows a more subtle signal reduction. This artifact was caused by fast subject motion over the several hundred milliseconds it took to acquire the red and yellow slices, which were acquired sequentially in time.

Figure 5

Example DTI data from a healthy subject. DTI scan parameters 2mm isotropic resolution, TR=8500ms, TE=90ms, 30 diffusion volumes with b=1000 and 6 volumes with b=0, two averages, scan time=11min. Top row shows b=0 s/mm² scan followed by 4 different slices with b=1000 s/mm² and different gradient orientations. Bottom row mean diffusivity, axial diffusivity, radial diffusivity, fractional anisotropy, tensor color map.

Figure 6

Radiological views of the 18 reconstructed white matter tracts overlaid on fractional anisotropy map in a control participant [coronal (A), sagittal (B) and axial (C)], and 3D anatomical view (D). CCG = cingulum-cingulate gyrus bundle; CST = corticospinal tract; FM = corpus callosum-forceps major; Fm = corpus callosumforceps minor; SLFP = superior longitudinal fasciculus-parietal endings; SLFT = superior longitudinal fasciculus-temporal endings; ILF = inferior longitudinal fasciculus; CAB = cingulum-angular bundle; UNC = uncinate fasciculus; ATR = anterior thalamic radiations (reprinted from (Lee et al. 2015) with permission).

Figure 7

Example of whole brain tractography using 3T diffusion MRI data. The streamline color code (DSI studio; http://dsi-studio.labsolver.org/) indicates local fiber orientation (red: left-right; green: anterior-posterior; blue: inferior-superior). The surface approximates the boundary white/grey matter and was obtained by thresholding the fractional anisotropy (FA) map. Top left: side view with cortical surface; Bottom left: side view with cortical surface removed revealing tractography streamlines; Top right: frontal view with cortical surface; Bottom right: coronal slice through corpus callosum. Data from the Human Connectome Project (HCP), WUMinn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Figure 8

Diffusion and parametric maps from a high resolution, MB-DTI, multi-shell data set. Scan parameters include MB=3, 1.5mm isotropic resolution, TR=3300ms, TE=68ms, 30 diffusion directions and 4 b=0 s/mm² volumes per shell, shells with b=1000, 1500, 2000, 2500 s/mm², image time=8:30 per scan. Two data sets were acquired with with opposite phase encode directions then merged together using the FSL tool eddy. Top row shows b=0, 1000, 1500, 2000, 2500 s/mm² volumes from the same gradient direction. Second row parametric maps of MD, RD, AD, FA, and FA color map computed using tensor model. Third row contains parametric maps fit using the NODDI toolkit (http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab), isotropic volume fraction (fiso), the intracellular volume fraction (ficvf), the extracellular volume fraction (fec), the concentration parameter of the Watson distribution (kappa), and the orientation dispersion index (ODI). The fourth row contains parametric maps from DKI fit to the data from the DKE toolkit (http://academicdepartments.musc.edu/cbi/dki/dke.html), mean kurtosis (kmean), radial kurtosis (krad), axial kurtosis (kax), kurtosis fractional anisotropy (kfa), and kurtisos mean (mkt).