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Insights Into the Connectivity of the Human Brain Using DTI Educational Exhibit NJR 2011; 1(1):78 – 91;Available online at www.nranepal.org Insights into the Connectivity of the Human Brain Using DTI S Kollias Institute of Neuroradiology, University Hospital of Zurich, Switzerland. Abstract Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. Key words: Diffusion Tensor Imaging, human brain, white matter anatomy Definitions diffusion coefficients for the cellular compartments. Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows The anisotropic diffusion of coherently in vivo and non-destructive visualization of oriented axonal fibers can be exploited for myeloarchitectonics in the neural tissue and anatomic mapping and quantitative provides quantitative estimates of WM characterization of white matter tracts. This integrity by measuring molecular diffusion. is done by depicting and measuring the It is based in the phenomenon of diffusion diffusion tensor within the imaging voxels of anisotropy in the nerve tissue, in that water the diffusion acquisition. Provided that 6 molecules diffuse faster along the neural diffusion-encoded images sets are acquired fiber direction and slower in the fiber- along noncollinear directions, the diffusion transverse direction (Fig. 1) The hindrance of tensor can be calculated. Diffusion tensor, is water diffusion in white matter is putative ____________________________________ due to the diffusion barrier represented by Correspondence to: Prof. Dr. med. Spyros the cell membrane and the myelin sheath Kollias, Chief of MRI, Institute of however, it is governed by several other, yet Neuroradiology, University Hospital of not completely elucidated, parameters related Zurich, Switzerland to the diffusion physics and biophysical E-mail: [email protected] properties of the tissue such as, the cell membrane permeability and the free NJR I VOL 1 I No. 1 I ISSUE 1 I JULY-DEC, 2011 78 Page Kollias et al. Insights into the Connectivity of the Brain using DTI Figure 1: Schematic representation of water Figure 2: graphic representation of the diffusion proton anisotropic diffusion along the long axis of ellipsoid. The long axis of the ellipsoid indicates myelinated white mater fibers. The displacement the preferred direction of diffusion. Its value varies of the protons in the 3D space is faster along the from 0 (isotropic diffusion) up to maximum 1, direction of the fibers. Estimation of the direction indicating perfectly linear (anisotropic) diffusion of faster diffusion indicates the along the primary eigenvector. a mathematical model of the 3D pattern of The fiber orientation information inherent in diffusion trsjectories (a 3X3 matrix of the primary eigenvector can be visualized on vectors) within the white matter tracts. The 2D images by assigning a color to each of three principal axes of the diffusion tensor the 3 mutually orthogonal axes, typically red are termed the eigenvectors. The largest for left-right, green for anteroposterior and eigenvector is termed the primary blue for superior-inferior. The orientation of eigenvector and indicates the direction and the primary eigenvector within each voxel magnitude of greatest water diffusion. It is gives the color in the specific voxel (Fig. 3). important for fiber tractography because it This way, white matter tissue can be further indicates the orientation of axonal fiber parcellated into specific white matter tracts bundles. The degree of directionality of according to their orientation in the 3D space intravoxel diffusivity is measured by the (Fig. 4). fractional anisotropy (FA) index. When the The orientation information can also be used value of the primary eigenvector is higher for 3D fiber tracking of specific white matter than the values of the second and third tracts (Fig 5). The objective of fiber tracking eigenvectors the FA will be high, indicating is to determine intervoxel connectivity by diffusion along a preferred direction. The using the diffusion tensor of each voxel to graphic representation of the three follow an axonal tract in 3D from voxel to eigenvectors corresponds to a prolate shape voxel through the neural tissue. Several fiber of diffusion ellipsoid with the long axis of tracking algorithms have been developed the ellipsoid indicating the preferred over the last years for anatomical direction of diffusion (Fig. 2). Its value reconstruction and quantitative varies from 0 (isotropic diffusion) up to characterization of white matter tracts. maximum 1, indicating perfectly linear Grossly, these algorithms can be divided into diffusion along the primary eigenvector. deterministic and probabilistic methods. Deterministic, “streamline” tractography, Visualization of white matter architecture infers the continuity of fiber bunbles from using DTI voxel-to-voxel. User-defined regions of interest, as well as constrains on the maximum turning angle of the fiber NJR I VOL 1 I No. 1 I ISSUE 1 I JULY-DEC, 2011 79 Page Kollias et al. Insights into the Connectivity of the Brain using DTI trajectory between voxels and on the minimum FA within a voxel for propagation of the streamline, are applied to restrict the fiber tracts to a specific pathway based on prior anatomical knowledge and thus to create realistic representations of the white matter pathways. Probabilistic methods, incorporate the expected uncertainty into the tracking algorithm and can be used to produce a connectivity metric for each voxel. They focus on the connectivity probabilities rather the actual WM pathways between Figure 4: 2D DTI overlaid on a coronal inversion voxels. Definition of a “probability density recovery (IR) anatomical MR image. Large white function” allows estimation of the matter tracts of the projectional (blue), uncertaintity of both intravoxel orientation commissural (red) and association (green) fiber systems are distinguished from each other on the and its propagation. These algorithms have DT image. the potential to delineate a greater portion of a white matter tract. Figure 3: 2D DTI color-coded according the orientation in the 3D space of the primary eigenvector within each voxel of the image (red for left-right, green for anterior-posterior and blue for Figure 5: 3D reconstruction of fiber systems using superior-inferior). The direction of the main the Fiber Assignment by Continuous Tracking eigenvector indicates the fiber orientation within (FACT) algorithm. Based on the localization and the specific voxel. the specific main orientation we can virtually reconstruct individual fiber systems of the WM in great detail. We indentify the mediolaterally oriented commisural corpus callosum (red) the Methods to improve determination of the vertically oriented projectional systems of the diffusion tensor corona radiata and internal caspule (blue) and the anteroposterior oriented long association fiber Much progress has also been achieved over systems of the superior occipito-frontal fasciculus and arcuate fasciculus (green). the last years in improving the acquisition schemes of DTI data collection for better characterization of the water molecule (DSI) although, this approach is time diffusion. These include: a) increasing the consuming and difficult to transfer in the angular resolution of the diffusion- clinical setting, c) performing measurements sensitizing gradients, b) performing a direct at many directions but one gradient strength measurement of water diffusion in all only, referred as Q-ball imaging, with an directions and at different gradient strengths, acquisition time more compatible with referred to as Diffusion Spectrum Imaging clinical requirements. Certainly, the quality NJR I VOL 1 I No. 1 I ISSUE 1 I JULY-DEC, 2011 80 Page Kollias et al. Insights into the Connectivity of the Brain using DTI of the DTI is greatly facilitated by the use of information between the cerebral higher field strength magnets (3-7 T), the use hemispheres. It was demonstrated that of specialized surface coils and the anterior and body callosal connections are development of parallel imaging acquisition essential to perform temporally independent schemes. bimanual finger movements, whereas the The clinical and scientific utility of DTI fiber posterior
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