Diffusion MRI of Complex Neural Architecture

Tuch, David S.; Reese, Timothy G.; Wiegell, Mette R.; Wedeen, Van J.

doi:10.1016/s0896-6273(03)00758-x

Cited by 691 publications

(705 citation statements)

References 47 publications

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“…The trajectories between the multiple ROIs should reflect the macroscopic architecture of the white matter. One of the well known limitations of DTI-based tractography is that it cannot properly handle crossing fibers within a pixel (Wiegell et al, 2000;Tuch et al, 2003). Tractography also contains errors due to noise and partial volume effects.…”

Section: Discussion Probabilistic Maps Of Tractographymentioning

confidence: 99%

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

et al. 2008

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Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T 2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

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Section: Discussion Probabilistic Maps Of Tractographymentioning

confidence: 99%

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

et al. 2008

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“…The best way to convey these statistical features in terms of tissue characteristics such as cell density, type, or shape is currently an area of active investigation (Alexander et al, 2002;Clark et al, 2002;Frank, 2002;Liu et al, 2004;Maier et al, 2004a,b;Ozarslan and Mareci, 2003;Tuch et al, 2002Tuch et al, , 2003Zhan et al, 2003). An optimal diffusion model retains the full complement of structural information present in the data, but contains a minimal number of adjustable parameters.…”

Section: Discussionmentioning

confidence: 99%

“…The most commonly adopted mathematical formalism used to express diffusion anisotropy in brain is the diffusion tensor imaging (DTI) model (Basser et al, 1994). Other expressions, such as a sum of diffusion tensors (Clark et al, 2002; Maier et al., 2004a,b), a spherical harmonic expansion of the diffusion profile (Alexander et al, 2002;Frank, 2002;Zhan et al, 2003), a function possessing higher moments than the DTI model function (e.g., kurtosis) (Liu et al, 2004;Ozarslan and Mareci, 2003), and methods for expressing diffusive displacement without reference to simplifying models (Tuch et al, 2002(Tuch et al, , 2003 have been proposed. We have found that water diffusion within fixed baboon brain may be modeled with high fidelity by selecting a single model from a series of expressions possessing varying degrees of radial symmetry on a voxel-by-voxel basis, provided that a constant term is added to the expression for the MR signal (see Appendix A).…”

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confidence: 99%

Diffusion MR imaging characteristics of the developing primate brain

et al. 2005

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Diffusion-based magnetic resonance imaging holds the potential to noninvasively demonstrate cellular-scale structural properties of brain. This method was applied to fixed baboon brains ranging from 90 to 185 days gestational age to characterize the changes in diffusion properties associated with brain development. Within each image voxel, a probability-theory-based approach was employed to choose, from a group of analytic equations, the one that best expressed water displacements. The resulting expressions contain eight or fewer adjustable parameters, indicating that relatively simple expressions are sufficient to obtain a complete description of the diffusion MRI signal in developing brain. The measured diffusion parameters changed systematically with gestational age, reflecting the rich underlying microstructural changes that take place during this developmental period. These changes closely parallel those of live, developing human brain. The information obtained from this primate model of cerebral microstructure is directly applicable to studies of human development. D 2004 Elsevier Inc. All rights reserved.Keywords: Magnetic resonance imaging; Diffusion anisotropy; Baboon brains; Brain development IntroductionMagnetic resonance imaging (MRI) is increasingly utilized in human and animal studies to provide information relating to cerebral injury and development in the newborn. Diffusion MRI, in particular, is being applied to assess brain development (Huppi et al., 1998;Neil et al., 2002;Partridge et al., 2004;Rutherford et al., 2004). Diffusion MRI provides information about the directional dependence of water displacements due to diffusion. This, in turn, reflects underlying tissue microstructure. In white matter, for example, water moving parallel to myelinated fibers can move within or between myelin layers, without crossing membranes. Water moving orthogonal to fibers, on the other hand, must either cross myelin layers or go around them. As a result, water displacements are smaller when measured orthogonal to myelinated fibers than when measured parallel to them. Thus, water diffusion in myelinated white matter is anisotropic. The regional changes in diffusion anisotropy within the developing white matter of the newborn brain have been well appreciated for some time (Huppi et al., 1998;Neil et al., 1998).Diffusion anisotropy has also been described within cerebral cortical gray matter in the developing human brain. Anisotropy is high early in development, with the preferred displacement direction oriented radially with respect to the cortical surface (Maas et al., 2004;Mori et al., 2001;Neil et al., 1998). This has been attributed to the orientation of radial glial fibers and the apical dendrites of pyramidal cells. As the brain matures, the magnitude of cortical diffusion anisotropy decreases (McKinstry et al., 2002;Mori et al., 2001), reflecting a loss in orientational coherence of cortical structures that restrict diffusion. This may be due to the elaboration of pyramidal cell basal dendrites, additio...

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“…These were related to the fact that DTI was unable to resolve multiple fiber bundle orientation inside an imaging voxel, and these crossing configurations appeared to be ubiquitous in the brain . While the theoretical foundations of diffusion MRI have come to be better understood (Wedeen et al, 2005), over the years, increasingly complex data acquisition schemes have been developed that are able to handle "the fiber-crossing problem" (Tournier et al, 2004;Tuch et al, 2003;Wedeen et al, 2000Wedeen et al, , 2005.…”

Section: Mapping the Connectome With Mrimentioning

confidence: 99%

“…Therefore most connectional studies up to now have been conducted with DTI schemes despite its recognized limitations. Somewhat in-between solutions, known as single-shell High Angular Resolution Diffusion Imaging (HARDI) techniques became quite popular since through some assumptions they were able to overcome the "fiber-crossing problem" without having to compromise to much scan time and without major hardware requirements (Jansons and Alexander, 2003;Seunarine and Alexander, 2009;Tournier et al, 2004;Tuch et al, 2002Tuch et al, , 2003. In our experience, there is a steady increase in performance from DTI to HARDI techniques and finally DSI when performed with the appropriate hardware, pulse sequence and post-processing (Gigandet, 2009;Wedeen et al, 2008), but this is not a broadly accepted fact.…”

Section: Mapping the Connectome With Mrimentioning

confidence: 99%

MR connectomics: Principles and challenges

Hagmann

Cammoun

Gigandet

et al. 2010

Journal of Neuroscience Methods

253

207

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a b s t r a c t MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

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Diffusion MRI of Complex Neural Architecture

Cited by 691 publications

References 47 publications

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

Diffusion MR imaging characteristics of the developing primate brain

MR connectomics: Principles and challenges

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