Gaussian Modeling of the Diffusion Signal

Jones, Derek K.

doi:10.1016/b978-0-12-396460-1.00005-6

Cited by 13 publications

(21 citation statements)

References 82 publications

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“…We finish by providing practical recommendations for a minimal NODDI-DTI acquisi- [53] are recommended to reduce partial volume effects. Accurate DT estimation requires measurement of at least 30 distinct diffusion directions [6]; we recommend at least this number for application of NODDI-DTI, although the lowest number of orientations tested was 60.…”

Section: Discussionmentioning

confidence: 99%

“…The ACID toolbox was used to compute FA, MD, and the eigenvalues of the DT from the low b-value shell of each dataset, and home-written SPM scripts were then used to generate ν and τ using Eqs. (6) and (3), respectively, from these DT parameters; we refer to these as the 'NODDI-DTI method' results. For subjects 1 and 2, the ACID toolbox was also used to simultaneously estimate the diffusion and kurtosis tensors [40], giving silver standard mean diffusivity estimates (MD DKI ) less biased by the effects of diffusional kurtosis [38] allowing evaluation of the validity of Eq.…”

Section: Parameter Estimation and Comparisonmentioning

confidence: 99%

“…Diffusion tensor imaging (DTI) [6] is, at present, the most commonly used method to observe WM changes in-vivo [4,5,7]. This is because DTI is simply implemented and time efficient while allowing robust extraction of complementary parameters (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…References [1,3]). Despite its microstructural sensitivity, the model underlying DTI (gaussian anisotropic diffusion [6]) is unspecific to biological changes. Numerous studies show MD and FA change in white matter (e.g.…”

Section: Introductionmentioning

confidence: 99%

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NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

Weiskopf

et al. 2016

Preprint

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NODDI-DTI is a simplification of the NODDI model that, when its underlying assumptions are met, allows extraction of biophysical parameters from standard single-shell DTI data, permitting biophysical analysis of DTI datasets.In contrast to the NODDI signal model, the NODDI-DTI signal model contains no CSF compartment, restricting its application to voxels with very low CSF partial-volume contamination. This simplification allowed derivation of simple analytical relations between parameters representing axon density, ν, and dispersion, τ , and DTI invariants (MD and FA) through moment expansion of the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. It should be emphasised that the NODDI-DTI model inherits the strong assumptions of the NODDI model, and so is nominally restricted to analysis of healthy brains.NODDI-DTI parameter estimates were computed by applying the proposed analytical relations to DTI parameters extracted from the first shell of data, and compared to parameters extracted by fitting the NODDI-DTI model to (i) both shells (recommended) and (ii) the first shell (as for DTI) of data in the white matter of three different in vivo diffusion datasets. NODDI-DTI parameters estimated from DTI and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model it was found that diffusional kurtosis strongly biased DTI-based MD values, and so a novel heuristic correction requiring only DTI data was derived and used to account for this bias.Our results demonstrate that NODDI-DTI is a promising model and technique to interpret restricted datasets acquired for DTI analysis with greater biophysical specificity, though its limitations must be borne in mind.

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Section: Discussionmentioning

confidence: 99%

Section: Parameter Estimation and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

Weiskopf

et al. 2016

Preprint

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“…The principal diffusivities (eigenvalues) of the diffusion tensor correspond to the diffusion along the principal directions (eigenvectors) parallel and perpendicular to the tissue fibers, the tensor trace provides the mean diffusivity (MD), and the variance of the three eigenvalues about their mean defines the fractional anisotropy (FA). 69,70 Based on preliminary ex vivo studies, 71 it was suggested that DTI-derived parameters, MD and FA, have potential in specifying the porous architecture of trabecular bone microstructure in such a way that highly isotropic diffusion (low FA) could be observed in fat, whereas spongy bone regions showed increased variability in size and orientation of trabecular bone. 68 However, it was observed that by increasing the diffusion time, the contrast of MD and FA between isotropic and anisotropic tissue components decreased, which was attributed to the presence of internal gradients induced by magnetic susceptibility contrast between fat and trabecular bone.…”

Section: Diffusion Tensor Imaging (Dti)mentioning

confidence: 99%

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis

Kazerooni

Pozo

McCloskey

et al. 2019

Magnetic Resonance Imaging

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Diffusion MRI (dMRI) is a growing imaging technique with the potential to provide biomarkers of tissue variation, such as cellular density, tissue anisotropy, and microvascular perfusion. However, the role of dMRI in characterizing different aspects of bone quality, especially in aging and osteoporosis, has not yet been fully established, particularly in clinical applications. The reason lies in the complications accompanied with implementation of dMRI in assessment of human bone structure, in terms of acquisition and quantification. Bone is a composite tissue comprising different elements, each contributing to the overall quality and functional competence of bone. As diffusion is a critical biophysical process in biological tissues, early changes of tissue microstructure and function can affect diffusive properties of the tissue. While there are multiple MRI methods to detect variations of individual properties of bone quality due to aging and osteoporosis, dMRI has potential to serve as a superior method for characterizing different aspects of bone quality within the same framework but with higher sensitivity to early alterations. This is mainly because several properties of the tissue including directionality and anisotropy of trabecular bone and cell density can be collected using only dMRI. In this review article, we first describe components of human bone that can be potentially detected by their diffusivity properties and contribute to variations in bone quality during aging and osteoporosis. Then we discuss considerations and challenges of dMRI in bone imaging, current status, and suggestions for development of dMRI in research studies and clinics to segregate different contributing components of bone quality in an integrated acquisition. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:975–992.

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Altered frontal aslant tracts as a heritable neural basis of social communication deficits in autism spectrum disorder: A sibling study using tract‐based automatic analysis

Chen

Hsu

et al. 2018

Autism Research

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Investigating social behaviors and brain structural alterations in unaffected siblings of individuals with autism spectrum disorder (ASD) may help identify intermediate phenotypes of social communication deficits in ASD. This study hypothesized that such intermediate phenotypes could be identified in white matter tracts of the social communication model that exhibited reduced tract integrity and associations with social communication deficits. Boys with ASD (N = 30), unaffected male siblings (N = 27), and typically developing (TD) boys (N = 30) underwent clinical evaluation and MRI scanning. Group differences in generalized fractional anisotropy (GFA) values, a white matter integrity index derived from diffusion MRI data, and the relationships of GFA with the Social Responsiveness Scale (SRS) scores and the Child Behavior Checklist (CBCL/4–18) scores were investigated. Significant differences were found in the GFA values of the frontal aslant tract (FAT) among the three groups, with the decreasing order of GFA from TD to siblings to ASD. The GFA values of the FAT were associated with the social communication scores (on the SRS) in the sibling group, and those of the superior longitudinal fasciculus III were associated with the social problems scores (on the CBCL/4–18) in the boys with ASD. Due to the altered tract integrity and association with social communication deficits in the unaffected siblings of individuals with ASD, the FAT might be a heritable neural basis for social communication deficits of ASD. Autism Res 2019, 12: 225–238 © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Autism spectrum disorder (ASD) is a group of highly heritable disorders with social communication deficits as one of the core symptoms. This study aimed to identify a neural trait of social communication deficits in individuals with ASD. We investigated brain structural alterations and their associations with social communication scores in unaffected siblings of individuals with ASD. The siblings' frontal aslant tract was found to be impaired, and this tract showed a significant association with the social communication scores. Our findings support that the frontal aslant tract might be a potential neural trait of social communication deficits in ASD.

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Gaussian Modeling of the Diffusion Signal

Cited by 13 publications

References 82 publications

NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis

Altered frontal aslant tracts as a heritable neural basis of social communication deficits in autism spectrum disorder: A sibling study using tract‐based automatic analysis

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