Mesoscopic structure of neuronal tracts from time-dependent diffusion

Burcaw, Lauren M.; Fieremans, Els; Novikov, Dmitry S.

doi:10.1016/j.neuroimage.2015.03.061

Cited by 222 publications

(509 citation statements)

References 60 publications

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“…The approach, as used here, also neatly avoids the need for any analytical model of tortuous extra-cellular diffusion for which only approximations are available (Burcaw et al, 2015). Given that these compartment models tend to overestimate key microstructure parameters such as the axon diameter , including the effects of these intractable parameters not only gives us clinically useful parameters, but may also improve the estimation of other microstructure indices.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Nedjati-Gilani

Schneider

Hall

et al. 2014

Lecture Notes in Computer Science

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A B S T R A C TSome microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τ i of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τ i . We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τ i . We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R = {0.88, 0.95, 0.82, 0.99} 2 ) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R = {0.75, 0.60, 0.11, 0.99} 2 ). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57 ± 0.05 s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33 ± 0.12 s in the normal appearing white matter (NAWM) and 0.19 ± 0.11 s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52 ± 0.09 s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56 ± 0.05 s in the NAWM and 0.13 ± 0.09 s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Nedjati-Gilani

Schneider

Hall

et al. 2014

Lecture Notes in Computer Science

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“…Some OGSE studies have reported that, at frequencies below 400 Hz, the OG-measured extra-axonal diffusivity transverse to axons in white matter is linearly dependent on the frequency of the employed OGSE sequence [2], whereas state-of-the art multi-compartment models of white matter relying on PGSE or OGSE sequences usually assume a Gaussian diffusion in the extra-axonal space [3][4][5][6]. A theoretical explanation was given in Burcaw et al [7], where the observed frequency-dependence is interpreted as resulting from the extra-axonal 2D short-range disorder of axonal packings in the plane transverse to white matter fibers.…”

Section: Introductionmentioning

confidence: 99%

“…where t is the diffusion time, ω is the dual frequency and t c represents the time to diffuse across the correlation length l c of the packing geometry (l c closely follows the mean external radius r ext of the axon packing [7,8]). Similar to recent models accounting for extra-axonal time-dependence in the case of single diffusion encoding sequences [9], a multi-compartment model for cosine OGSE sequences was proposed in Ginsburger et al [10] which added a frequency-dependent term in the extra-axonal diffusion tensor perpendicular diffusivity based on Equation (2), showing a significant improvement of the model fit quality.…”

Section: Introductionmentioning

confidence: 99%

“…An empirical law A ∼ l 2 c relating A to the correlation length l c was given in Burcaw et al [7] and Fieremans et al [8] but is not sufficient to catch the complexity of the scaling coefficient A. The value of A is a measure of the strength of the structural disorder [7,11], thus related to the geometrical properties characterizing the spatial organization of white matter at various scales. A possible approach to decipher the complex relationship between A and white matter features is to perform Monte-Carlo simulations of the diffusion process in diffusing media with increasing level of structural disorder.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Realism of White Matter Numerical Phantoms: A Step toward a Better Understanding of the Influence of Structural Disorders in Diffusion MRI

et al. 2018

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White matter is composed of irregularly packed axons leading to a structural disorder in the extra-axonal space. Diffusion MRI experiments using oscillating gradient spin echo sequences have shown that the diffusivity transverse to axons in this extra-axonal space is dependent on the frequency of the employed sequence. In this study, we observe the same frequency-dependence using 3D simulations of the diffusion process in disordered media. We design a novel white matter numerical phantom generation algorithm which constructs biomimicking geometric configurations with few design parameters, and enables to control the level of disorder of the generated phantoms. The influence of various geometrical parameters present in white matter, such as global angular dispersion, tortuosity, presence of Ranvier nodes, beading, on the extra-cellular perpendicular diffusivity frequency dependence was investigated by simulating the diffusion process in numerical phantoms of increasing complexity and fitting the resulting simulated diffusion MR signal attenuation with an adequate analytical model designed for trapezoidal OGSE sequences.This work suggests that angular dispersion and especially beading have non-negligible effects on this extracellular diffusion metrics that may be measured using standard OGSE DW-MRI clinical protocols.

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“…Recent literature is increasingly emphasizing the need for such a representation, where accounting for the diffusion time dependence of the extra-axonal diffusion signal [1,2] has already resulted in a more accurate estimation of the axon density and diameter [3]. To measure the four-dimensional dMRI signal it is necessary to go beyond a multi-shell q-space acquisition -which only varies gradient strength and direction -and also vary the diffusion time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

Fick

Petiet

Santin

et al. 2017

Computational Diffusion MRI

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Abstract. Effective representation of the diffusion signal's dependence on diffusion time is a sought-after, yet still unsolved, challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this four-dimensional spacevarying over gradient strength, direction and diffusion time. In particular, we provide regularization tools imposing signal sparsity and signal smoothness to drastically reduce the number of measurements we need to probe the properties of this multi-spherical space. We illustrate a novel application of our approach, which is the estimation of time-dependent q-space indices, on both synthetic data generated using Monte-Carlo simulations and in vivo data acquired from a C57Bl6 wild-type mouse. In both cases, we find that our regularization approach stabilizes the signal fit and index estimation as we remove samples, which may bring multi-spherical diffusion MRI within the reach of clinical application.

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Mesoscopic structure of neuronal tracts from time-dependent diffusion

Cited by 222 publications

References 60 publications

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Improving the Realism of White Matter Numerical Phantoms: A Step toward a Better Understanding of the Influence of Structural Disorders in Diffusion MRI

Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

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