The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three‐dimensional electron microscopy

NMR in Biomedicine, Journal Year: 2024, Volume and Issue: 37(4)

Published: Jan. 2, 2024

The increasing availability of high‐performance gradient systems in human MRI scanners has generated great interest diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon is attained at strong weightings , where the deviation from expected scaling white matter yields a finite transverse diffusivity, which then translated into an estimate. While axons are usually modeled perfectly straight, impermeable cylinders, local variations (caliber variation or beading) and direction (undulation) known influence estimates have been observed microscopy data axons. In this study, we performed Monte Carlo simulations reconstructed three‐dimensional electron temporal lobe specimen using simulated sequence parameters matched maximal strength next‐generation Connectome 2.0 scanner ( 500 mT/m). We show that estimation accurate for nonbeaded, nonundulating fibers; however, fibers with caliber undulations, heavily underestimated due variations, effect overshadows overestimation undulations. This unexpected underestimation may originate coarse‐grained axial diffusivity variations. Given increased beading undulations pathological tissues, traumatic brain injury ischemia, interpretation alterations pathology be significantly confounded.

Language: Английский

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The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence

Journal of Magnetic Resonance, Journal Year: 2024, Volume and Issue: 366, P. 107745 - 107745

Published: Aug. 6, 2024

Language: Английский

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Evaluation of diffusion time–dependent changes in radial diffusivity as a surrogate for axon diameter

Magnetic Resonance in Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Abstract Purpose To experimentally evaluate the change in radial diffusivity with diffusion time () as a simple estimate of axon diameter. Methods Ex vivo ferret spinal cords were imaged via MRI and scanning electron microscopy. Region‐of‐interest comparisons made between area‐weighted mean diameter, , derived from Additional quantitative myelin metrics. Results A strong linear correlation was found . Negative correlations water fraction well bound pool Conclusion The value is shown to be good size ex regardless variations content, indicated by MRI.

Language: Английский

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Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Nov. 26, 2024

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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CACTUS: a computational framework for generating realistic white matter microstructure substrates

Frontiers in Neuroinformatics, Journal Year: 2023, Volume and Issue: 17

Published: Aug. 1, 2023

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental understanding the link between micrometre-scale properties and DW-MRI signals measured at millimetre-scale, optimizing acquisition protocols to target of interest, exploring robustness accuracy estimation methods. However, accurate require substrates that reflect main microstructural features studied tissue. To address this challenge, we introduce novel computational workflow, CACTUS (Computational Axonal Configurator Tailored Ultradense Substrates), white matter substrates. Our approach allows constructing with higher packing density than existing methods, up 95% intra-axonal volume fraction, larger voxel sizes 500μm3 rich fibre complexity. generates bundles angular dispersion, bundle crossings, variations along fibres their inner outer radii g-ratio. We achieve introducing global cost function radial growth match predefined targeted characteristics mirror those reported histological studies. improves development complex substrates, paving way future applications imaging.

Language: Английский

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Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Aug. 22, 2024

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 6, 2024

Abstract Water exchange is increasingly recognized as an important biological process that can affect the study of tissue using diffusion MR. Methods to measure exchange, however, remain immature opposed those used characterize restriction, with no consensus on optimal pulse sequence(s) or signal model(s). In general, trend has been towards data-intensive fitting highly parameterized models. We take opposite approach and show a judicious sub-sample spectroscopy (DEXSY) data be robustly quantify well in data-efficient manner. This sampling produces ratio two points per mixing time: (i) one point equal weighting both encoding periods, which gives maximal contrast, (ii) same total just first period, for normalization. call this quotient Diffusion EXchange Ratio (DEXR). Furthermore, we it probe time-dependent by estimating velocity autocorrelation function (VACF) over intermediate long times (∼ 2 − 500 ms). provide comprehensive theoretical framework design DEXR experiments case static constant gradients. Data from Monte Carlo simulations acquired fixed viable ex vivo neonatal mouse spinal cord permanent magnet system are presented test validate approach. cord, report following apparent parameters 6 points: τ k = 17 ± 4 ms, f NG 0.71 0.01, R eff 1.10 0.01 μ m, 0.21 0.06 m/ms, correspond time, restricted non-Gaussian fraction, effective spherical radius, permeability, respectively. For VACF, long-time, power-law scaling ≈ t 2.4 , approximately consistent disordered domains 3-D. Overall, method shown efficient, capable providing valuable quantitative metrics minimal MR data.

Language: Английский

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μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Aug. 22, 2024

This work proposes μGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Oct. 30, 2024

This work proposes μGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology

Human Brain Mapping, Journal Year: 2023, Volume and Issue: 44(14), P. 4859 - 4874

Published: July 20, 2023

Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate fibers from combination magnetic resonance imaging (MRI) data electroencephalography (EEG) measures interhemispheric transfer time (IHTT). In original study, IHTT were computed EEG averaged across a group, leading bias estimates. Here, we seek individual IHTT, obtained acquired visual evoked potential experiment. Subject-specific IHTTs are data-driven framework with minimal priori constraints, based on maximal peak neural responses stimuli within periods statistically significant activity inverse solution space. The subject-specific estimates ranged 8 29 ms except one participant between-session variability was comparable between-subject variability. mean radius distribution, MRI data, 0 1.09 μm subjects. change g-ratio 0.62 0.81 μm-α . single-subject measurement yields that consistent histological values. However, improvement repeatability is required improve specificity

Language: Английский

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