Axon diameter inferences in the human corpus callosum using oscillating gradient spin echo sequences

Herrera, Sheryl L.; Sheft, Maxina; Mercredi, Morgan; Buist, Richard; Matsuda, Kyle; Martin, Melanie

doi:10.1016/j.mri.2021.10.014

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Importantly, DWI-derived estimation of the axonal diameter profile received a lot of criticism 55 . Accordingly the values estimated by AxtiveAx were higher than standard values reported post-mortem in other species 56 . While postmortem shrinking can affect values, future investigation on our dataset will allow us to provide true postmortem estimation of the axonal diameter in the squirrel monkey.…”

Section: Technical Validationcontrasting

confidence: 69%

A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

et al. 2023

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Although very well adapted to brain study, Magnetic Resonance Imaging (MRI) remains limited by the facilities and capabilities required to acquire data, especially for non-human primates. Addressing the data gaps resulting from these limitations requires making data more accessible and open. In contempt of the regular use of Saimiri sciureus in neuroscience research, in vivo diffusion has yet to be openly available for this species. Here we built and made openly available a unique new resource consisting of a high-resolution, multishell diffusion-weighted dataset in the anesthetized Saimiri sciureus. The data were acquired on 11 individuals with an 11.7 T MRI scanner (isotropic resolution of 400 µm3). This paper presents an overview of our dataset and illustrates some of its possible use through example analyses. To assess the quality of our data, we analyzed long-range connections (whole-brain tractography), microstructure (Neurite Orientation Dispersion and Density Imaging), and axon diameter in the corpus callosum (ActiveAx). Constituting an essential new resource for primate evolution studies, all data are openly available.

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Section: Technical Validationcontrasting

confidence: 69%

A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

et al. 2023

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“…Our study has important implications for previous and future dMRI studies of white matter microstructure. Previous studies, such as those by Assaf et al (2008) , Alexander et al (2010) , Zhang et al (2011) , Dyrby et al (2013) , Xu et al (2014) , Daducci et al (2015) , Horowitz et al (2015) , Huang et al (2015) , Benjamini et al (2016) , Drobnjak et al (2016) , Sepehrband et al (2016b) , Romascano et al (2020) , Harkins et al (2021) , Herrera et al (2022) , estimated axon radius without considering any T 2 dependence, assuming the same T 2 for all axons and intra- and extra-axonal water compartments. This simplification may affect the estimation of the intra-axonal diffusivities from which the axon radii are derived.…”

Section: Discussionmentioning

confidence: 99%

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Barakovic,

Pizzolato,

Tax

et al. 2023

Front. Neurosci.

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Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

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“…One is the tractographic parcellation of U-fiber bundles (Guevara et al 2020), and the other is the microstructural imaging methods . The latter is usually based on DW-MRI signals to construct a biophysical model and estimate the model parameters, which in turn provides microstructural features of the tissue (Lebel and Deoni 2018), such as fractional anisotropy (FA), mean diffusivity (MD), extracellular free-water (FW) (Pasternak et al 2009), axon diameter (Herrera et al 2022), neurite density (Palombo et al 2020), orientation dispersion index (ODI) (Zhang et al 2012) and so on. For SWM regions, Wang et al (2022) assessed white matter abnormalities in SWM and DWM by estimating imaging markers such as FA, FW, and MD, and investigated their contribution to cerebral small vessel disease (CSVD) processing speed.…”

Section: Introductionmentioning

confidence: 99%

Superficial white matter microstructural imaging method based on time-space fractional-order diffusion

He,

Wang

2024

Phys. Med. Biol.

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Objective. Microstructure imaging based on diffusion magnetic resonance signal is an advanced imaging technique that enables in vivo mapping of the brain’s microstructure. Superficial white matter (SWM) plays an important role in brain development, maturation, and aging, while fewer microstructure imaging methods address the superficial white matter due to its complexity. Therefore, this study aims to develop a diffusion propagation model to investigate the microstructural characteristics of the SWM region. Approach. In this paper, we hypothesize that the effect of cell membrane permeability and the water exchange between soma and dendrites cannot be neglected for typical clinical diffusion times (20ms<t<80ms). We then use SpinDoctor to simulate the diffusion magnetic resonance signals of real neurons and propose a time-space fractional-order diffusion model for SWM microstructure imaging. We evaluate the validity regime of our model using numerical simulations and compare the model parameters with several state-of-the-art methods. Main results. By analyzing the simulation signals of real neuronal cells as well as diffusion magnetic resonance data from the brains of fourteen healthy human subjects, we find that the time-space fractional-order diffusion model can be used to capture the structural complexity of the tissue, indirectly through the association of time fractional exponents with restricted diffusion and space fractional exponents with perfusion and membrane permeability. Significance. The results show that the diffusion propagation model can provide new insights into the tissue architecture of the superficial white matter.

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Axon diameter inferences in the human corpus callosum using oscillating gradient spin echo sequences

Cited by 6 publications

References 40 publications

A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Superficial white matter microstructural imaging method based on time-space fractional-order diffusion

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