Towards a representative reference for MRI-based human axon radius assessment using light microscopy

Mordhorst, Laurin; Morozova, Maria; Papazoglou, Sebastian; Fricke, Björn; Oeschger, Jan Malte; Tabarin, Thibault; Rusch, Henriette; Jäger, Carsten; Geyer, Stefan; Weiskopf, Nikolaus; Morawski, Markus; Mohammadi, Siawoosh

doi:10.1016/j.neuroimage.2022.118906

Cited by 2 publications

(1 citation statement)

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“…For these reasons, the presented histological results should not be considered the definitive “ground truth.” Future studies should aim to identify optimal histological procedures, such as those suggested by Sepehrband et al (2016b) , and also explore the use of neural network approaches for automatic measurement of tens of thousands of axons per ROI to reduce sampling biases ( Mordhorst et al, 2022 ). It is also worth noting that because the proposed calibration approach uses histological data as a reference, the predicted radii are relative to the specific histological sample employed.…”

Section: Appendix: Effective Axon Radiusmentioning

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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Section: Appendix: Effective Axon Radiusmentioning

confidence: 99%