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Brain Struct Funct. 2019 May;224(4):1469-1488. doi: 10.1007/s00429-019-01844-6. Epub 2019 Feb 21.

Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI.

Author information

1
Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA. Honghsi.Lee@nyulangone.org.
2
Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA. Honghsi.Lee@nyulangone.org.
3
Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA.
4
Department of Cell Biology and Microscopy Core, New York University School of Medicine, 540 First Avenue, New York, NY, 10016, USA.
5
Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA.

Abstract

Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its¬†distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~‚ÄČ17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the "apparent" inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.

KEYWORDS:

3d axon segmentation; 3d electron microscopy; Axonal diameter distribution; Axonal diameter variation; Corpus callosum; Diffusion coarse-graining; Diffusion time-dependence; Fiber orientation distribution; g-Ratio

PMID:
30790073
PMCID:
PMC6510616
[Available on 2020-05-01]
DOI:
10.1007/s00429-019-01844-6

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