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

Mordhorst, L., Morozova, M., Papazoglou, S., Fricke, B., Oeschger, J. M., Tabarin, T., et al. (2022). Towards a representative reference for MRI-based human axon radius assessment using light microscopy. NeuroImage, 249: 118906. doi:10.1016/j.neuroimage.2022.118906.

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 Creators:
Mordhorst, Laurin1, Author
Morozova, Maria2, 3, Author              
Papazoglou, Sebastian1, Author
Fricke, Björn1, Author
Oeschger, Jan Malte1, Author
Tabarin, Thibault1, Author
Rusch, Henriette3, Author
Jaeger, Carsten2, Author              
Geyer, Stefan2, Author              
Weiskopf, Nikolaus2, 4, Author              
Morawski, Markus2, 3, Author              
Mohammadi, Siawoosh2, Author              
Affiliations:
1Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ou_persistent22              
2Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
3Paul Flechsig Institute of Brain Research, Medical Faculty, Leipzig University, Leipzig, Germany, ou_persistent22              
4Felix Bloch Institute for Solid State Physics, Leipzig University, Leipzig, Germany, ou_persistent22              

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Free keywords: Axon radii distribution; Cross microscopy; Deep learning; Histological MRI; Neuroanatomy
 Abstract: Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.

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Language(s): eng - English
 Dates: 2022-01-062021-06-012022-01-112022-01-132022-04-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2022.118906
Other: epub 2022
PMID: 35032659
 Degree: -

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Project name : -
Grant ID : 616905
Funding program : -
Funding organization : European Research Council
Project name : -
Grant ID : MO 2397/5-1; MO 2249/3-1; GE 2967/1-1; MO 2397/4-1
Funding program : -
Funding organization : German Research Foundation (DFG)
Project name : -
Grant ID : 01EW1711A and B
Funding program : -
Funding organization : Bundesministerium für Bildung und Forschung (BMBF)
Project name : -
Grant ID : 01fmthh2017
Funding program : -
Funding organization : Forschungszentrums Medizintechnik Hamburg

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 249 Sequence Number: 118906 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166