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Abstract #0985

Predicting the arithmetic mean radius from the MRI-visible axon radius

Laurin Mordhorst1, Mohammad Ashtarayeh1, Maria Morozova2,3, Sebastian Papazoglou1, Björn Fricke1, Tobias Streubel1,2, Carsten Jäger2, Henriette Rusch3, Ludger Starke4, Thomas Gladytz4, Ehsan Tasbihi4, Thoralf Niendorf4,5, Nikolaus Weiskopf2,6, Markus Morawski2,3, and Siawoosh Mohammadi1,2
1Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 2Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 3Paul Flechsig Institute of Brain Research, University of Leipzig, Leipzig, Germany, 4Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, 5Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, 6Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig, Germany


The axon radius is a main determinant of the conduction velocity of action potentials. Robust, MRI-based axon radius estimation is sensitive to a tail-weighted estimate of the ensemble-average axon radius, i.e., the effective axon radius ($$$r_{\text{eff}}$$$). It is unclear how $$$r_{\text{eff}}$$$ translates into the arithmetic mean axon radius ($$$r_{\text{arith}}$$$), which may be more indicative of the conduction velocity than $$$r_{\text{eff}}$$$. We investigate the feasibility to predict $$$r_{\text{arith}}$$$ from $$$r_{\text{eff}}$$$ using linear regression on high-resolution, large-scale light-microscopy images of a human corpus callosum sample and validate this linear relationship with microscopy and diffusion-weighted MRI images of another human corpus callosum sample.

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