Biology subjects

Mordhorst, L.

Publications and source records attributed to Mordhorst, L..

2 recordsLinked to original sources

bioRxiv2022

Deciphering the fibre-orientation independent component of R2* (R2,iso*) in the human brain with a single multi-echo gradient-recalled-echo measurement under varying microstructural conditions

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain, e.g. the g-ratio characterising the relative myelination of axons. However, R2* depends on the orientation of the fibres relative to the main magnetic field degrading its reproducibility and that of any microstructural derivative measure. To decipher its orientation-independent part (R2,iso*), a second-order polynomial in time (M2) can be applied to single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientation. The linear-time dependent parameter, {beta}1, of M2 can be biophysically related to R2,iso* when neglecting the signal from the myelin water (MW) in the hollow cylinder fibre model (HCFM). Here, we examined the effectiveness of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We showed that the fitted {beta}1 effectively estimates R2,iso*when using meGRE with long maximum echo time (TEmax {approx} 54 ms) but its microscopic dependence on the g-ratio was not accurately captured. This error was reduced to less than 12% when accounting for the MW contribution in a newly introduced biophysical expression for {beta}1. We further used this new expression to estimate the MW fraction (0.14) and g-ratio (0.79) in a human optic chiasm. However, the proposed method failed to estimate R2,iso* for a typical in-vivo meGRE protocol (TEmax {approx} 18 ms). At this TEmax and around the magic angle, the HCFM-based simulations failed to explain the R2*-orientation-dependence. In conclusion, estimation of R2,iso* with M2 in vivo requires meGRE protocols with very long TEmax {approx} 54 ms.

neuroscience↗

bioRxiv2021

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

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 on 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 frequency-weighted 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 potential error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (normalized-root-mean-square-error of reff: 7.2 %; rarith: 21.5 %) and lower bias (normalized-mean-bias-error of reff: -1.7 %; rarith: 16 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.9 % and 3 % to reff. In conclusion, the proposed method accurately estimates reff at MRI voxel resolution across a human corpus callosum sample. Further investigations are required to assess generalization to brain areas with different axon radii ensembles.

neuroscience↗