Biology subjects

Fricke, B.

Publications and source records attributed to Fricke, B..

3 recordsLinked to original sources

bioRxiv2026

Diffusion tensor imaging of whole human brains during long-term formaldehyde fixation: Temporal evolution of diffusion parameters, post-mortem conditions, and dependence on tissue structure

Purpose: Post-mortem Diffusion Magnetic Resonance Imaging (dMRI) findings on fixation-related changes in diffusion tensor imaging (DTI) parameters remain inconsistent, partly due to lower diffusion weighting and sparse early-fixation sampling. This study investigated how post-mortem tissue condition, fixation progression, and diffusion weighting shape DTI parameters in whole human brains, including whether an ex-vivo-adjusted protocol also suits in-situ and early fixation measurements. Methods: Six neurologically healthy whole human brains (post-mortem interval (PMI) 12 - 24 h) were scanned across tissue conditions, including an in-vivo reference cohort. Five brains were scanned longitudinally through immersion fixation (0 - 150 days), yielding 175 datasets with dense early sampling. Diffusion measurements at b-values 1000 - 4000 s/mm2 assessed the influence of diffusion weighting on DTI parameters. Analyses covered multiple white- and gray-matter regions and within white matter stratification by fiber orientation dispersion ({kappa}). Results: The largest mean diffusivity (MD) shift occurred between in-vivo and in-situ, whereas fractional anisotropy (FA) changed most strongly during early fixation. During immersion fixation, MD showed reproducible monoexponential saturation, while FA was heterogeneous across brains. PMI, {kappa}, and regional anatomy explained substantial FA heterogeneity across specimens. Qualitatively, the fixationrelated changes matched across diffusion weightings, but precision across specimens was highest at b-value 4000 s/mm2. Conclusion: During fixation, MD is a robust temporal marker, whereas FA requires microstructureaware interpretation. Diffusion weighting modulates how clearly fixation effects can be resolved, but the same qualitative fixation dynamics remained detectable at in-vivo-like weighting with reduced precision.

neuroscience↗

bioRxiv2023

ACID: A Comprehensive Toolbox for Image Processing and Modeling of Brain, Spinal Cord, and Post-mortem Diffusion MRI Data

Diffusion MRI (dMRI) has become a crucial imaging technique in the field of neuroscience, with a growing number of clinical applications. Although most studies still focus on the brain, there is a growing interest in utilizing dMRI to investigate the healthy or injured spinal cord. The past decade has also seen the development of biophysical models that link MR-based diffusion measures to underlying microscopic tissue characteristics, which necessitates validation through ex vivo dMRI measurements. Building upon 13 years of research and development, we present an open-source, MATLAB-based academic software toolkit dubbed ACID: A Comprehensive Toolbox for Image Processing and Modeling of Brain, Spinal Cord, and Ex Vivo Diffusion MRI Data. ACID is an extension to the Statistical Parametric Mapping (SPM) software, designed to process and model dMRI data of the brain, spinal cord, and ex vivo specimens by incorporating state-of-the-art artifact correction tools, diffusion and kurtosis tensor imaging, and biophysical models that enable the estimation of microstructural properties in white matter. Additionally, the software includes an array of linear and non-linear fitting algorithms for accurate diffusion parameter estimation. By adhering to the Brain Imaging Data Structure (BIDS) data organization principles, ACID facilitates standardized analysis, ensures compatibility with other BIDS-compliant software, and aligns with the growing availability of large databases utilizing the BIDS format. Furthermore, being integrated into the popular SPM framework, ACID benefits from a wide range of segmentation, spatial processing, and statistical analysis tools as well as a large and growing number of SPM extensions. As such, this comprehensive toolbox covers the entire processing chain from raw DICOM data to group-level statistics, all within a single software package.

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↗