Comparisons of MR and EM inferred tissue microstructure properties using a human autopsy corpus callosum sample
Bibliographic record
Abstract
Degeneration of white matter (WM) microstructure in the central nervous system is characteristic of many neurodegenerative conditions. Previous research indicates that axonal degeneration visible in ex vivo electron microscopy (EM) photomicrographs precede the onset of clinical symptoms. Measuring WM microstructural features, such as axon diameter and packing fraction, currently require these highly invasive methods of analysis and it is therefore of great importance to develop methods for in vivo measurements. Diffusion weighted Magnetic Resonance Imaging (MRI) is a non-invasive method which can be used in conjunction with temporal diffusion spectroscopy (TDS) and an oscillating gradient spin echo (OGSE) pulse sequence to probe micron-scale structures within neural tissue. The current experiment aims to compare axon diameter measurements, mean effective axon diameter (AxD¯), and packing fractions calculated from EM histopathological analysis and inferred values from MR images. Mathematical models of axon diameters used for analysis include the ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD) model and the AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) model using ROI (Region of Interest) based analysis (RBA) and voxel-based analysis (VBA), respectively. Overall, it was observed that MRI inferred WM microstructural parameters overestimate those calculated from EM. This may be attributable to tissue shrinkage during EM dehydration, the sensitivity of MR pulse sequences to larger diameter axons, and/or inaccurate model assumptions. The results of the current study provide a means to characterize the precision and accuracy of RBA-ACD and VBA-AAD OGSE-TDS and highlight the need for further research investigating the relationship between ex vivo MRI and EM, with the goal of reaching in vivo MRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".