Formalin Tissue Fixation Biases Myelin-Sensitive MRI
Bibliographic record
Abstract
Abstract Purpose Chemical fixatives, such as formalin, form cross-links between proteins and affect the relaxation times and diffusion properties of tissue. These fixation-induced changes likely also affect myelin density measurements produced by quantitative magnetization transfer (qMT) and myelin water imaging (MWI). In this work, we evaluate these myelin-sensitive MRI methods for fixation-induced biases. Methods We perform qMT, MWI, and D 2 O-exchanged zero echo-time (ZTE) imaging on unfixed human spinal cord tissue, and repeat these measurements after 1 day and 31 days of formalin fixation. Results The qMT bound pool fraction increased by 30.7±21.1% after 1 day of fixation and by 42.6±33.9% after 31 days of fixation. Myelin water fraction increased by 39.7±15.5% and 37.0±15.9% at these same time points, and mean T 2 of the myelin water pool nearly doubled. Reference-normalized D 2 O-exchanged ZTE signal intensity increased by 8.17±6.03% after 31 days of fixation, but did not change significantly after 1 day of fixation. After fixation, specimen cross-sectional area decreased by approximately 5%; after correction for shrinkage, changes in D 2 O-exchanged ZTE intensity were nearly eliminated. Conclusion F and MWF are significantly increased by formalin fixation, while D 2 O-exchanged ZTE intensity is minimally affected. Changes in qMT and MWI may be due, in part, to delamination and formation of vacuoles in the myelin sheath. D 2 O-exchanged signal intensity may be altered by fixation-induced changes in myelin lipid solid-state 1 H T 1 . We urge caution in the comparison of these measurements across subjects or specimens in different states, especially unfixed vs. fixed tissue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".