Comparison of T1- and T2-weighted MRI contrasts of <i>ex vivo ex situ</i> brains fixed with solutions used in gross anatomy laboratories
Bibliographic record
Abstract
Abstract Post-mortem magnetic resonance imaging (MRI) offers high resolution and histological correlation, so protocols have been developed by brain banks using hemispheres fixed by immersion in Neutral-buffered formalin (NBF), but they provide limited tissue samples. Conversely, anatomy laboratories could supply complete brains perfused either with a salt-saturated (SSS) or an alcohol-formaldehyde (AFS) solution. These fixation methods alter the brain’s molecular properties, potentially affecting MRI quality and structural characteristics. T1- and T2-weighted (T1w, T2w) contrasts change with NBF fixation, but the effects of SSS or AFS remain unknown. We compared T1w and T2w intensities of different regions of interest (ROIs), including subcortical white matter (WM), cortical and deep gray matter (GM), in brains fixed with NBF, SSS and AFS. We scanned 20 ex situ hemispheres (NBF-immersed=7; SSS-perfused=7; AFS-perfused=6) in a 3T MRI scanner using T1w (0.7mm 3 ) and T2w (0.64mm 3 ) sequences overnight. Mean intensities of 29 ROIs in T1w and T2w MRIs and GM-WM ratios were calculated and compared in brains fixed with the three solutions. We found that T1w images were more affected by the fixation process, inverting the contrast of in vivo T1w and reducing the GM-WM contrast in AFS-fixed brains. T2w images resembled in vivo scans and maintained a sharp contrast in brains fixed with the three solutions, although the GM-WM intensity ratios were lowered in SSS-fixed brains. In conclusion, brains fixed with SSS and AFS from anatomy laboratories could be used for MRI studies, especially with the T2w sequence that seems more appropriate for structural analyses in different ROIs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".