Histology to MRI registration quality of <i>ex vivo</i> human brain blocks fixed with solutions used in anatomy laboratories
Bibliographic record
Abstract
Abstract Introduction Post-mortem brain tissue is obtained from brain banks that provide small tissue samples, while gross anatomy laboratories could become a source of complete brains for neuroscientists. These are preserved with solutions whose chemical composition differs from the classic neutral-buffered formalin (NBF) used in brain banks, such as a saturated-salt-solution (SSS) or an alcohol-formaldehyde solution (AFS) that preserve antigenicity of the main brain cell populations. Since histology remains the gold standard in neuroscientific research, while MRI is the most common imaging modality, MRI-histology registration quality needs to be assessed to ensure the suitability using brains fixed with innovative solutions for research procedures. Hence, our goal was to compare the registration quality of human brains fixed with NBF, SSS and AFS, as well as the histological characteristics that could affect the registration. Methods We used 12 human brain blocks of 3×3×3 cm 3 fixed in our anatomy laboratory using SSS (N=4), AFS (N=4), or NBF (N=4). The blocks were scanned using a 7Tesla Bruker animal MRI scanner with a T2-TurboRARE sequence at 0.13×0.13×0.5mm 3 . Blocks were then cut into 40μm thick sections (parallel to the 0.13×0.13 plane) using a vibratome. Sections were stained with histochemistry (HC) (Cresyl violet, Prussian blue, Luxol fast blue, H&E, and Bielschowsky) and immunohistochemistry (IHC) of the 4 main cell populations: neurons (NeuN), astrocytes (GFAP), microglia (Iba1) and myelin (PLP) either with or without an antigen retrieval (AR) protocol. Stained sections were imaged using a slide scanner microscope and segmented with masks using Display, and the sections were manually registered to the T2-TurboRare images using landmarks in Register (MincToolKit). Results More landmarks were needed to achieve proper alignment of the histology to MRI images for the SSS-fixed blocks, due to the lower GM-WM contrast in these brains. However, there was no significant difference in the staining intensity of the histology sections of blocks fixed with the three solutions, while SSS-fixed blocks showed a lower percentage of overlap between the good histology quality masks and the MRI masks. This resulted in a sufficient registration quality of all blocks, although more challenging when fixed with SSS. Conclusion We have developed histology, MRI and registration protocols that are of good quality in brain blocks fixed with solutions used in gross anatomy laboratories. These results are promising for neuroscientists interested in using full brains from anatomy laboratories, either using MRI, histology or registration of both modalities to study normal aging and neurodegenerative conditions.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".